Evaluation of Electroencephalography Source Localization Algorithms with Multiple Cortical Sources

PubMed Central

Bradley, Allison; Yao, Jun; Dewald, Jules; Richter, Claus-Peter

2016-01-01

Background Source localization algorithms often show multiple active cortical areas as the source of electroencephalography (EEG). Yet, there is little data quantifying the accuracy of these results. In this paper, the performance of current source density source localization algorithms for the detection of multiple cortical sources of EEG data has been characterized. Methods EEG data were generated by simulating multiple cortical sources (2–4) with the same strength or two sources with relative strength ratios of 1:1 to 4:1, and adding noise. These data were used to reconstruct the cortical sources using current source density (CSD) algorithms: sLORETA, MNLS, and LORETA using a p-norm with p equal to 1, 1.5 and 2. Precision (percentage of the reconstructed activity corresponding to simulated activity) and Recall (percentage of the simulated sources reconstructed) of each of the CSD algorithms were calculated. Results While sLORETA has the best performance when only one source is present, when two or more sources are present LORETA with p equal to 1.5 performs better. When the relative strength of one of the sources is decreased, all algorithms have more difficulty reconstructing that source. However, LORETA 1.5 continues to outperform other algorithms. If only the strongest source is of interest sLORETA is recommended, while LORETA with p equal to 1.5 is recommended if two or more of the cortical sources are of interest. These results provide guidance for choosing a CSD algorithm to locate multiple cortical sources of EEG and for interpreting the results of these algorithms. PMID:26809000

Simultaneous EEG and MEG source reconstruction in sparse electromagnetic source imaging.

PubMed

Ding, Lei; Yuan, Han

2013-04-01

Electroencephalography (EEG) and magnetoencephalography (MEG) have different sensitivities to differently configured brain activations, making them complimentary in providing independent information for better detection and inverse reconstruction of brain sources. In the present study, we developed an integrative approach, which integrates a novel sparse electromagnetic source imaging method, i.e., variation-based cortical current density (VB-SCCD), together with the combined use of EEG and MEG data in reconstructing complex brain activity. To perform simultaneous analysis of multimodal data, we proposed to normalize EEG and MEG signals according to their individual noise levels to create unit-free measures. Our Monte Carlo simulations demonstrated that this integrative approach is capable of reconstructing complex cortical brain activations (up to 10 simultaneously activated and randomly located sources). Results from experimental data showed that complex brain activations evoked in a face recognition task were successfully reconstructed using the integrative approach, which were consistent with other research findings and validated by independent data from functional magnetic resonance imaging using the same stimulus protocol. Reconstructed cortical brain activations from both simulations and experimental data provided precise source localizations as well as accurate spatial extents of localized sources. In comparison with studies using EEG or MEG alone, the performance of cortical source reconstructions using combined EEG and MEG was significantly improved. We demonstrated that this new sparse ESI methodology with integrated analysis of EEG and MEG data could accurately probe spatiotemporal processes of complex human brain activations. This is promising for noninvasively studying large-scale brain networks of high clinical and scientific significance. Copyright © 2011 Wiley Periodicals, Inc.

Optimal use of EEG recordings to target active brain areas with transcranial electrical stimulation.

PubMed

Dmochowski, Jacek P; Koessler, Laurent; Norcia, Anthony M; Bikson, Marom; Parra, Lucas C

2017-08-15

To demonstrate causal relationships between brain and behavior, investigators would like to guide brain stimulation using measurements of neural activity. Particularly promising in this context are electroencephalography (EEG) and transcranial electrical stimulation (TES), as they are linked by a reciprocity principle which, despite being known for decades, has not led to a formalism for relating EEG recordings to optimal stimulation parameters. Here we derive a closed-form expression for the TES configuration that optimally stimulates (i.e., targets) the sources of recorded EEG, without making assumptions about source location or distribution. We also derive a duality between TES targeting and EEG source localization, and demonstrate that in cases where source localization fails, so does the proposed targeting. Numerical simulations with multiple head models confirm these theoretical predictions and quantify the achieved stimulation in terms of focality and intensity. We show that constraining the stimulation currents automatically selects optimal montages that involve only a few (4-7) electrodes, with only incremental loss in performance when targeting focal activations. The proposed technique allows brain scientists and clinicians to rationally target the sources of observed EEG and thus overcomes a major obstacle to the realization of individualized or closed-loop brain stimulation. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Optimal use of EEG recordings to target active brain areas with transcranial electrical stimulation

PubMed Central

Dmochowski, Jacek P.; Koessler, Laurent; Norcia, Anthony M.; Bikson, Marom; Parra, Lucas C.

2018-01-01

To demonstrate causal relationships between brain and behavior, investigators would like to guide brain stimulation using measurements of neural activity. Particularly promising in this context are electroencephalography (EEG) and transcranial electrical stimulation (TES), as they are linked by a reciprocity principle which, despite being known for decades, has not led to a formalism for relating EEG recordings to optimal stimulation parameters. Here we derive a closed-form expression for the TES configuration that optimally stimulates (i.e., targets) the sources of recorded EEG, without making assumptions about source location or distribution. We also derive a duality between TES targeting and EEG source localization, and demonstrate that in cases where source localization fails, so does the proposed targeting. Numerical simulations with multiple head models confirm these theoretical predictions and quantify the achieved stimulation in terms of focality and intensity. We show that constraining the stimulation currents automatically selects optimal montages that involve only a few (4–7) electrodes, with only incremental loss in performance when targeting focal activations. The proposed technique allows brain scientists and clinicians to rationally target the sources of observed EEG and thus overcomes a major obstacle to the realization of individualized or closed-loop brain stimulation. PMID:28578130

Continuous EEG source imaging enhances analysis of EEG-fMRI in focal epilepsy.

PubMed

Vulliemoz, S; Rodionov, R; Carmichael, D W; Thornton, R; Guye, M; Lhatoo, S D; Michel, C M; Duncan, J S; Lemieux, L

2010-02-15

EEG-correlated fMRI (EEG-fMRI) studies can reveal haemodynamic changes associated with Interictal Epileptic Discharges (IED). Methodological improvements are needed to increase sensitivity and specificity for localising the epileptogenic zone. We investigated whether the estimated EEG source activity improved models of the BOLD changes in EEG-fMRI data, compared to conventional < event-related > designs based solely on the visual identification of IED. Ten patients with pharmaco-resistant focal epilepsy underwent EEG-fMRI. EEG Source Imaging (ESI) was performed on intra-fMRI averaged IED to identify the irritative zone. The continuous activity of this estimated IED source (cESI) over the entire recording was used for fMRI analysis (cESI model). The maps of BOLD signal changes explained by cESI were compared to results of the conventional IED-related model. ESI was concordant with non-invasive data in 13/15 different types of IED. The cESI model explained significant additional BOLD variance in regions concordant with video-EEG, structural MRI or, when available, intracranial EEG in 10/15 IED. The cESI model allowed better detection of the BOLD cluster, concordant with intracranial EEG in 4/7 IED, compared to the IED model. In 4 IED types, cESI-related BOLD signal changes were diffuse with a pattern suggestive of contamination of the source signal by artefacts, notably incompletely corrected motion and pulse artefact. In one IED type, there was no significant BOLD change with either model. Continuous EEG source imaging can improve the modelling of BOLD changes related to interictal epileptic activity and this may enhance the localisation of the irritative zone. Copyright 2009 Elsevier Inc. All rights reserved.

A simple method for EEG guided transcranial electrical stimulation without models.

PubMed

Cancelli, Andrea; Cottone, Carlo; Tecchio, Franca; Truong, Dennis Q; Dmochowski, Jacek; Bikson, Marom

2016-06-01

There is longstanding interest in using EEG measurements to inform transcranial Electrical Stimulation (tES) but adoption is lacking because users need a simple and adaptable recipe. The conventional approach is to use anatomical head-models for both source localization (the EEG inverse problem) and current flow modeling (the tES forward model), but this approach is computationally demanding, requires an anatomical MRI, and strict assumptions about the target brain regions. We evaluate techniques whereby tES dose is derived from EEG without the need for an anatomical head model, target assumptions, difficult case-by-case conjecture, or many stimulation electrodes. We developed a simple two-step approach to EEG-guided tES that based on the topography of the EEG: (1) selects locations to be used for stimulation; (2) determines current applied to each electrode. Each step is performed based solely on the EEG with no need for head models or source localization. Cortical dipoles represent idealized brain targets. EEG-guided tES strategies are verified using a finite element method simulation of the EEG generated by a dipole, oriented either tangential or radial to the scalp surface, and then simulating the tES-generated electric field produced by each model-free technique. These model-free approaches are compared to a 'gold standard' numerically optimized dose of tES that assumes perfect understanding of the dipole location and head anatomy. We vary the number of electrodes from a few to over three hundred, with focality or intensity as optimization criterion. Model-free approaches evaluated include (1) voltage-to-voltage, (2) voltage-to-current; (3) Laplacian; and two Ad-Hoc techniques (4) dipole sink-to-sink; and (5) sink to concentric. Our results demonstrate that simple ad hoc approaches can achieve reasonable targeting for the case of a cortical dipole, remarkably with only 2-8 electrodes and no need for a model of the head. Our approach is verified directly only for a theoretically localized source, but may be potentially applied to an arbitrary EEG topography. For its simplicity and linearity, our recipe for model-free EEG guided tES lends itself to broad adoption and can be applied to static (tDCS), time-variant (e.g., tACS, tRNS, tPCS), or closed-loop tES.

A simple method for EEG guided transcranial electrical stimulation without models

NASA Astrophysics Data System (ADS)

Cancelli, Andrea; Cottone, Carlo; Tecchio, Franca; Truong, Dennis Q.; Dmochowski, Jacek; Bikson, Marom

2016-06-01

Objective. There is longstanding interest in using EEG measurements to inform transcranial Electrical Stimulation (tES) but adoption is lacking because users need a simple and adaptable recipe. The conventional approach is to use anatomical head-models for both source localization (the EEG inverse problem) and current flow modeling (the tES forward model), but this approach is computationally demanding, requires an anatomical MRI, and strict assumptions about the target brain regions. We evaluate techniques whereby tES dose is derived from EEG without the need for an anatomical head model, target assumptions, difficult case-by-case conjecture, or many stimulation electrodes. Approach. We developed a simple two-step approach to EEG-guided tES that based on the topography of the EEG: (1) selects locations to be used for stimulation; (2) determines current applied to each electrode. Each step is performed based solely on the EEG with no need for head models or source localization. Cortical dipoles represent idealized brain targets. EEG-guided tES strategies are verified using a finite element method simulation of the EEG generated by a dipole, oriented either tangential or radial to the scalp surface, and then simulating the tES-generated electric field produced by each model-free technique. These model-free approaches are compared to a ‘gold standard’ numerically optimized dose of tES that assumes perfect understanding of the dipole location and head anatomy. We vary the number of electrodes from a few to over three hundred, with focality or intensity as optimization criterion. Main results. Model-free approaches evaluated include (1) voltage-to-voltage, (2) voltage-to-current; (3) Laplacian; and two Ad-Hoc techniques (4) dipole sink-to-sink; and (5) sink to concentric. Our results demonstrate that simple ad hoc approaches can achieve reasonable targeting for the case of a cortical dipole, remarkably with only 2-8 electrodes and no need for a model of the head. Significance. Our approach is verified directly only for a theoretically localized source, but may be potentially applied to an arbitrary EEG topography. For its simplicity and linearity, our recipe for model-free EEG guided tES lends itself to broad adoption and can be applied to static (tDCS), time-variant (e.g., tACS, tRNS, tPCS), or closed-loop tES.

Source-based neurofeedback methods using EEG recordings: training altered brain activity in a functional brain source derived from blind source separation

PubMed Central

White, David J.; Congedo, Marco; Ciorciari, Joseph

2014-01-01

A developing literature explores the use of neurofeedback in the treatment of a range of clinical conditions, particularly ADHD and epilepsy, whilst neurofeedback also provides an experimental tool for studying the functional significance of endogenous brain activity. A critical component of any neurofeedback method is the underlying physiological signal which forms the basis for the feedback. While the past decade has seen the emergence of fMRI-based protocols training spatially confined BOLD activity, traditional neurofeedback has utilized a small number of electrode sites on the scalp. As scalp EEG at a given electrode site reflects a linear mixture of activity from multiple brain sources and artifacts, efforts to successfully acquire some level of control over the signal may be confounded by these extraneous sources. Further, in the event of successful training, these traditional neurofeedback methods are likely influencing multiple brain regions and processes. The present work describes the use of source-based signal processing methods in EEG neurofeedback. The feasibility and potential utility of such methods were explored in an experiment training increased theta oscillatory activity in a source derived from Blind Source Separation (BSS) of EEG data obtained during completion of a complex cognitive task (spatial navigation). Learned increases in theta activity were observed in two of the four participants to complete 20 sessions of neurofeedback targeting this individually defined functional brain source. Source-based EEG neurofeedback methods using BSS may offer important advantages over traditional neurofeedback, by targeting the desired physiological signal in a more functionally and spatially specific manner. Having provided preliminary evidence of the feasibility of these methods, future work may study a range of clinically and experimentally relevant brain processes where individual brain sources may be targeted by source-based EEG neurofeedback. PMID:25374520

Source analysis of MEG activities during sleep (abstract)

NASA Astrophysics Data System (ADS)

Ueno, S.; Iramina, K.

1991-04-01

The present study focuses on magnetic fields of the brain activities during sleep, in particular on K-complexes, vertex waves, and sleep spindles in human subjects. We analyzed these waveforms based on both topographic EEG (electroencephalographic) maps and magnetic fields measurements, called MEGs (magnetoencephalograms). The components of magnetic fields perpendicular to the surface of the head were measured using a dc SQUID magnetometer with a second derivative gradiometer. In our computer simulation, the head is assumed to be a homogeneous spherical volume conductor, with electric sources of brain activity modeled as current dipoles. Comparison of computer simulations with the measured data, particularly the MEG, suggests that the source of K-complexes can be modeled by two current dipoles. A source for the vertex wave is modeled by a single current dipole which orients along the body axis out of the head. By again measuring the simultaneous MEG and EEG signals, it is possible to uniquely determine the orientation of this dipole, particularly when it is tilted slightly off-axis. In sleep stage 2, fast waves of magnetic fields consistently appeared, but EEG spindles appeared intermittently. The results suggest that there exist sources which are undetectable by electrical measurement but are detectable by magnetic-field measurement. Such source can be described by a pair of opposing dipoles of which directions are oppositely oriented.

Effects of sertraline on brain current source of the high beta frequency band: analysis of electroencephalography during audiovisual erotic stimulation in males with premature ejaculation.

PubMed

Kwon, O Y; Kam, S C; Choi, J H; Do, J M; Hyun, J S

2011-01-01

To identify the effects of sertraline, a selective serotonin reuptake inhibitor, for the treatment of premature ejaculation (PE), changes in brain current-source density (CSD) of the high beta frequency band (22-30 Hz) induced by sertraline administration were investigated during audiovisual erotic stimulation. Eleven patients with PE (36.9±7.8 yrs) and 11 male volunteers (24.2±1.9 years) were enrolled. Scalp electroencephalography (EEG) was conducted twice: once before sertraline administration and then again 4 h after the administration of 50 mg sertraline. Statistical non-parametric maps were obtained using the EEG segments to detect the current-density differences in the high beta frequency bands (beta-3, 22-30 Hz) between the EEGs before and after sertraline administration in the patient group and between the patient group and controls after the administration of sertraline during the erotic video sessions. Comparing between before and after sertraline administration in the patients with PE, the CSD of the high beta frequency band at 4 h after sertraline administration increased significantly in both superior frontal gyri and the right medial frontal gyrus (P<0.01). The CSD of the beta-3 band of the patients with PE were less activated significantly in the middle and superior temporal gyrus, lingual and fusiform gyrus, inferior occipital gyrus and cuneus of the right cerebral hemisphere compared with the normal volunteers 4 h after sertraline administration (P<0.01). In conclusion, sertraline administration increased the CSD in both the superior frontal and right middle temporal gyrus in patients with PE. The results suggest that the increased neural activity in these particular cerebral regions after sertraline administration may be associated with inhibitory effects on ejaculation in patients with PE.

Effect of Sertraline on Current-Source Distribution of the High Beta Frequency Band: Analysis of Electroencephalography under Audiovisual Erotic Stimuli in Healthy, Right-Handed Males.

PubMed

Lee, Seung Hyun; Hyun, Jae Seog; Kwon, Oh-Young

2010-08-01

The purpose of this study was to examine the cerebral changes in high beta frequency oscillations (22-30 Hz) induced by sertraline and by audiovisual erotic stimuli in healthy adult males. Scalp electroencephalographies (EEGs) were conducted twice in 11 healthy, right-handed males, once before sertraline intake and again 4 hours thereafter. The EEGs included four sessions recorded sequentially while the subjects were resting, watching a music video, resting, and watching an erotic video for 3 minutes, 5 minutes, 3 minutes, and 5 minutes, respectively. We performed frequency-domain analysis using the EEGs with a distributed model of current-source analysis. The statistical nonparametric maps were obtained from the sessions of watching erotic and music videos (p<0.05). The erotic stimuli decreased the current-source density of the high beta frequency band in the middle frontal gyrus, the precentral gyrus, the postcentral gyrus, and the supramarginal gyrus of the left cerebral hemisphere in the baseline EEGs taken before sertraline intake (p<0.05). The erotic stimuli did not induce any changes in current-source distribution of the brain 4 hours after sertraline intake. It is speculated that erotic stimuli may decrease the function of the middle frontal gyrus, the precentral gyrus, the postcentral gyrus, and the supramarginal gyrus of the left cerebral hemisphere in healthy adult males. This change may debase the inhibitory control of the brain against erotic stimuli. Sertraline may reduce the decrement in inhibitory control.

Effect of Sertraline on Current-Source Distribution of the High Beta Frequency Band: Analysis of Electroencephalography under Audiovisual Erotic Stimuli in Healthy, Right-Handed Males

PubMed Central

Lee, Seung Hyun; Hyun, Jae Seog

2010-01-01

Purpose The purpose of this study was to examine the cerebral changes in high beta frequency oscillations (22-30 Hz) induced by sertraline and by audiovisual erotic stimuli in healthy adult males. Materials and Methods Scalp electroencephalographies (EEGs) were conducted twice in 11 healthy, right-handed males, once before sertraline intake and again 4 hours thereafter. The EEGs included four sessions recorded sequentially while the subjects were resting, watching a music video, resting, and watching an erotic video for 3 minutes, 5 minutes, 3 minutes, and 5 minutes, respectively. We performed frequency-domain analysis using the EEGs with a distributed model of current-source analysis. The statistical nonparametric maps were obtained from the sessions of watching erotic and music videos (p<0.05). Results The erotic stimuli decreased the current-source density of the high beta frequency band in the middle frontal gyrus, the precentral gyrus, the postcentral gyrus, and the supramarginal gyrus of the left cerebral hemisphere in the baseline EEGs taken before sertraline intake (p<0.05). The erotic stimuli did not induce any changes in current-source distribution of the brain 4 hours after sertraline intake. Conclusions It is speculated that erotic stimuli may decrease the function of the middle frontal gyrus, the precentral gyrus, the postcentral gyrus, and the supramarginal gyrus of the left cerebral hemisphere in healthy adult males. This change may debase the inhibitory control of the brain against erotic stimuli. Sertraline may reduce the decrement in inhibitory control. PMID:20733961

Electric Field Encephalography as a tool for functional brain research: a modeling study.

PubMed

Petrov, Yury; Sridhar, Srinivas

2013-01-01

We introduce the notion of Electric Field Encephalography (EFEG) based on measuring electric fields of the brain and demonstrate, using computer modeling, that given the appropriate electric field sensors this technique may have significant advantages over the current EEG technique. Unlike EEG, EFEG can be used to measure brain activity in a contactless and reference-free manner at significant distances from the head surface. Principal component analysis using simulated cortical sources demonstrated that electric field sensors positioned 3 cm away from the scalp and characterized by the same signal-to-noise ratio as EEG sensors provided the same number of uncorrelated signals as scalp EEG. When positioned on the scalp, EFEG sensors provided 2-3 times more uncorrelated signals. This significant increase in the number of uncorrelated signals can be used for more accurate assessment of brain states for non-invasive brain-computer interfaces and neurofeedback applications. It also may lead to major improvements in source localization precision. Source localization simulations for the spherical and Boundary Element Method (BEM) head models demonstrated that the localization errors are reduced two-fold when using electric fields instead of electric potentials. We have identified several techniques that could be adapted for the measurement of the electric field vector required for EFEG and anticipate that this study will stimulate new experimental approaches to utilize this new tool for functional brain research.

Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods.

PubMed

Gramfort, Alexandre; Kowalski, Matthieu; Hämäläinen, Matti

2012-04-07

Magneto- and electroencephalography (M/EEG) measure the electromagnetic fields produced by the neural electrical currents. Given a conductor model for the head, and the distribution of source currents in the brain, Maxwell's equations allow one to compute the ensuing M/EEG signals. Given the actual M/EEG measurements and the solution of this forward problem, one can localize, in space and in time, the brain regions that have produced the recorded data. However, due to the physics of the problem, the limited number of sensors compared to the number of possible source locations, and measurement noise, this inverse problem is ill-posed. Consequently, additional constraints are needed. Classical inverse solvers, often called minimum norm estimates (MNE), promote source estimates with a small ℓ₂ norm. Here, we consider a more general class of priors based on mixed norms. Such norms have the ability to structure the prior in order to incorporate some additional assumptions about the sources. We refer to such solvers as mixed-norm estimates (MxNE). In the context of M/EEG, MxNE can promote spatially focal sources with smooth temporal estimates with a two-level ℓ₁/ℓ₂ mixed-norm, while a three-level mixed-norm can be used to promote spatially non-overlapping sources between different experimental conditions. In order to efficiently solve the optimization problems of MxNE, we introduce fast first-order iterative schemes that for the ℓ₁/ℓ₂ norm give solutions in a few seconds making such a prior as convenient as the simple MNE. Furthermore, thanks to the convexity of the optimization problem, we can provide optimality conditions that guarantee global convergence. The utility of the methods is demonstrated both with simulations and experimental MEG data.

Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods

PubMed Central

Gramfort, Alexandre; Kowalski, Matthieu; Hämäläinen, Matti

2012-01-01

Magneto- and electroencephalography (M/EEG) measure the electromagnetic fields produced by the neural electrical currents. Given a conductor model for the head, and the distribution of source currents in the brain, Maxwell’s equations allow one to compute the ensuing M/EEG signals. Given the actual M/EEG measurements and the solution of this forward problem, one can localize, in space and in time, the brain regions than have produced the recorded data. However, due to the physics of the problem, the limited number of sensors compared to the number of possible source locations, and measurement noise, this inverse problem is ill-posed. Consequently, additional constraints are needed. Classical inverse solvers, often called Minimum Norm Estimates (MNE), promote source estimates with a small ℓ2 norm. Here, we consider a more general class of priors based on mixed-norms. Such norms have the ability to structure the prior in order to incorporate some additional assumptions about the sources. We refer to such solvers as Mixed-Norm Estimates (MxNE). In the context of M/EEG, MxNE can promote spatially focal sources with smooth temporal estimates with a two-level ℓ1/ℓ2 mixed-norm, while a three-level mixed-norm can be used to promote spatially non-overlapping sources between different experimental conditions. In order to efficiently solve the optimization problems of MxNE, we introduce fast first-order iterative schemes that for the ℓ1/ℓ2 norm give solutions in a few seconds making such a prior as convenient as the simple MNE. Furhermore, thanks to the convexity of the optimization problem, we can provide optimality conditions that guarantee global convergence. The utility of the methods is demonstrated both with simulations and experimental MEG data. PMID:22421459

Independent component analysis of EEG dipole source localization in resting and action state of brain

NASA Astrophysics Data System (ADS)

Almurshedi, Ahmed; Ismail, Abd Khamim

2015-04-01

EEG source localization was studied in order to determine the location of the brain sources that are responsible for the measured potentials at the scalp electrodes using EEGLAB with Independent Component Analysis (ICA) algorithm. Neuron source locations are responsible in generating current dipoles in different states of brain through the measured potentials. The current dipole sources localization are measured by fitting an equivalent current dipole model using a non-linear optimization technique with the implementation of standardized boundary element head model. To fit dipole models to ICA components in an EEGLAB dataset, ICA decomposition is performed and appropriate components to be fitted are selected. The topographical scalp distributions of delta, theta, alpha, and beta power spectrum and cross coherence of EEG signals are observed. In close eyes condition it shows that during resting and action states of brain, alpha band was activated from occipital (O1, O2) and partial (P3, P4) area. Therefore, parieto-occipital area of brain are active in both resting and action state of brain. However cross coherence tells that there is more coherence between right and left hemisphere in action state of brain than that in the resting state. The preliminary result indicates that these potentials arise from the same generators in the brain.

MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy.

PubMed

Chowdhury, Rasheda Arman; Zerouali, Younes; Hedrich, Tanguy; Heers, Marcel; Kobayashi, Eliane; Lina, Jean-Marc; Grova, Christophe

2015-11-01

The purpose of this study is to develop and quantitatively assess whether fusion of EEG and MEG (MEEG) data within the maximum entropy on the mean (MEM) framework increases the spatial accuracy of source localization, by yielding better recovery of the spatial extent and propagation pathway of the underlying generators of inter-ictal epileptic discharges (IEDs). The key element in this study is the integration of the complementary information from EEG and MEG data within the MEM framework. MEEG was compared with EEG and MEG when localizing single transient IEDs. The fusion approach was evaluated using realistic simulation models involving one or two spatially extended sources mimicking propagation patterns of IEDs. We also assessed the impact of the number of EEG electrodes required for an efficient EEG-MEG fusion. MEM was compared with minimum norm estimate, dynamic statistical parametric mapping, and standardized low-resolution electromagnetic tomography. The fusion approach was finally assessed on real epileptic data recorded from two patients showing IEDs simultaneously in EEG and MEG. Overall the localization of MEEG data using MEM provided better recovery of the source spatial extent, more sensitivity to the source depth and more accurate detection of the onset and propagation of IEDs than EEG or MEG alone. MEM was more accurate than the other methods. MEEG proved more robust than EEG and MEG for single IED localization in low signal-to-noise ratio conditions. We also showed that only few EEG electrodes are required to bring additional relevant information to MEG during MEM fusion.

Electrophysiological evidence during episodic prospection implicates medial prefrontal and bilateral middle temporal gyrus.

PubMed

Hsu, Chia-Fen; Sonuga-Barke, Edmund J S

2016-08-01

fMRI studies have implicated the medial prefrontal cortex and medial temporal lobe, components of the default mode network (DMN), in episodic prospection. This study compared quantitative EEG localized to these DMN regions during prospection and during resting and while waiting for rewards. EEG was recorded in twenty-two adults while they were asked to (i) envision future monetary episodes; (ii) wait for rewards and (iii) rest. Activation sources were localized to core DMN regions. EEG power and phase coherence were compared across conditions. Prospection, compared to resting and waiting, was associated with reduced power in the medial prefrontal gyrus and increased power in the bilateral medial temporal gyrus across frequency bands as well as greater phase synchrony between these regions in the delta band. The current quantitative EEG analysis confirms prior fMRI research suggesting that medial prefrontal and medial temporal gyrus interactions are central to the capacity for episodic prospection. Copyright © 2016 Elsevier B.V. All rights reserved.

EEG-NIRS based assessment of neurovascular coupling during anodal transcranial direct current stimulation--a stroke case series.

PubMed

Dutta, Anirban; Jacob, Athira; Chowdhury, Shubhajit Roy; Das, Abhijit; Nitsche, Michael A

2015-04-01

A method for electroencephalography (EEG) - near-infrared spectroscopy (NIRS) based assessment of neurovascular coupling (NVC) during anodal transcranial direct current stimulation (tDCS). Anodal tDCS modulates cortical neural activity leading to a hemodynamic response, which was used to identify impaired NVC functionality. In this study, the hemodynamic response was estimated with NIRS. NIRS recorded changes in oxy-hemoglobin (HbO2) and deoxy-hemoglobin (Hb) concentrations during anodal tDCS-induced activation of the cortical region located under the electrode and in-between the light sources and detectors. Anodal tDCS-induced alterations in the underlying neuronal current generators were also captured with EEG. Then, a method for the assessment of NVC underlying the site of anodal tDCS was proposed that leverages the Hilbert-Huang Transform. The case series including four chronic (>6 months) ischemic stroke survivors (3 males, 1 female from age 31 to 76) showed non-stationary effects of anodal tDCS on EEG that correlated with the HbO2 response. Here, the initial dip in HbO2 at the beginning of anodal tDCS corresponded with an increase in the log-transformed mean-power of EEG within 0.5Hz-11.25Hz frequency band. The cross-correlation coefficient changed signs but was comparable across subjects during and after anodal tDCS. The log-transformed mean-power of EEG lagged HbO2 response during tDCS but then led post-tDCS. This case series demonstrated changes in the degree of neurovascular coupling to a 0.526 A/m(2) square-pulse (0-30 s) of anodal tDCS. The initial dip in HbO2 needs to be carefully investigated in a larger cohort, for example in patients with small vessel disease.

Association of posterior EEG alpha with prioritization of religion or spirituality: a replication and extension at 20-year follow-up

PubMed Central

Tenke, Craig E.; Kayser, Jürgen; Svob, Connie; Miller, Lisa; Alvarenga, Jorge E.; Abraham, Karen; Warner, Virginia; Wickramaratne, Priya; Weissman, Myrna M.; Bruder, Gerard E.

2017-01-01

A prior report (Tenke et al. 2013 Biol. Psychol. 94:426–432) found that participants who rated religion or spirituality (R/S) highly important had greater posterior alpha after 10 years compared to those who did not. Participants who subsequently lowered their rating also had prominent alpha, while those who increased their rating did not. Here we report EEG findings 20 years after initial assessment. Clinical evaluations and R/S ratings were obtained from 73 (52 new) participants in a longitudinal study of family risk for depression. Frequency PCA of current source density transformed EEG concisely quantified posterior alpha. Those who initially rated R/S as highly important had greater alpha compared to those who did not, even if their R/S rating later increased. Furthermore, changes in religious denomination were associated with decreased alpha. Results suggest the possibility of a critical stage in the ontogenesis of R/S that is linked to posterior resting alpha. PMID:28119066

Group Independent Component Analysis (gICA) and Current Source Density (CSD) in the study of EEG in ADHD adults.

PubMed

Ponomarev, Valery A; Mueller, Andreas; Candrian, Gian; Grin-Yatsenko, Vera A; Kropotov, Juri D

2014-01-01

To investigate the performance of the spectral analysis of resting EEG, Current Source Density (CSD) and group independent components (gIC) in diagnosing ADHD adults. Power spectra of resting EEG, CSD and gIC (19 channels, linked ears reference, eyes open/closed) from 96 ADHD and 376 healthy adults were compared between eyes open and eyes closed conditions, and between groups of subjects. Pattern of differences in gIC and CSD spectral power between conditions was approximately similar, whereas it was more widely spatially distributed for EEG. Size effect (Cohen's d) of differences in gIC and CSD spectral power between groups of subjects was considerably greater than in the case of EEG. Significant reduction of gIC and CSD spectral power depending on conditions was found in ADHD patients. Reducing power in a wide frequency range in the fronto-central areas is a common phenomenon regardless of whether the eyes were open or closed. Spectral power of local EEG activity isolated by gICA or CSD in the fronto-central areas may be a suitable marker for discrimination of ADHD and healthy adults. Spectral analysis of gIC and CSD provides better sensitivity to discriminate ADHD and healthy adults. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Length matters: Improved high field EEG-fMRI recordings using shorter EEG cables.

PubMed

Assecondi, Sara; Lavallee, Christina; Ferrari, Paolo; Jovicich, Jorge

2016-08-30

The use of concurrent EEG-fMRI recordings has increased in recent years, allowing new avenues of medical and cognitive neuroscience research; however, currently used setups present problems with data quality and reproducibility. We propose a compact experimental setup for concurrent EEG-fMRI at 4T and compare it to a more standard reference setup. The compact setup uses short EEG cables connecting to the amplifiers, which are placed right at the back of the head RF coil on a form-fitting extension force-locked to the patient MR bed. We compare the two setups in terms of sensitivity to MR-room environmental noise, interferences between measuring devices (EEG or fMRI), and sensitivity to functional responses in a visual stimulation paradigm. The compact setup reduces the system sensitivity to both external noise and MR-induced artefacts by at least 60%, with negligible EEG noise induced from the mechanical vibrations of the cryogenic cooling compression pump. The compact setup improved EEG data quality and the overall performance of MR-artifact correction techniques. Both setups were similar in terms of the fMRI data, with higher reproducibility for cable placement within the scanner in the compact setup. This improved compact setup may be relevant to MR laboratories interested in reducing the sensitivity of their EEG-fMRI experimental setup to external noise sources, setting up an EEG-fMRI workplace for the first time, or for creating a more reproducible configuration of equipment and cables. Implications for safety and ergonomics are discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open-source TESA software.

PubMed

Rogasch, Nigel C; Sullivan, Caley; Thomson, Richard H; Rose, Nathan S; Bailey, Neil W; Fitzgerald, Paul B; Farzan, Faranak; Hernandez-Pavon, Julio C

2017-02-15

The concurrent use of transcranial magnetic stimulation with electroencephalography (TMS-EEG) is growing in popularity as a method for assessing various cortical properties such as excitability, oscillations and connectivity. However, this combination of methods is technically challenging, resulting in artifacts both during recording and following typical EEG analysis methods, which can distort the underlying neural signal. In this article, we review the causes of artifacts in EEG recordings resulting from TMS, as well as artifacts introduced during analysis (e.g. as the result of filtering over high-frequency, large amplitude artifacts). We then discuss methods for removing artifacts, and ways of designing pipelines to minimise analysis-related artifacts. Finally, we introduce the TMS-EEG signal analyser (TESA), an open-source extension for EEGLAB, which includes functions that are specific for TMS-EEG analysis, such as removing and interpolating the TMS pulse artifact, removing and minimising TMS-evoked muscle activity, and analysing TMS-evoked potentials. The aims of TESA are to provide users with easy access to current TMS-EEG analysis methods and to encourage direct comparisons of these methods and pipelines. It is hoped that providing open-source functions will aid in both improving and standardising analysis across the field of TMS-EEG research. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Frontal midline theta and the error-related negativity: neurophysiological mechanisms of action regulation.

PubMed

Luu, Phan; Tucker, Don M; Makeig, Scott

2004-08-01

The error-related negativity (ERN) is an event-related potential (ERP) peak occurring between 50 and 100 ms after the commission of a speeded motor response that the subject immediately realizes to be in error. The ERN is believed to index brain processes that monitor action outcomes. Our previous analyses of ERP and EEG data suggested that the ERN is dominated by partial phase-locking of intermittent theta-band EEG activity. In this paper, this possibility is further evaluated. The possibility that the ERN is produced by phase-locking of theta-band EEG activity was examined by analyzing the single-trial EEG traces from a forced-choice speeded response paradigm before and after applying theta-band (4-7 Hz) filtering and by comparing the averaged and single-trial phase-locked (ERP) and non-phase-locked (other) EEG data. Electrical source analyses were used to estimate the brain sources involved in the generation of the ERN. Beginning just before incorrect button presses in a speeded choice response paradigm, midfrontal theta-band activity increased in amplitude and became partially and transiently phase-locked to the subject's motor response, accounting for 57% of ERN peak amplitude. The portion of the theta-EEG activity increase remaining after subtracting the response-locked ERP from each trial was larger and longer lasting after error responses than after correct responses, extending on average 400 ms beyond the ERN peak. Multiple equivalent-dipole source analysis suggested 3 possible equivalent dipole sources of the theta-bandpassed ERN, while the scalp distribution of non-phase-locked theta amplitude suggested the presence of additional frontal theta-EEG sources. These results appear consistent with a body of research that demonstrates a relationship between limbic theta activity and action regulation, including error monitoring and learning.

Scalp and Source Power Topography in Sleepwalking and Sleep Terrors: A High-Density EEG Study

PubMed Central

Castelnovo, Anna; Riedner, Brady A.; Smith, Richard F.; Tononi, Giulio; Boly, Melanie; Benca, Ruth M.

2016-01-01

Study Objectives: To examine scalp and source power topography in sleep arousals disorders (SADs) using high-density EEG (hdEEG). Methods: Fifteen adult subjects with sleep arousal disorders (SADs) and 15 age- and gender-matched good sleeping healthy controls were recorded in a sleep laboratory setting using a 256 channel EEG system. Results: Scalp EEG analysis of all night NREM sleep revealed a localized decrease in slow wave activity (SWA) power (1–4 Hz) over centro-parietal regions relative to the rest of the brain in SADs compared to good sleeping healthy controls. Source modelling analysis of 5-minute segments taken from N3 during the first half of the night revealed that the local decrease in SWA power was prominent at the level of the cingulate, motor, and sensori-motor associative cortices. Similar patterns were also evident during REM sleep and wake. These differences in local sleep were present in the absence of any detectable clinical or electrophysiological sign of arousal. Conclusions: Overall, results suggest the presence of local sleep differences in the brain of SADs patients during nights without clinical episodes. The persistence of similar topographical changes in local EEG power during REM sleep and wakefulness points to trait-like functional changes that cross the boundaries of NREM sleep. The regions identified by source imaging are consistent with the current neurophysiological understanding of SADs as a disorder caused by local arousals in motor and cingulate cortices. Persistent localized changes in neuronal excitability may predispose affected subjects to clinical episodes. Citation: Castelnovo A, Riedner BA, Smith RF, Tononi G, Boly M, Benca RM. Scalp and source power topography in sleepwalking and sleep terrors: a high-density EEG study. SLEEP 2016;39(10):1815–1825. PMID:27568805

Pediatric ICU EEG Monitoring: Current Resources and Practice in the United States and Canada

PubMed Central

Sanchez, Sarah M.; Carpenter, Jessica; Chapman, Kevin E.; Dlugos, Dennis J.; Gallentine, William; Giza, Christopher C.; Goldstein, Joshua L.; Hahn, Cecil D.; Kessler, Sudha Kilaru; Loddenkemper, Tobias; Riviello, James J.; Abend, Nicholas S.

2013-01-01

PURPOSE To describe current continuous EEG (cEEG) utilization in critically ill children. METHODS An online survey of pediatric neurologists from 50 United States (U.S.) and 11 Canadian institutions was conducted in August 2011. RESULTS Responses were received from 58 of 61 (95%) surveyed institutions. Common cEEG indications are altered mental status after a seizure or status epilepticus (97%), altered mental status of unknown etiology (88%), or altered mental status with an acute primary neurological condition (88%). The median number of patients undergoing cEEG per month per center increased from August 2010 to August 2011 (6 to 10 per month in U.S., 2 to 3 per month in Canada). Few institutions have clinical pathways addressing cEEG use (31%). Physicians most commonly review cEEG twice per day (37%). There is variability regarding which services can order cEEG, the degree of neurology involvement, technologist availability, and whether technologists perform cEEG screening. CONCLUSIONS Among the surveyed institutions, which included primarily large academic centers, cEEG use in pediatric intensive care units is increasing and is often considered indicated for children with altered mental status at risk for non-convulsive seizures. However, there remains substantial variability in cEEG access and utilization among institutions. PMID:23545766

Bluetooth Communication Interface for EEG Signal Recording in Hyperbaric Chambers.

PubMed

Pastena, Lucio; Formaggio, Emanuela; Faralli, Fabio; Melucci, Massimo; Rossi, Marco; Gagliardi, Riccardo; Ricciardi, Lucio; Storti, Silvia F

2015-07-01

Recording biological signals inside a hyperbaric chamber poses technical challenges (the steel walls enclosing it greatly attenuate or completely block the signals as in a Faraday cage), practical (lengthy cables creating eddy currents), and safety (sparks hazard from power supply to the electronic apparatus inside the chamber) which can be overcome with new wireless technologies. In this technical report we present the design and implementation of a Bluetooth system for electroencephalographic (EEG) recording inside a hyperbaric chamber and describe the feasibility of EEG signal transmission outside the chamber. Differently from older systems, this technology allows the online recording of amplified signals, without interference from eddy currents. In an application of this technology, we measured EEG activity in professional divers under three experimental conditions in a hyperbaric chamber to determine how oxygen, assumed at a constant hyperbaric pressure of 2.8 ATA , affects the bioelectrical activity. The EEG spectral power estimated by fast Fourier transform and the cortical sources of the EEG rhythms estimated by low-resolution brain electromagnetic analysis were analyzed in three different EEG acquisitions: breathing air at sea level; breathing oxygen at a simulated depth of 18 msw, and breathing air at sea level after decompression.

Combining EEG and MEG for the Reconstruction of Epileptic Activity Using a Calibrated Realistic Volume Conductor Model

PubMed Central

Aydin, Ümit; Vorwerk, Johannes; Küpper, Philipp; Heers, Marcel; Kugel, Harald; Galka, Andreas; Hamid, Laith; Wellmer, Jörg; Kellinghaus, Christoph; Rampp, Stefan; Wolters, Carsten Hermann

2014-01-01

To increase the reliability for the non-invasive determination of the irritative zone in presurgical epilepsy diagnosis, we introduce here a new experimental and methodological source analysis pipeline that combines the complementary information in EEG and MEG, and apply it to data from a patient, suffering from refractory focal epilepsy. Skull conductivity parameters in a six compartment finite element head model with brain anisotropy, constructed from individual MRI data, are estimated in a calibration procedure using somatosensory evoked potential (SEP) and field (SEF) data. These data are measured in a single run before acquisition of further runs of spontaneous epileptic activity. Our results show that even for single interictal spikes, volume conduction effects dominate over noise and need to be taken into account for accurate source analysis. While cerebrospinal fluid and brain anisotropy influence both modalities, only EEG is sensitive to skull conductivity and conductivity calibration significantly reduces the difference in especially depth localization of both modalities, emphasizing its importance for combining EEG and MEG source analysis. On the other hand, localization differences which are due to the distinct sensitivity profiles of EEG and MEG persist. In case of a moderate error in skull conductivity, combined source analysis results can still profit from the different sensitivity profiles of EEG and MEG to accurately determine location, orientation and strength of the underlying sources. On the other side, significant errors in skull modeling are reflected in EEG reconstruction errors and could reduce the goodness of fit to combined datasets. For combined EEG and MEG source analysis, we therefore recommend calibrating skull conductivity using additionally acquired SEP/SEF data. PMID:24671208

Intracranial EEG potentials estimated from MEG sources: A new approach to correlate MEG and iEEG data in epilepsy.

PubMed

Grova, Christophe; Aiguabella, Maria; Zelmann, Rina; Lina, Jean-Marc; Hall, Jeffery A; Kobayashi, Eliane

2016-05-01

Detection of epileptic spikes in MagnetoEncephaloGraphy (MEG) requires synchronized neuronal activity over a minimum of 4cm2. We previously validated the Maximum Entropy on the Mean (MEM) as a source localization able to recover the spatial extent of the epileptic spike generators. The purpose of this study was to evaluate quantitatively, using intracranial EEG (iEEG), the spatial extent recovered from MEG sources by estimating iEEG potentials generated by these MEG sources. We evaluated five patients with focal epilepsy who had a pre-operative MEG acquisition and iEEG with MRI-compatible electrodes. Individual MEG epileptic spikes were localized along the cortical surface segmented from a pre-operative MRI, which was co-registered with the MRI obtained with iEEG electrodes in place for identification of iEEG contacts. An iEEG forward model estimated the influence of every dipolar source of the cortical surface on each iEEG contact. This iEEG forward model was applied to MEG sources to estimate iEEG potentials that would have been generated by these sources. MEG-estimated iEEG potentials were compared with measured iEEG potentials using four source localization methods: two variants of MEM and two standard methods equivalent to minimum norm and LORETA estimates. Our results demonstrated an excellent MEG/iEEG correspondence in the presumed focus for four out of five patients. In one patient, the deep generator identified in iEEG could not be localized in MEG. MEG-estimated iEEG potentials is a promising method to evaluate which MEG sources could be retrieved and validated with iEEG data, providing accurate results especially when applied to MEM localizations. Hum Brain Mapp 37:1661-1683, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Neural Correlates of Three Promising Endophenotypes of Depression: Evidence from the EMBARC Study

PubMed Central

Webb, Christian A; Dillon, Daniel G; Pechtel, Pia; Goer, Franziska K; Murray, Laura; Huys, Quentin JM; Fava, Maurizio; McGrath, Patrick J; Weissman, Myrna; Parsey, Ramin; Kurian, Benji T; Adams, Phillip; Weyandt, Sarah; Trombello, Joseph M; Grannemann, Bruce; Cooper, Crystal M; Deldin, Patricia; Tenke, Craig; Trivedi, Madhukar; Bruder, Gerard; Pizzagalli, Diego A

2016-01-01

Major depressive disorder (MDD) is clinically, and likely pathophysiologically, heterogeneous. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes. Guided by the NIMH Research Domain Criteria initiative, we used source localization of scalp-recorded EEG resting data to examine the neural correlates of three emerging endophenotypes of depression: neuroticism, blunted reward learning, and cognitive control deficits. Data were drawn from the ongoing multi-site EMBARC study. We estimated intracranial current density for standard EEG frequency bands in 82 unmedicated adults with MDD, using Low-Resolution Brain Electromagnetic Tomography. Region-of-interest and whole-brain analyses tested associations between resting state EEG current density and endophenotypes of interest. Neuroticism was associated with increased resting gamma (36.5–44 Hz) current density in the ventral (subgenual) anterior cingulate cortex (ACC) and orbitofrontal cortex (OFC). In contrast, reduced cognitive control correlated with decreased gamma activity in the left dorsolateral prefrontal cortex (dlPFC), decreased theta (6.5–8 Hz) and alpha2 (10.5–12 Hz) activity in the dorsal ACC, and increased alpha2 activity in the right dlPFC. Finally, blunted reward learning correlated with lower OFC and left dlPFC gamma activity. Computational modeling of trial-by-trial reinforcement learning further indicated that lower OFC gamma activity was linked to reduced reward sensitivity. Three putative endophenotypes of depression were found to have partially dissociable resting intracranial EEG correlates, reflecting different underlying neural dysfunctions. Overall, these findings highlight the need to parse the heterogeneity of MDD by focusing on promising endophenotypes linked to specific pathophysiological abnormalities. PMID:26068725

Mode and site of acupuncture modulation in the human brain: 3D (124-ch) EEG power spectrum mapping and source imaging.

PubMed

Chen, Andrew C N; Liu, Feng-Jun; Wang, Li; Arendt-Nielsen, Lars

2006-02-15

This study determined: (a) if acupuncture stimulation at a traditional site might modulate ongoing EEG as compared with stimulation of a control site; (b) if high-frequency vs. low-frequency stimulation could exert differential effects of acupuncture; (c) if the observed effects of acupuncture were specific to certain EEG bands; and (d) if the acupuncture effect could be isolated at a specific scalp field, with its putative underlying intracranial source. Twelve healthy male volunteers (age range 22-35) participated in two experimental sessions separated by 1 week, which involved transcutaneous acupoint stimulation at selected acupoint (Li 4, HeGu) vs. a mock point at the fourth interosseous muscle area on the left hand in high (HF: 100 Hz) vs. low-frequency (LF: 2 Hz) stimulation by counter-balanced order. 124-ch EEG data were used to analyze the Delta, Theta, Alpha-1, Alpha-2, Beta, and Gamma bands. The absolute EEG powers (muv2) at focal maxima across three stages (baseline, stimulation, post) were examined by two-way (condition, stage) repeated measures ANOVA. The activity of the Theta power significantly decreased (P = 0.02), compared with control during HF but not LF stimulation at acupoint stimulation, however, there was no study effect at the mock point. A decreased Theta EEG power was prominent at the frontal midline sites (FCz, Fz) and the contralateral right hemisphere front site (FCC2h). In contrast, the Theta power of low-frequency stimulation showed an increase from the baseline as those in both controlled mock point stimulations. The observed high-frequency acupoint stimulation effects of Theta EEG were only present during, but not after, simulation. The topographic Theta activity was tentatively identified to originate from the intracranial current source in cingulate cortex, likely ACC. It is likely that short-term cortical plasticity occurs during high-frequency but not low-frequency stimulation at the HeGu point, but not mock point. We suggest that HeGu acupuncture stimulation modulates limbic cingulum by a frequency modulation mode, which then may damp nociceptive processing in the brain.

Association of posterior EEG alpha with prioritization of religion or spirituality: A replication and extension at 20-year follow-up.

PubMed

Tenke, Craig E; Kayser, Jürgen; Svob, Connie; Miller, Lisa; Alvarenga, Jorge E; Abraham, Karen; Warner, Virginia; Wickramaratne, Priya; Weissman, Myrna M; Bruder, Gerard E

2017-03-01

A prior report (Tenke et al., 2013 Biol. Psychol. 94:426-432) found that participants who rated religion or spirituality (R/S) highly important had greater posterior alpha after 10 years compared to those who did not. Participants who subsequently lowered their rating also had prominent alpha, while those who increased their rating did not. Here we report EEG findings 20 years after initial assessment. Clinical evaluations and R/S ratings were obtained from 73 (52 new) participants in a longitudinal study of family risk for depression. Frequency PCA of current source density transformed EEG concisely quantified posterior alpha. Those who initially rated R/S as highly important had greater alpha compared to those who did not, even if their R/S rating later increased. Furthermore, changes in religious denomination were associated with decreased alpha. Results suggest the possibility of a critical stage in the ontogenesis of R/S that is linked to posterior resting alpha. Copyright © 2017 Elsevier B.V. All rights reserved.

Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features.

PubMed

Shim, Miseon; Hwang, Han-Jeong; Kim, Do-Won; Lee, Seung-Hwan; Im, Chang-Hwan

2016-10-01

Recently, an increasing number of researchers have endeavored to develop practical tools for diagnosing patients with schizophrenia using machine learning techniques applied to EEG biomarkers. Although a number of studies showed that source-level EEG features can potentially be applied to the differential diagnosis of schizophrenia, most studies have used only sensor-level EEG features such as ERP peak amplitude and power spectrum for machine learning-based diagnosis of schizophrenia. In this study, we used both sensor-level and source-level features extracted from EEG signals recorded during an auditory oddball task for the classification of patients with schizophrenia and healthy controls. EEG signals were recorded from 34 patients with schizophrenia and 34 healthy controls while each subject was asked to attend to oddball tones. Our results demonstrated higher classification accuracy when source-level features were used together with sensor-level features, compared to when only sensor-level features were used. In addition, the selected sensor-level features were mostly found in the frontal area, and the selected source-level features were mostly extracted from the temporal area, which coincide well with the well-known pathological region of cognitive processing in patients with schizophrenia. Our results suggest that our approach would be a promising tool for the computer-aided diagnosis of schizophrenia. Copyright © 2016 Elsevier B.V. All rights reserved.

The temporal evolution of electromagnetic markers sensitive to the capacity limits of visual short-term memory.

PubMed

Mitchell, Daniel J; Cusack, Rhodri

2011-01-01

An electroencephalographic (EEG) marker of the limited contents of human visual short-term memory (VSTM) has previously been described. Termed contralateral delay activity, this consists of a sustained, posterior, negative potential that correlates with memory load and is greatest contralateral to the remembered hemifield. The current investigation replicates this finding and uses magnetoencephalography (MEG) to characterize its magnetic counterparts and their neural generators as they evolve throughout the memory delay. A parametric manipulation of memory load, within and beyond capacity limits, allows separation of signals that asymptote with behavioral VSTM performance from additional responses that contribute to a linear increase with set-size. Both EEG and MEG yielded bilateral signals that track the number of objects held in memory, and contralateral signals that are independent of memory load. In MEG, unlike EEG, the contralateral interaction between hemisphere and item load is much weaker, suggesting that bilateral and contralateral markers of memory load reflect distinct sources to which EEG and MEG are differentially sensitive. Nonetheless, source estimation allowed both the bilateral and the weaker contralateral capacity-limited responses to be localized, along with a load-independent contralateral signal. Sources of global and hemisphere-specific signals all localized to the posterior intraparietal sulcus during the early delay. However the bilateral load response peaked earlier and its generators shifted later in the delay. Therefore the hemifield-specific response may be more closely tied to memory maintenance while the global load response may be involved in initial processing of a limited number of attended objects, such as their individuation or consolidation into memory.

Direction of Magnetoencephalography Sources Associated with Feedback and Feedforward Contributions in a Visual Object Recognition Task

PubMed Central

Ahlfors, Seppo P.; Jones, Stephanie R.; Ahveninen, Jyrki; Hämäläinen, Matti S.; Belliveau, John W.; Bar, Moshe

2014-01-01

Identifying inter-area communication in terms of the hierarchical organization of functional brain areas is of considerable interest in human neuroimaging. Previous studies have suggested that the direction of magneto- and electroencephalography (MEG, EEG) source currents depends on the layer-specific input patterns into a cortical area. We examined the direction in MEG source currents in a visual object recognition experiment in which there were specific expectations of activation in the fusiform region being driven by either feedforward or feedback inputs. The source for the early non-specific visual evoked response, presumably corresponding to feedforward driven activity, pointed outward, i.e., away from the white matter. In contrast, the source for the later, object-recognition related signals, expected to be driven by feedback inputs, pointed inward, toward the white matter. Associating specific features of the MEG/EEG source waveforms to feedforward and feedback inputs could provide unique information about the activation patterns within hierarchically organized cortical areas. PMID:25445356

Scalp and Source Power Topography in Sleepwalking and Sleep Terrors: A High-Density EEG Study.

PubMed

Castelnovo, Anna; Riedner, Brady A; Smith, Richard F; Tononi, Giulio; Boly, Melanie; Benca, Ruth M

2016-10-01

To examine scalp and source power topography in sleep arousals disorders (SADs) using high-density EEG (hdEEG). Fifteen adult subjects with sleep arousal disorders (SADs) and 15 age- and gender-matched good sleeping healthy controls were recorded in a sleep laboratory setting using a 256 channel EEG system. Scalp EEG analysis of all night NREM sleep revealed a localized decrease in slow wave activity (SWA) power (1-4 Hz) over centro-parietal regions relative to the rest of the brain in SADs compared to good sleeping healthy controls. Source modelling analysis of 5-minute segments taken from N3 during the first half of the night revealed that the local decrease in SWA power was prominent at the level of the cingulate, motor, and sensori-motor associative cortices. Similar patterns were also evident during REM sleep and wake. These differences in local sleep were present in the absence of any detectable clinical or electrophysiological sign of arousal. Overall, results suggest the presence of local sleep differences in the brain of SADs patients during nights without clinical episodes. The persistence of similar topographical changes in local EEG power during REM sleep and wakefulness points to trait-like functional changes that cross the boundaries of NREM sleep. The regions identified by source imaging are consistent with the current neurophysiological understanding of SADs as a disorder caused by local arousals in motor and cingulate cortices. Persistent localized changes in neuronal excitability may predispose affected subjects to clinical episodes. © 2016 Associated Professional Sleep Societies, LLC.

Spatial filters and automated spike detection based on brain topographies improve sensitivity of EEG-fMRI studies in focal epilepsy.

PubMed

Siniatchkin, Michael; Moeller, Friederike; Jacobs, Julia; Stephani, Ulrich; Boor, Rainer; Wolff, Stephan; Jansen, Olav; Siebner, Hartwig; Scherg, Michael

2007-09-01

The ballistocardiogram (BCG) represents one of the most prominent sources of artifacts that contaminate the electroencephalogram (EEG) during functional MRI. The BCG artifacts may affect the detection of interictal epileptiform discharges (IED) in patients with epilepsy, reducing the sensitivity of the combined EEG-fMRI method. In this study we improved the BCG artifact correction using a multiple source correction (MSC) approach. On the one hand, a source analysis of the IEDs was applied to the EEG data obtained outside the MRI scanner to prevent the distortion of EEG signals of interest during the correction of BCG artifacts. On the other hand, the topographies of the BCG artifacts were defined based on the EEG recorded inside the scanner. The topographies of the BCG artifacts were then added to the surrogate model of IED sources and a combined source model was applied to the data obtained inside the scanner. The artifact signal was then subtracted without considerable distortion of the IED topography. The MSC approach was compared with the traditional averaged artifact subtraction (AAS) method. Both methods reduced the spectral power of BCG-related harmonics and enabled better detection of IEDs. Compared with the conventional AAS method, the MSC approach increased the sensitivity of IED detection because the IED signal was less attenuated when subtracting the BCG artifacts. The proposed MSC method is particularly useful in situations in which the BCG artifact is spatially correlated and time-locked with the EEG signal produced by the focal brain activity of interest.

A new wavelet transform to sparsely represent cortical current densities for EEG/MEG inverse problems.

PubMed

Liao, Ke; Zhu, Min; Ding, Lei

2013-08-01

The present study investigated the use of transform sparseness of cortical current density on human brain surface to improve electroencephalography/magnetoencephalography (EEG/MEG) inverse solutions. Transform sparseness was assessed by evaluating compressibility of cortical current densities in transform domains. To do that, a structure compression method from computer graphics was first adopted to compress cortical surface structure, either regular or irregular, into hierarchical multi-resolution meshes. Then, a new face-based wavelet method based on generated multi-resolution meshes was proposed to compress current density functions defined on cortical surfaces. Twelve cortical surface models were built by three EEG/MEG softwares and their structural compressibility was evaluated and compared by the proposed method. Monte Carlo simulations were implemented to evaluate the performance of the proposed wavelet method in compressing various cortical current density distributions as compared to other two available vertex-based wavelet methods. The present results indicate that the face-based wavelet method can achieve higher transform sparseness than vertex-based wavelet methods. Furthermore, basis functions from the face-based wavelet method have lower coherence against typical EEG and MEG measurement systems than vertex-based wavelet methods. Both high transform sparseness and low coherent measurements suggest that the proposed face-based wavelet method can improve the performance of L1-norm regularized EEG/MEG inverse solutions, which was further demonstrated in simulations and experimental setups using MEG data. Thus, this new transform on complicated cortical structure is promising to significantly advance EEG/MEG inverse source imaging technologies. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Effects of A 60 Hz Magnetic Field of Up to 50 milliTesla on Human Tremor and EEG: A Pilot Study.

PubMed

Davarpanah Jazi, Shirin; Modolo, Julien; Baker, Cadence; Villard, Sebastien; Legros, Alexandre

2017-11-24

Humans are surrounded by sources of daily exposure to power-frequency (60 Hz in North America) magnetic fields (MFs). Such time-varying MFs induce electric fields and currents in living structures which possibly lead to biological effects. The present pilot study examined possible extremely low frequency (ELF) MF effects on human neuromotor control in general, and physiological postural tremor and electroencephalography (EEG) in particular. Since the EEG cortical mu-rhythm (8-12 Hz) from the primary motor cortex and physiological tremor are related, it was hypothesized that a 60 Hz MF exposure focused on this cortical region could acutely modulate human physiological tremor. Ten healthy volunteers (age: 23.8 ± 4 SD) were fitted with a MRI-compatible EEG cap while exposed to 11 MF conditions (60 Hz, 0 to 50 mT rms , 5 mT rms increments). Simultaneously, physiological tremor (recorded from the contralateral index finger) and EEG (from associated motor and somatosensory brain regions) were measured. Results showed no significant main effect of MF exposure conditions on any of the analyzed physiological tremor characteristics. In terms of EEG, no significant effects of the MF were observed for C1, C3, C5 and CP1 electrodes. However, a significant main effect was found for CP3 and CP5 electrodes, both suggesting a decreased mu-rhythm spectral power with increasing MF flux density. This is however not confirmed by Bonferroni corrected pairwise comparisons. Considering both EEG and tremor findings, no effect of the MF exposure on human motor control was observed. However, MF exposure had a subtle effect on the mu-rhythm amplitude in the brain region involved in tactile perception. Current findings are to be considered with caution due to the small size of this pilot work, but they provide preliminary insights to international agencies establishing guidelines regarding electromagnetic field exposure with new experimental data acquired in humans exposed to high mT-range MFs.

Incorporating modern neuroscience findings to improve brain-computer interfaces: tracking auditory attention.

PubMed

Wronkiewicz, Mark; Larson, Eric; Lee, Adrian Kc

2016-10-01

Brain-computer interface (BCI) technology allows users to generate actions based solely on their brain signals. However, current non-invasive BCIs generally classify brain activity recorded from surface electroencephalography (EEG) electrodes, which can hinder the application of findings from modern neuroscience research. In this study, we use source imaging-a neuroimaging technique that projects EEG signals onto the surface of the brain-in a BCI classification framework. This allowed us to incorporate prior research from functional neuroimaging to target activity from a cortical region involved in auditory attention. Classifiers trained to detect attention switches performed better with source imaging projections than with EEG sensor signals. Within source imaging, including subject-specific anatomical MRI information (instead of using a generic head model) further improved classification performance. This source-based strategy also reduced accuracy variability across three dimensionality reduction techniques-a major design choice in most BCIs. Our work shows that source imaging provides clear quantitative and qualitative advantages to BCIs and highlights the value of incorporating modern neuroscience knowledge and methods into BCI systems.

Quantitative EEG and Current Source Density Analysis of Combined Antiepileptic Drugs and Dopaminergic Agents in Genetic Epilepsy: Two Case Studies.

PubMed

Emory, Hamlin; Wells, Christopher; Mizrahi, Neptune

2015-07-01

Two adolescent females with absence epilepsy were classified, one as attention deficit and the other as bipolar disorder. Physical and cognitive exams identified hypotension, bradycardia, and cognitive dysfunction. Their initial electroencephalograms (EEGs) were considered slightly slow, but within normal limits. Quantitative EEG (QEEG) data included relative theta excess and low alpha mean frequencies. A combined treatment of antiepileptic drugs with a catecholamine agonist/reuptake inhibitor was sequentially used. Both patients' physical and cognitive functions improved and they have remained seizure free. The clinical outcomes were correlated with statistically significant changes in QEEG measures toward normal Z-scores in both anterior and posterior regions. In addition, low resolution electromagnetic tomography (LORETA) Z-scored source correlation analyses of the initial and treated QEEG data showed normalized patterns, supporting a neuroanatomic resolution. This study presents preliminary evidence for a neurophysiologic approach to patients with absence epilepsy and comorbid disorders and may provide a method for further research. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Equivalent charge source model based iterative maximum neighbor weight for sparse EEG source localization.

PubMed

Xu, Peng; Tian, Yin; Lei, Xu; Hu, Xiao; Yao, Dezhong

2008-12-01

How to localize the neural electric activities within brain effectively and precisely from the scalp electroencephalogram (EEG) recordings is a critical issue for current study in clinical neurology and cognitive neuroscience. In this paper, based on the charge source model and the iterative re-weighted strategy, proposed is a new maximum neighbor weight based iterative sparse source imaging method, termed as CMOSS (Charge source model based Maximum neighbOr weight Sparse Solution). Different from the weight used in focal underdetermined system solver (FOCUSS) where the weight for each point in the discrete solution space is independently updated in iterations, the new designed weight for each point in each iteration is determined by the source solution of the last iteration at both the point and its neighbors. Using such a new weight, the next iteration may have a bigger chance to rectify the local source location bias existed in the previous iteration solution. The simulation studies with comparison to FOCUSS and LORETA for various source configurations were conducted on a realistic 3-shell head model, and the results confirmed the validation of CMOSS for sparse EEG source localization. Finally, CMOSS was applied to localize sources elicited in a visual stimuli experiment, and the result was consistent with those source areas involved in visual processing reported in previous studies.

Ghost interactions in MEG/EEG source space: A note of caution on inter-areal coupling measures.

PubMed

Palva, J Matias; Wang, Sheng H; Palva, Satu; Zhigalov, Alexander; Monto, Simo; Brookes, Matthew J; Schoffelen, Jan-Mathijs; Jerbi, Karim

2018-06-01

When combined with source modeling, magneto- (MEG) and electroencephalography (EEG) can be used to study long-range interactions among cortical processes non-invasively. Estimation of such inter-areal connectivity is nevertheless hindered by instantaneous field spread and volume conduction, which artificially introduce linear correlations and impair source separability in cortical current estimates. To overcome the inflating effects of linear source mixing inherent to standard interaction measures, alternative phase- and amplitude-correlation based connectivity measures, such as imaginary coherence and orthogonalized amplitude correlation have been proposed. Being by definition insensitive to zero-lag correlations, these techniques have become increasingly popular in the identification of correlations that cannot be attributed to field spread or volume conduction. We show here, however, that while these measures are immune to the direct effects of linear mixing, they may still reveal large numbers of spurious false positive connections through field spread in the vicinity of true interactions. This fundamental problem affects both region-of-interest-based analyses and all-to-all connectome mappings. Most importantly, beyond defining and illustrating the problem of spurious, or "ghost" interactions, we provide a rigorous quantification of this effect through extensive simulations. Additionally, we further show that signal mixing also significantly limits the separability of neuronal phase and amplitude correlations. We conclude that spurious correlations must be carefully considered in connectivity analyses in MEG/EEG source space even when using measures that are immune to zero-lag correlations. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Incorporating an ERP Project into Undergraduate Instruction

PubMed Central

Nyhus, Erika; Curtis, Nancy

2016-01-01

Electroencephalogram (EEG) is a relatively non-invasive, simple technique, and recent advances in open source analysis tools make it feasible to implement EEG as a component in undergraduate neuroscience curriculum. We have successfully led students to design novel experiments, record EEG data, and analyze event-related potentials (ERPs) during a one-semester laboratory course for undergraduates in cognitive neuroscience. First, students learned how to set up an EEG recording and completed an analysis tutorial. Students then learned how to set up a novel EEG experiment; briefly, they formed groups of four and designed an EEG experiment on a topic of their choice. Over the course of two weeks students collected behavioral and EEG data. Each group then analyzed their behavioral and ERP data and presented their results both as a presentation and as a final paper. Upon completion of the group project students reported a deeper understanding of cognitive neuroscience methods and a greater appreciation for the strengths and weaknesses of the EEG technique. Although recent advances in open source software made this project possible, it also required access to EEG recording equipment and proprietary software. Future efforts should be directed at making publicly available datasets to learn ERP analysis techniques and making publicly available EEG recording and analysis software to increase the accessibility of hands-on research experience in undergraduate cognitive neuroscience laboratory courses. PMID:27385925

EEG background activity is abnormal in the temporal and inferior parietal cortex in benign rolandic epilepsy of childhood: a LORETA study.

PubMed

Besenyei, M; Varga, E; Fekete, I; Puskás, S; Hollódy, K; Fogarasi, A; Emri, M; Opposits, G; Kis, S A; Clemens, B

2012-01-01

Benign rolandic epilepsy of childhood (BERS) is an epilepsy syndrome with presumably genetic-developmental etiology. The pathological basis of this syndrome is completely unknown. We postulated that a developmental abnormality presumably results in abnormal EEG background activity findings. 20 children with typical BERS and an age- and sex-matched group of healthy control children underwent EEG recording and analysis. 60×2 s epochs of waking EEG background activity (without epileptiform potentials and artifacts) were analyzed in the 1-25 Hz frequency range, in very narrow bands (VNB, 1 Hz bandwidth). LORETA (Low Resolution Electromagnetic Tomography) localized multiple distributed sources of EEG background activity in the Talairach space. LORETA activity (current source density) was computed for 2394 voxels and 25 VNBs. Normalized LORETA data were processed to voxel-wise comparison between the BERS and control groups. Bonferroni-corrected p<0.05 Student's t-values were accepted as statistically significant. Increased LORETA activity was found in the BERS group (as compared to the controls) in the left and right temporal lobes (fusiform gyri, posterior parts of the superior, middle and inferior temporal gyri) and in the angular gyri in the parietal lobes, in the 4-6 Hz VNBs, mainly at 5 Hz. (1) Areas of abnormal LORETA activity exactly correspond to the temporal and parietal cortical areas that are major components of the Mirsky attention model and also the perisylvian speech network. Thus the LORETA findings may correspond to impaired attention and speech in BERS patients. (2) The LORETA findings may contribute to delineating the epileptic network in BERS. The novel findings may contribute to investigating neuropsychological disturbances and organization of the epileptic network in BERS. Copyright © 2011 Elsevier B.V. All rights reserved.

PyEEG: an open source Python module for EEG/MEG feature extraction.

PubMed

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

PubMed Central

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction. PMID:21512582

Electroencephalography (EEG) forward modeling via H(div) finite element sources with focal interpolation.

PubMed

Pursiainen, S; Vorwerk, J; Wolters, C H

2016-12-21

The goal of this study is to develop focal, accurate and robust finite element method (FEM) based approaches which can predict the electric potential on the surface of the computational domain given its structure and internal primary source current distribution. While conducting an EEG evaluation, the placement of source currents to the geometrically complex grey matter compartment is a challenging but necessary task to avoid forward errors attributable to tissue conductivity jumps. Here, this task is approached via a mathematically rigorous formulation, in which the current field is modeled via divergence conforming H(div) basis functions. Both linear and quadratic functions are used while the potential field is discretized via the standard linear Lagrangian (nodal) basis. The resulting model includes dipolar sources which are interpolated into a random set of positions and orientations utilizing two alternative approaches: the position based optimization (PBO) and the mean position/orientation (MPO) method. These results demonstrate that the present dipolar approach can reach or even surpass, at least in some respects, the accuracy of two classical reference methods, the partial integration (PI) and St. Venant (SV) approach which utilize monopolar loads instead of dipolar currents.

Correspondence of electroencephalography and near-infrared spectroscopy sensitivities to the cerebral cortex using a high-density layout

PubMed Central

Giacometti, Paolo; Diamond, Solomon G.

2014-01-01

Abstract. This study investigates the correspondence of the cortical sensitivity of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). EEG forward model sensitivity to the cerebral cortex was calculated for 329 EEG electrodes following the 10-5 EEG positioning system using a segmented structural magnetic resonance imaging scan of a human subject. NIRS forward model sensitivity was calculated for the same subject using 156 NIRS source-detector pairs selected from 32 source and 32 detector optodes positioned on the scalp using a subset of the 10-5 EEG positioning system. Sensitivity correlations between colocalized NIRS source-detector pair groups and EEG channels yielded R=0.46±0.08. Groups of NIRS source-detector pairs with maximum correlations to EEG electrode sensitivities are tabulated. The mean correlation between the point spread functions for EEG and NIRS regions of interest (ROI) was R=0.43±0.07. Spherical ROIs with radii of 26 mm yielded the maximum correlation between EEG and NIRS averaged across all cortical mesh nodes. These sensitivity correlations between EEG and NIRS should be taken into account when designing multimodal studies of neurovascular coupling and when using NIRS as a statistical prior for EEG source localization. PMID:25558462

Evaluation of multiple comparison correction procedures in drug assessment studies using LORETA maps.

PubMed

Alonso, Joan Francesc; Romero, Sergio; Mañanas, Miguel Ángel; Rojas, Mónica; Riba, Jordi; Barbanoj, Manel José

2015-10-01

The identification of the brain regions involved in the neuropharmacological action is a potential procedure for drug development. These regions are commonly determined by the voxels showing significant statistical differences after comparing placebo-induced effects with drug-elicited effects. LORETA is an electroencephalography (EEG) source imaging technique frequently used to identify brain structures affected by the drug. The aim of the present study was to evaluate different methods for the correction of multiple comparisons in the LORETA maps. These methods which have been commonly used in neuroimaging and also simulated studies have been applied on a real case of pharmaco-EEG study where the effects of increasing benzodiazepine doses on the central nervous system measured by LORETA were investigated. Data consisted of EEG recordings obtained from nine volunteers who received single oral doses of alprazolam 0.25, 0.5, and 1 mg, and placebo in a randomized crossover double-blind design. The identification of active regions was highly dependent on the selected multiple test correction procedure. The combined criteria approach known as cluster mass was useful to reveal that increasing drug doses led to higher intensity and spread of the pharmacologically induced changes in intracerebral current density.

TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation.

PubMed

Atluri, Sravya; Frehlich, Matthew; Mei, Ye; Garcia Dominguez, Luis; Rogasch, Nigel C; Wong, Willy; Daskalakis, Zafiris J; Farzan, Faranak

2016-01-01

Concurrent recording of electroencephalography (EEG) during transcranial magnetic stimulation (TMS) is an emerging and powerful tool for studying brain health and function. Despite a growing interest in adaptation of TMS-EEG across neuroscience disciplines, its widespread utility is limited by signal processing challenges. These challenges arise due to the nature of TMS and the sensitivity of EEG to artifacts that often mask TMS-evoked potentials (TEP)s. With an increase in the complexity of data processing methods and a growing interest in multi-site data integration, analysis of TMS-EEG data requires the development of a standardized method to recover TEPs from various sources of artifacts. This article introduces TMSEEG, an open-source MATLAB application comprised of multiple algorithms organized to facilitate a step-by-step procedure for TMS-EEG signal processing. Using a modular design and interactive graphical user interface (GUI), this toolbox aims to streamline TMS-EEG signal processing for both novice and experienced users. Specifically, TMSEEG provides: (i) targeted removal of TMS-induced and general EEG artifacts; (ii) a step-by-step modular workflow with flexibility to modify existing algorithms and add customized algorithms; (iii) a comprehensive display and quantification of artifacts; (iv) quality control check points with visual feedback of TEPs throughout the data processing workflow; and (v) capability to label and store a database of artifacts. In addition to these features, the software architecture of TMSEEG ensures minimal user effort in initial setup and configuration of parameters for each processing step. This is partly accomplished through a close integration with EEGLAB, a widely used open-source toolbox for EEG signal processing. In this article, we introduce TMSEEG, validate its features and demonstrate its application in extracting TEPs across several single- and multi-pulse TMS protocols. As the first open-source GUI-based pipeline for TMS-EEG signal processing, this toolbox intends to promote the widespread utility and standardization of an emerging technology in brain research.

TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation

PubMed Central

Atluri, Sravya; Frehlich, Matthew; Mei, Ye; Garcia Dominguez, Luis; Rogasch, Nigel C.; Wong, Willy; Daskalakis, Zafiris J.; Farzan, Faranak

2016-01-01

Concurrent recording of electroencephalography (EEG) during transcranial magnetic stimulation (TMS) is an emerging and powerful tool for studying brain health and function. Despite a growing interest in adaptation of TMS-EEG across neuroscience disciplines, its widespread utility is limited by signal processing challenges. These challenges arise due to the nature of TMS and the sensitivity of EEG to artifacts that often mask TMS-evoked potentials (TEP)s. With an increase in the complexity of data processing methods and a growing interest in multi-site data integration, analysis of TMS-EEG data requires the development of a standardized method to recover TEPs from various sources of artifacts. This article introduces TMSEEG, an open-source MATLAB application comprised of multiple algorithms organized to facilitate a step-by-step procedure for TMS-EEG signal processing. Using a modular design and interactive graphical user interface (GUI), this toolbox aims to streamline TMS-EEG signal processing for both novice and experienced users. Specifically, TMSEEG provides: (i) targeted removal of TMS-induced and general EEG artifacts; (ii) a step-by-step modular workflow with flexibility to modify existing algorithms and add customized algorithms; (iii) a comprehensive display and quantification of artifacts; (iv) quality control check points with visual feedback of TEPs throughout the data processing workflow; and (v) capability to label and store a database of artifacts. In addition to these features, the software architecture of TMSEEG ensures minimal user effort in initial setup and configuration of parameters for each processing step. This is partly accomplished through a close integration with EEGLAB, a widely used open-source toolbox for EEG signal processing. In this article, we introduce TMSEEG, validate its features and demonstrate its application in extracting TEPs across several single- and multi-pulse TMS protocols. As the first open-source GUI-based pipeline for TMS-EEG signal processing, this toolbox intends to promote the widespread utility and standardization of an emerging technology in brain research. PMID:27774054

Propofol Anesthesia and Sleep: A High-Density EEG Study

PubMed Central

Murphy, Michael; Bruno, Marie-Aurelie; Riedner, Brady A.; Boveroux, Pierre; Noirhomme, Quentin; Landsness, Eric C.; Brichant, Jean-Francois; Phillips, Christophe; Massimini, Marcello; Laureys, Steven; Tononi, Giulio; Boly, Melanie

2011-01-01

Study Objectives: The electrophysiological correlates of anesthetic sedation remain poorly understood. We used high-density electroencephalography (hd-EEG) and source modeling to investigate the cortical processes underlying propofol anesthesia and compare them to sleep. Design: 256-channel EEG recordings in humans during propofol anesthesia. Setting: Hospital operating room. Patients or Participants: 8 healthy subjects (4 males) Interventions: N/A Measurements and Results: Initially, propofol induced increases in EEG power from 12–25 Hz. Loss of consciousness (LOC) was accompanied by the appearance of EEG slow waves that resembled the slow waves of NREM sleep. We compared slow waves in propofol to slow waves recorded during natural sleep and found that both populations of waves share similar cortical origins and preferentially propagate along the mesial components of the default network. However, propofol slow waves were spatially blurred compared to sleep slow waves and failed to effectively entrain spindle activity. Propofol also caused an increase in gamma (25–40 Hz) power that persisted throughout LOC. Source modeling analysis showed that this increase in gamma power originated from the anterior and posterior cingulate cortices. During LOC, we found increased gamma functional connectivity between these regions compared to the wakefulness. Conclusions: Propofol anesthesia is a sleep-like state and slow waves are associated with diminished consciousness even in the presence of high gamma activity. Citation: Murphy M; Bruno MA; Riedner BA; Boveroux P; Noirhomme Q; Landsness EC; Brichant JF; Phillips C; Massimini M; Laureys S; Tononi G; Boly M. Propofol anesthesia and sleep: a high-density EEG study. SLEEP 2011;34(3):283-291. PMID:21358845

s-SMOOTH: Sparsity and Smoothness Enhanced EEG Brain Tomography

PubMed Central

Li, Ying; Qin, Jing; Hsin, Yue-Loong; Osher, Stanley; Liu, Wentai

2016-01-01

EEG source imaging enables us to reconstruct current density in the brain from the electrical measurements with excellent temporal resolution (~ ms). The corresponding EEG inverse problem is an ill-posed one that has infinitely many solutions. This is due to the fact that the number of EEG sensors is usually much smaller than that of the potential dipole locations, as well as noise contamination in the recorded signals. To obtain a unique solution, regularizations can be incorporated to impose additional constraints on the solution. An appropriate choice of regularization is critically important for the reconstruction accuracy of a brain image. In this paper, we propose a novel Sparsity and SMOOthness enhanced brain TomograpHy (s-SMOOTH) method to improve the reconstruction accuracy by integrating two recently proposed regularization techniques: Total Generalized Variation (TGV) regularization and ℓ1−2 regularization. TGV is able to preserve the source edge and recover the spatial distribution of the source intensity with high accuracy. Compared to the relevant total variation (TV) regularization, TGV enhances the smoothness of the image and reduces staircasing artifacts. The traditional TGV defined on a 2D image has been widely used in the image processing field. In order to handle 3D EEG source images, we propose a voxel-based Total Generalized Variation (vTGV) regularization that extends the definition of second-order TGV from 2D planar images to 3D irregular surfaces such as cortex surface. In addition, the ℓ1−2 regularization is utilized to promote sparsity on the current density itself. We demonstrate that ℓ1−2 regularization is able to enhance sparsity and accelerate computations than ℓ1 regularization. The proposed model is solved by an efficient and robust algorithm based on the difference of convex functions algorithm (DCA) and the alternating direction method of multipliers (ADMM). Numerical experiments using synthetic data demonstrate the advantages of the proposed method over other state-of-the-art methods in terms of total reconstruction accuracy, localization accuracy and focalization degree. The application to the source localization of event-related potential data further demonstrates the performance of the proposed method in real-world scenarios. PMID:27965529

Tracking the Effect of Cathodal Transcranial Direct Current Stimulation on Cortical Excitability and Connectivity by Means of TMS-EEG

PubMed Central

Varoli, Erica; Pisoni, Alberto; Mattavelli, Giulia C.; Vergallito, Alessandra; Gallucci, Alessia; Mauro, Lilia D.; Rosanova, Mario; Bolognini, Nadia; Vallar, Giuseppe; Romero Lauro, Leonor J.

2018-01-01

Transcranial direct current stimulation (tDCS) is increasingly used in both research and therapeutic settings, but its precise mechanisms remain largely unknown. At a neuronal level, tDCS modulates cortical excitability by shifting the resting membrane potential in a polarity-dependent way: anodal stimulation increases the spontaneous firing rate, while cathodal decreases it. However, the neurophysiological underpinnings of anodal/cathodal tDCS seem to be different, as well as their behavioral effect, in particular when high order areas are involved, compared to when motor or sensory brain areas are targeted. Previously, we investigated the effect of anodal tDCS on cortical excitability, by means of a combination of Transcranial Magnetic Stimulation (TMS) and Electroencephalography (EEG). Results showed a diffuse rise of cortical excitability in a bilateral fronto-parietal network. In the present study, we tested, with the same paradigm, the effect of cathodal tDCS. Single pulse TMS was delivered over the left posterior parietal cortex (PPC), before, during, and after 10 min of cathodal or sham tDCS over the right PPC, while recording HD-EEG. Indexes of global and local cortical excitability were obtained both at sensors and cortical sources level. At sensors, global and local mean field power (GMFP and LMFP) were computed for three temporal windows (0–50, 50–100, and 100–150 ms), on all channels (GMFP), and in four different clusters of electrodes (LMFP, left and right, in frontal and parietal regions). After source reconstruction, Significant Current Density was computed at the global level, and for four Broadmann's areas (left/right BA 6 and 7). Both sensors and cortical sources results converge in showing no differences during and after cathodal tDCS compared to pre-stimulation sessions, both at global and local level. The same holds for sham tDCS. These data highlight an asymmetric impact of anodal and cathodal stimulation on cortical excitability, with a diffuse effect of anodal and no effect of cathodal tDCS over the parietal cortex. These results are consistent with the current literature: while anodal-excitatory and cathodal-inhibitory effects are well-established in the sensory and motor domains, both at physiological and behavioral levels, results for cathodal stimulation are more controversial for modulation of exitability of higher order areas. PMID:29867330

Intelligence and EEG current density using low-resolution electromagnetic tomography (LORETA).

PubMed

Thatcher, R W; North, D; Biver, C

2007-02-01

The purpose of this study was to compare EEG current source densities in high IQ subjects vs. low IQ subjects. Resting eyes closed EEG was recorded from 19 scalp locations with a linked ears reference from 442 subjects ages 5 to 52 years. The Wechsler Intelligence Test was administered and subjects were divided into low IQ (< or =90), middle IQ (>90 to <120) and high IQ (> or =120) groups. Low-resolution electromagnetic tomographic current densities (LORETA) from 2,394 cortical gray matter voxels were computed from 1-30 Hz based on each subject's EEG. Differences in current densities using t tests, multivariate analyses of covariance, and regression analyses were used to evaluate the relationships between IQ and current density in Brodmann area groupings of cortical gray matter voxels. Frontal, temporal, parietal, and occipital regions of interest (ROIs) consistently exhibited a direct relationship between LORETA current density and IQ. Maximal t test differences were present at 4 Hz, 9 Hz, 13 Hz, 18 Hz, and 30 Hz with different anatomical regions showing different maxima. Linear regression fits from low to high IQ groups were statistically significant (P < 0.0001). Intelligence is directly related to a general level of arousal and to the synchrony of neural populations driven by thalamo-cortical resonances. A traveling frame model of sequential microstates is hypothesized to explain the results.

Quantitative EEG and low resolution electromagnetic tomography (LORETA) imaging of patients with persistent auditory hallucinations.

PubMed

Lee, Seung-Hwan; Wynn, Jonathan K; Green, Michael F; Kim, Hyun; Lee, Kang-Joon; Nam, Min; Park, Joong-Kyu; Chung, Young-Cho

2006-04-01

Electrophysiological studies have demonstrated gamma and beta frequency oscillations in response to auditory stimuli. The purpose of this study was to test whether auditory hallucinations (AH) in schizophrenia patients reflect abnormalities in gamma and beta frequency oscillations and to investigate source generators of these abnormalities. This theory was tested using quantitative electroencephalography (qEEG) and low-resolution electromagnetic tomography (LORETA) source imaging. Twenty-five schizophrenia patients with treatment refractory AH, lasting for at least 2 years, and 23 schizophrenia patients with non-AH (N-AH) in the past 2 years were recruited for the study. Spectral analysis of the qEEG and source imaging of frequency bands of artifact-free 30 s epochs were examined during rest. AH patients showed significantly increased beta 1 and beta 2 frequency amplitude compared with N-AH patients. Gamma and beta (2 and 3) frequencies were significantly correlated in AH but not in N-AH patients. Source imaging revealed significantly increased beta (1 and 2) activity in the left inferior parietal lobule and the left medial frontal gyrus in AH versus N-AH patients. These results imply that AH is reflecting increased beta frequency oscillations with neural generators localized in speech-related areas.

High-resolution EEG (HR-EEG) and magnetoencephalography (MEG).

PubMed

Gavaret, M; Maillard, L; Jung, J

2015-03-01

High-resolution EEG (HR-EEG) and magnetoencephalography (MEG) allow the recording of spontaneous or evoked electromagnetic brain activity with excellent temporal resolution. Data must be recorded with high temporal resolution (sampling rate) and high spatial resolution (number of channels). Data analyses are based on several steps with selection of electromagnetic signals, elaboration of a head model and use of algorithms in order to solve the inverse problem. Due to considerable technical advances in spatial resolution, these tools now represent real methods of ElectroMagnetic Source Imaging. HR-EEG and MEG constitute non-invasive and complementary examinations, characterized by distinct sensitivities according to the location and orientation of intracerebral generators. In the presurgical assessment of drug-resistant partial epilepsies, HR-EEG and MEG can characterize and localize interictal activities and thus the irritative zone. HR-EEG and MEG often yield significant additional data that are complementary to other presurgical investigations and particularly relevant in MRI-negative cases. Currently, the determination of the epileptogenic zone and functional brain mapping remain rather less well-validated indications. In France, in 2014, HR-EEG is now part of standard clinical investigation of epilepsy, while MEG remains a research technique. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

EEG and MEG: sensitivity to epileptic spike activity as function of source orientation and depth.

PubMed

Hunold, A; Funke, M E; Eichardt, R; Stenroos, M; Haueisen, J

2016-07-01

Simultaneous electroencephalography (EEG) and magnetoencephalography (MEG) recordings of neuronal activity from epileptic patients reveal situations in which either EEG or MEG or both modalities show visible interictal spikes. While different signal-to-noise ratios (SNRs) of the spikes in EEG and MEG have been reported, a quantitative relation of spike source orientation and depth as well as the background brain activity to the SNR has not been established. We investigated this quantitative relationship for both dipole and patch sources in an anatomically realistic cortex model. Altogether, 5600 dipole and 3300 patch sources were distributed on the segmented cortical surfaces of two volunteers. The sources were classified according to their quantified depths and orientations, ranging from 20 mm to 60 mm below the skin surface and radial and tangential, respectively. The source time-courses mimicked an interictal spike, and the simulated background activity emulated resting activity. Simulations were conducted with individual three-compartment boundary element models. The SNR was evaluated for 128 EEG, 102 MEG magnetometer, and 204 MEG gradiometer channels. For superficial dipole and superficial patch sources, EEG showed higher SNRs for dominantly radial orientations, and MEG showed higher values for dominantly tangential orientations. Gradiometers provided higher SNR than magnetometers for superficial sources, particularly for those with dominantly tangential orientations. The orientation dependent difference in SNR in EEG and MEG gradually changed as the sources were located deeper, where the interictal spikes generated higher SNRs in EEG compared to those in MEG for all source orientations. With deep sources, the SNRs in gradiometers and magnetometers were of the same order. To better detect spikes, both EEG and MEG should be used.

EEG-Informed fMRI: A Review of Data Analysis Methods

PubMed Central

Abreu, Rodolfo; Leal, Alberto; Figueiredo, Patrícia

2018-01-01

The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest. PMID:29467634

Real-time Adaptive EEG Source Separation using Online Recursive Independent Component Analysis

PubMed Central

Hsu, Sheng-Hsiou; Mullen, Tim; Jung, Tzyy-Ping; Cauwenberghs, Gert

2016-01-01

Independent Component Analysis (ICA) has been widely applied to electroencephalographic (EEG) biosignal processing and brain-computer interfaces. The practical use of ICA, however, is limited by its computational complexity, data requirements for convergence, and assumption of data stationarity, especially for high-density data. Here we study and validate an optimized online recursive ICA algorithm (ORICA) with online recursive least squares (RLS) whitening for blind source separation of high-density EEG data, which offers instantaneous incremental convergence upon presentation of new data. Empirical results of this study demonstrate the algorithm's: (a) suitability for accurate and efficient source identification in high-density (64-channel) realistically-simulated EEG data; (b) capability to detect and adapt to non-stationarity in 64-ch simulated EEG data; and (c) utility for rapidly extracting principal brain and artifact sources in real 61-channel EEG data recorded by a dry and wearable EEG system in a cognitive experiment. ORICA was implemented as functions in BCILAB and EEGLAB and was integrated in an open-source Real-time EEG Source-mapping Toolbox (REST), supporting applications in ICA-based online artifact rejection, feature extraction for real-time biosignal monitoring in clinical environments, and adaptable classifications in brain-computer interfaces. PMID:26685257

EEG functional connectivity is partially predicted by underlying white matter connectivity

PubMed Central

Chu, CJ; Tanaka, N; Diaz, J; Edlow, BL; Wu, O; Hämäläinen, M; Stufflebeam, S; Cash, SS; Kramer, MA.

2015-01-01

Over the past decade, networks have become a leading model to illustrate both the anatomical relationships (structural networks) and the coupling of dynamic physiology (functional networks) linking separate brain regions. The relationship between these two levels of description remains incompletely understood and an area of intense research interest. In particular, it is unclear how cortical currents relate to underlying brain structural architecture. In addition, although theory suggests that brain communication is highly frequency dependent, how structural connections influence overlying functional connectivity in different frequency bands has not been previously explored. Here we relate functional networks inferred from statistical associations between source imaging of EEG activity and underlying cortico-cortical structural brain connectivity determined by probabilistic white matter tractography. We evaluate spontaneous fluctuating cortical brain activity over a long time scale (minutes) and relate inferred functional networks to underlying structural connectivity for broadband signals, as well as in seven distinct frequency bands. We find that cortical networks derived from source EEG estimates partially reflect both direct and indirect underlying white matter connectivity in all frequency bands evaluated. In addition, we find that when structural support is absent, functional connectivity is significantly reduced for high frequency bands compared to low frequency bands. The association between cortical currents and underlying white matter connectivity highlights the obligatory interdependence of functional and structural networks in the human brain. The increased dependence on structural support for the coupling of higher frequency brain rhythms provides new evidence for how underlying anatomy directly shapes emergent brain dynamics at fast time scales. PMID:25534110

Integration of EEG source imaging and fMRI during continuous viewing of natural movies.

PubMed

Whittingstall, Kevin; Bartels, Andreas; Singh, Vanessa; Kwon, Soyoung; Logothetis, Nikos K

2010-10-01

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are noninvasive neuroimaging tools which can be used to measure brain activity with excellent temporal and spatial resolution, respectively. By combining the neural and hemodynamic recordings from these modalities, we can gain better insight into how and where the brain processes complex stimuli, which may be especially useful in patients with different neural diseases. However, due to their vastly different spatial and temporal resolutions, the integration of EEG and fMRI recordings is not always straightforward. One fundamental obstacle has been that paradigms used for EEG experiments usually rely on event-related paradigms, while fMRI is not limited in this regard. Therefore, here we ask whether one can reliably localize stimulus-driven EEG activity using the continuously varying feature intensities occurring in natural movie stimuli presented over relatively long periods of time. Specifically, we asked whether stimulus-driven aspects in the EEG signal would be co-localized with the corresponding stimulus-driven BOLD signal during free viewing of a movie. Secondly, we wanted to integrate the EEG signal directly with the BOLD signal, by estimating the underlying impulse response function (IRF) that relates the BOLD signal to the underlying current density in the primary visual area (V1). We made sequential fMRI and 64-channel EEG recordings in seven subjects who passively watched 2-min-long segments of a James Bond movie. To analyze EEG data in this natural setting, we developed a method based on independent component analysis (ICA) to reject EEG artifacts due to blinks, subject movement, etc., in a way unbiased by human judgment. We then calculated the EEG source strength of this artifact-free data at each time point of the movie within the entire brain volume using low-resolution electromagnetic tomography (LORETA). This provided for every voxel in the brain (i.e., in 3D space) an estimate of the current density at every time point. We then carried out a correlation between the time series of visual contrast changes in the movie with that of EEG voxels. We found the most significant correlations in visual area V1, just as seen in previous fMRI studies (Bartels A, Zeki, S, Logothetis NK. Natural vision reveals regional specialization to local motion and to contrast-invariant, global flow in the human brain. Cereb Cortex 2008;18(3):705-717), but on the time scale of milliseconds rather than of seconds. To obtain an estimate of how the EEG signal relates to the BOLD signal, we calculated the IRF between the BOLD signal and the estimated current density in area V1. We found that this IRF was very similar to that observed using combined intracortical recordings and fMRI experiments in nonhuman primates. Taken together, these findings open a new approach to noninvasive mapping of the brain. It allows, firstly, the localization of feature-selective brain areas during natural viewing conditions with the temporal resolution of EEG. Secondly, it provides a tool to assess EEG/BOLD transfer functions during processing of more natural stimuli. This is especially useful in combined EEG/fMRI experiments, where one can now potentially study neural-hemodynamic relationships across the whole brain volume in a noninvasive manner. Copyright © 2010 Elsevier Inc. All rights reserved.

Brain activity underlying auditory perceptual learning during short period training: simultaneous fMRI and EEG recording

PubMed Central

2013-01-01

Background There is an accumulating body of evidence indicating that neuronal functional specificity to basic sensory stimulation is mutable and subject to experience. Although fMRI experiments have investigated changes in brain activity after relative to before perceptual learning, brain activity during perceptual learning has not been explored. This work investigated brain activity related to auditory frequency discrimination learning using a variational Bayesian approach for source localization, during simultaneous EEG and fMRI recording. We investigated whether the practice effects are determined solely by activity in stimulus-driven mechanisms or whether high-level attentional mechanisms, which are linked to the perceptual task, control the learning process. Results The results of fMRI analyses revealed significant attention and learning related activity in left and right superior temporal gyrus STG as well as the left inferior frontal gyrus IFG. Current source localization of simultaneously recorded EEG data was estimated using a variational Bayesian method. Analysis of current localized to the left inferior frontal gyrus and the right superior temporal gyrus revealed gamma band activity correlated with behavioral performance. Conclusions Rapid improvement in task performance is accompanied by plastic changes in the sensory cortex as well as superior areas gated by selective attention. Together the fMRI and EEG results suggest that gamma band activity in the right STG and left IFG plays an important role during perceptual learning. PMID:23316957

A generic EEG artifact removal algorithm based on the multi-channel Wiener filter

NASA Astrophysics Data System (ADS)

Somers, Ben; Francart, Tom; Bertrand, Alexander

2018-06-01

Objective. The electroencephalogram (EEG) is an essential neuro-monitoring tool for both clinical and research purposes, but is susceptible to a wide variety of undesired artifacts. Removal of these artifacts is often done using blind source separation techniques, relying on a purely data-driven transformation, which may sometimes fail to sufficiently isolate artifacts in only one or a few components. Furthermore, some algorithms perform well for specific artifacts, but not for others. In this paper, we aim to develop a generic EEG artifact removal algorithm, which allows the user to annotate a few artifact segments in the EEG recordings to inform the algorithm. Approach. We propose an algorithm based on the multi-channel Wiener filter (MWF), in which the artifact covariance matrix is replaced by a low-rank approximation based on the generalized eigenvalue decomposition. The algorithm is validated using both hybrid and real EEG data, and is compared to other algorithms frequently used for artifact removal. Main results. The MWF-based algorithm successfully removes a wide variety of artifacts with better performance than current state-of-the-art methods. Significance. Current EEG artifact removal techniques often have limited applicability due to their specificity to one kind of artifact, their complexity, or simply because they are too ‘blind’. This paper demonstrates a fast, robust and generic algorithm for removal of EEG artifacts of various types, i.e. those that were annotated as unwanted by the user.

A comparison of independent component analysis algorithms and measures to discriminate between EEG and artifact components.

PubMed

Dharmaprani, Dhani; Nguyen, Hoang K; Lewis, Trent W; DeLosAngeles, Dylan; Willoughby, John O; Pope, Kenneth J

2016-08-01

Independent Component Analysis (ICA) is a powerful statistical tool capable of separating multivariate scalp electrical signals into their additive independent or source components, specifically EEG or electroencephalogram and artifacts. Although ICA is a widely accepted EEG signal processing technique, classification of the recovered independent components (ICs) is still flawed, as current practice still requires subjective human decisions. Here we build on the results from Fitzgibbon et al. [1] to compare three measures and three ICA algorithms. Using EEG data acquired during neuromuscular paralysis, we tested the ability of the measures (spectral slope, peripherality and spatial smoothness) and algorithms (FastICA, Infomax and JADE) to identify components containing EMG. Spatial smoothness showed differentiation between paralysis and pre-paralysis ICs comparable to spectral slope, whereas peripherality showed less differentiation. A combination of the measures showed better differentiation than any measure alone. Furthermore, FastICA provided the best discrimination between muscle-free and muscle-contaminated recordings in the shortest time, suggesting it may be the most suited to EEG applications of the considered algorithms. Spatial smoothness results suggest that a significant number of ICs are mixed, i.e. contain signals from more than one biological source, and so the development of an ICA algorithm that is optimised to produce ICs that are easily classifiable is warranted.

EEG source imaging during two Qigong meditations.

PubMed

Faber, Pascal L; Lehmann, Dietrich; Tei, Shisei; Tsujiuchi, Takuya; Kumano, Hiroaki; Pascual-Marqui, Roberto D; Kochi, Kieko

2012-08-01

Experienced Qigong meditators who regularly perform the exercises "Thinking of Nothing" and "Qigong" were studied with multichannel EEG source imaging during their meditations. The intracerebral localization of brain electric activity during the two meditation conditions was compared using sLORETA functional EEG tomography. Differences between conditions were assessed using t statistics (corrected for multiple testing) on the normalized and log-transformed current density values of the sLORETA images. In the EEG alpha-2 frequency, 125 voxels differed significantly; all were more active during "Qigong" than "Thinking of Nothing," forming a single cluster in parietal Brodmann areas 5, 7, 31, and 40, all in the right hemisphere. In the EEG beta-1 frequency, 37 voxels differed significantly; all were more active during "Thinking of Nothing" than "Qigong," forming a single cluster in prefrontal Brodmann areas 6, 8, and 9, all in the left hemisphere. Compared to combined initial-final no-task resting, "Qigong" showed activation in posterior areas whereas "Thinking of Nothing" showed activation in anterior areas. The stronger activity of posterior (right) parietal areas during "Qigong" and anterior (left) prefrontal areas during "Thinking of Nothing" may reflect a predominance of self-reference, attention and input-centered processing in the "Qigong" meditation, and of control-centered processing in the "Thinking of Nothing" meditation.

EEG Oscillations Are Modulated in Different Behavior-Related Networks during Rhythmic Finger Movements.

PubMed

Seeber, Martin; Scherer, Reinhold; Müller-Putz, Gernot R

2016-11-16

Sequencing and timing of body movements are essential to perform motoric tasks. In this study, we investigate the temporal relation between cortical oscillations and human motor behavior (i.e., rhythmic finger movements). High-density EEG recordings were used for source imaging based on individual anatomy. We separated sustained and movement phase-related EEG source amplitudes based on the actual finger movements recorded by a data glove. Sustained amplitude modulations in the contralateral hand area show decrease for α (10-12 Hz) and β (18-24 Hz), but increase for high γ (60-80 Hz) frequencies during the entire movement period. Additionally, we found movement phase-related amplitudes, which resembled the flexion and extension sequence of the fingers. Especially for faster movement cadences, movement phase-related amplitudes included high β (24-30 Hz) frequencies in prefrontal areas. Interestingly, the spectral profiles and source patterns of movement phase-related amplitudes differed from sustained activities, suggesting that they represent different frequency-specific large-scale networks. First, networks were signified by the sustained element, which statically modulate their synchrony levels during continuous movements. These networks may upregulate neuronal excitability in brain regions specific to the limb, in this study the right hand area. Second, movement phase-related networks, which modulate their synchrony in relation to the movement sequence. We suggest that these frequency-specific networks are associated with distinct functions, including top-down control, sensorimotor prediction, and integration. The separation of different large-scale networks, we applied in this work, improves the interpretation of EEG sources in relation to human motor behavior. EEG recordings provide high temporal resolution suitable to relate cortical oscillations to actual movements. Investigating EEG sources during rhythmic finger movements, we distinguish sustained from movement phase-related amplitude modulations. We separate these two EEG source elements motivated by our previous findings in gait. Here, we found two types of large-scale networks, representing the right fingers in distinction from the time sequence of the movements. These findings suggest that EEG source amplitudes reconstructed in a cortical patch are the superposition of these simultaneously present network activities. Separating these frequency-specific networks is relevant for studying function and possible dysfunction of the cortical sensorimotor system in humans as well as to provide more advanced features for brain-computer interfaces. Copyright © 2016 the authors 0270-6474/16/3611671-11$15.00/0.

A comparison between EEG source localization and fMRI during the processing of emotional visual stimuli

NASA Astrophysics Data System (ADS)

Hu, Jin; Tian, Jie; Pan, Xiaohong; Liu, Jiangang

2007-03-01

The purpose of this paper is to compare between EEG source localization and fMRI during emotional processing. 108 pictures for EEG (categorized as positive, negative and neutral) and 72 pictures for fMRI were presented to 24 healthy, right-handed subjects. The fMRI data were analyzed using statistical parametric mapping with SPM2. LORETA was applied to grand averaged ERP data to localize intracranial sources. Statistical analysis was implemented to compare spatiotemporal activation of fMRI and EEG. The fMRI results are in accordance with EEG source localization to some extent, while part of mismatch in localization between the two methods was also observed. In the future we should apply the method for simultaneous recording of EEG and fMRI to our study.

Source analysis of alpha rhythm reactivity using LORETA imaging with 64-channel EEG and individual MRI.

PubMed

Cuspineda, E R; Machado, C; Virues, T; Martínez-Montes, E; Ojeda, A; Valdés, P A; Bosch, J; Valdes, L

2009-07-01

Conventional EEG and quantitative EEG visual stimuli (close-open eyes) reactivity analysis have shown their usefulness in clinical practice; however studies at the level of EEG generators are limited. The focus of the study was visual reactivity of cortical resources in healthy subjects and in a stroke patient. The 64 channel EEG and T1 magnetic resonance imaging (MRI) studies were obtained from 32 healthy subjects and a middle cerebral artery stroke patient. Low Resolution Electromagnetic Tomography (LORETA) was used to estimate EEG sources for both close eyes (CE) vs. open eyes (OE) conditions using individual MRI. The t-test was performed between source spectra of the two conditions. Thresholds for statistically significant t values were estimated by the local false discovery rate (lfdr) method. The Z transform was used to quantify the differences in cortical reactivity between the patient and healthy subjects. Closed-open eyes alpha reactivity sources were found mainly in posterior regions (occipito-parietal zones), extended in some cases to anterior and thalamic regions. Significant cortical reactivity sources were found in frequencies different from alpha (lower t-values). Significant changes at EEG reactivity sources were evident in the damaged brain hemisphere. Reactivity changes were also found in the "healthy" hemisphere when compared with the normal population. In conclusion, our study of brain sources of EEG alpha reactivity provides information that is not evident in the usual topographic analysis.

Temporo-insular enhancement of EEG low and high frequencies in patients with chronic tinnitus. QEEG study of chronic tinnitus patients

PubMed Central

2010-01-01

Background The physiopathological mechanism underlying the tinnitus phenomenon is still the subject of an ongoing debate. Since oscillatory EEG activity is increasingly recognized as a fundamental hallmark of cortical integrative functions, this study investigates deviations from the norm of different resting EEG parameters in patients suffering from chronic tinnitus. Results Spectral parameters of resting EEG of male tinnitus patients (n = 8, mean age 54 years) were compared to those of age-matched healthy males (n = 15, mean age 58.8 years). On average, the patient group exhibited higher spectral power over the frequency range of 2-100 Hz. Using LORETA source analysis, the generators of delta, theta, alpha and beta power increases were localized dominantly to left auditory (Brodmann Areas (BA) 41,42, 22), temporo-parietal, insular posterior, cingulate anterior and parahippocampal cortical areas. Conclusions Tinnitus patients show a deviation from the norm of different resting EEG parameters, characterized by an overproduction of resting state delta, theta and beta brain activities, providing further support for the microphysiological and magnetoencephalographic evidence pointing to a thalamocortical dysrhythmic process at the source of tinnitus. These results also provide further confirmation that reciprocal involvements of both auditory and associative/paralimbic areas are essential in the generation of tinnitus. PMID:20334674

MNE software for processing MEG and EEG data

PubMed Central

Gramfort, A.; Luessi, M.; Larson, E.; Engemann, D.; Strohmeier, D.; Brodbeck, C.; Parkkonen, L.; Hämäläinen, M.

2013-01-01

Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals originating from neural currents in the brain. Using these signals to characterize and locate brain activity is a challenging task, as evidenced by several decades of methodological contributions. MNE, whose name stems from its capability to compute cortically-constrained minimum-norm current estimates from M/EEG data, is a software package that provides comprehensive analysis tools and workflows including preprocessing, source estimation, time–frequency analysis, statistical analysis, and several methods to estimate functional connectivity between distributed brain regions. The present paper gives detailed information about the MNE package and describes typical use cases while also warning about potential caveats in analysis. The MNE package is a collaborative effort of multiple institutes striving to implement and share best methods and to facilitate distribution of analysis pipelines to advance reproducibility of research. Full documentation is available at http://martinos.org/mne. PMID:24161808

Open Ephys electroencephalography (Open Ephys  +  EEG): a modular, low-cost, open-source solution to human neural recording

NASA Astrophysics Data System (ADS)

Black, Christopher; Voigts, Jakob; Agrawal, Uday; Ladow, Max; Santoyo, Juan; Moore, Christopher; Jones, Stephanie

2017-06-01

Objective. Electroencephalography (EEG) offers a unique opportunity to study human neural activity non-invasively with millisecond resolution using minimal equipment in or outside of a lab setting. EEG can be combined with a number of techniques for closed-loop experiments, where external devices are driven by specific neural signals. However, reliable, commercially available EEG systems are expensive, often making them impractical for individual use and research development. Moreover, by design, a majority of these systems cannot be easily altered to the specification needed by the end user. We focused on mitigating these issues by implementing open-source tools to develop a new EEG platform to drive down research costs and promote collaboration and innovation. Approach. Here, we present methods to expand the open-source electrophysiology system, Open Ephys (www.openephys.org), to include human EEG recordings. We describe the equipment and protocol necessary to interface various EEG caps with the Open Ephys acquisition board, and detail methods for processing data. We present applications of Open Ephys  +  EEG as a research tool and discuss how this innovative EEG technology lays a framework for improved closed-loop paradigms and novel brain-computer interface experiments. Main results. The Open Ephys  +  EEG system can record reliable human EEG data, as well as human EMG data. A side-by-side comparison of eyes closed 8-14 Hz activity between the Open Ephys  +  EEG system and the Brainvision ActiCHamp EEG system showed similar average power and signal to noise. Significance. Open Ephys  +  EEG enables users to acquire high-quality human EEG data comparable to that of commercially available systems, while maintaining the price point and extensibility inherent to open-source systems.

Open Ephys electroencephalography (Open Ephys  +  EEG): a modular, low-cost, open-source solution to human neural recording.

PubMed

Black, Christopher; Voigts, Jakob; Agrawal, Uday; Ladow, Max; Santoyo, Juan; Moore, Christopher; Jones, Stephanie

2017-06-01

Electroencephalography (EEG) offers a unique opportunity to study human neural activity non-invasively with millisecond resolution using minimal equipment in or outside of a lab setting. EEG can be combined with a number of techniques for closed-loop experiments, where external devices are driven by specific neural signals. However, reliable, commercially available EEG systems are expensive, often making them impractical for individual use and research development. Moreover, by design, a majority of these systems cannot be easily altered to the specification needed by the end user. We focused on mitigating these issues by implementing open-source tools to develop a new EEG platform to drive down research costs and promote collaboration and innovation. Here, we present methods to expand the open-source electrophysiology system, Open Ephys (www.openephys.org), to include human EEG recordings. We describe the equipment and protocol necessary to interface various EEG caps with the Open Ephys acquisition board, and detail methods for processing data. We present applications of Open Ephys  +  EEG as a research tool and discuss how this innovative EEG technology lays a framework for improved closed-loop paradigms and novel brain-computer interface experiments. The Open Ephys  +  EEG system can record reliable human EEG data, as well as human EMG data. A side-by-side comparison of eyes closed 8-14 Hz activity between the Open Ephys  +  EEG system and the Brainvision ActiCHamp EEG system showed similar average power and signal to noise. Open Ephys  +  EEG enables users to acquire high-quality human EEG data comparable to that of commercially available systems, while maintaining the price point and extensibility inherent to open-source systems.

Detecting large-scale networks in the human brain using high-density electroencephalography.

PubMed

Liu, Quanying; Farahibozorg, Seyedehrezvan; Porcaro, Camillo; Wenderoth, Nicole; Mantini, Dante

2017-09-01

High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and exact low-resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp 38:4631-4643, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Source reconstruction via the spatiotemporal Kalman filter and LORETA from EEG time series with 32 or fewer electrodes.

PubMed

Hamid, Laith; Al Farawn, Ali; Merlet, Isabelle; Japaridze, Natia; Heute, Ulrich; Stephani, Ulrich; Galka, Andreas; Wendling, Fabrice; Siniatchkin, Michael

2017-07-01

The clinical routine of non-invasive electroencephalography (EEG) is usually performed with 8-40 electrodes, especially in long-term monitoring, infants or emergency care. There is a need in clinical and scientific brain imaging to develop inverse solution methods that can reconstruct brain sources from these low-density EEG recordings. In this proof-of-principle paper we investigate the performance of the spatiotemporal Kalman filter (STKF) in EEG source reconstruction with 9-, 19- and 32- electrodes. We used simulated EEG data of epileptic spikes generated from lateral frontal and lateral temporal brain sources using state-of-the-art neuronal population models. For validation of source reconstruction, we compared STKF results to the location of the simulated source and to the results of low-resolution brain electromagnetic tomography (LORETA) standard inverse solution. STKF consistently showed less localization bias compared to LORETA, especially when the number of electrodes was decreased. The results encourage further research into the application of the STKF in source reconstruction of brain activity from low-density EEG recordings.

Removal of EOG Artifacts from EEG Recordings Using Stationary Subspace Analysis

PubMed Central

Zeng, Hong; Song, Aiguo

2014-01-01

An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. The proposed approach first conducts the blind source separation on the raw EEG recording by the stationary subspace analysis (SSA) algorithm. Unlike the classic blind source separation algorithms, SSA is explicitly tailored to the understanding of distribution changes, where both the mean and the covariance matrix are taken into account. In addition, neither independency nor uncorrelation is required among the sources by SSA. Thereby, it can concentrate artifacts in fewer components than the representative blind source separation methods. Next, the components that are determined to be related to the ocular artifacts are projected back to be subtracted from EEG signals, producing the clean EEG data eventually. The experimental results on both the artificially contaminated EEG data and real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly nonstationary and the underlying sources cannot be assumed to be independent or uncorrelated. PMID:24550696

Estimation of Symptom Severity Scores for Patients with Schizophrenia Using ERP Source Activations during a Facial Affect Discrimination Task.

PubMed

Kim, Do-Won; Lee, Seung-Hwan; Shim, Miseon; Im, Chang-Hwan

2017-01-01

Precise diagnosis of psychiatric diseases and a comprehensive assessment of a patient's symptom severity are important in order to establish a successful treatment strategy for each patient. Although great efforts have been devoted to searching for diagnostic biomarkers of schizophrenia over the past several decades, no study has yet investigated how accurately these biomarkers are able to estimate an individual patient's symptom severity. In this study, we applied electrophysiological biomarkers obtained from electroencephalography (EEG) analyses to an estimation of symptom severity scores of patients with schizophrenia. EEG signals were recorded from 23 patients while they performed a facial affect discrimination task. Based on the source current density analysis results, we extracted voxels that showed a strong correlation between source activity and symptom scores. We then built a prediction model to estimate the symptom severity scores of each patient using the source activations of the selected voxels. The symptom scores of the Positive and Negative Syndrome Scale (PANSS) were estimated using the linear prediction model. The results of leave-one-out cross validation (LOOCV) showed that the mean errors of the estimated symptom scores were 3.34 ± 2.40 and 3.90 ± 3.01 for the Positive and Negative PANSS scores, respectively. The current pilot study is the first attempt to estimate symptom severity scores in schizophrenia using quantitative EEG features. It is expected that the present method can be extended to other cognitive paradigms or other psychological illnesses.

Inhibition of Lateral Prefrontal Cortex Produces Emotionally Biased First Impressions: A Transcranial Magnetic Stimulation and Electroencephalography Study.

PubMed

Lapate, Regina C; Samaha, Jason; Rokers, Bas; Hamzah, Hamdi; Postle, Bradley R; Davidson, Richard J

2017-07-01

Optimal functioning in everyday life requires the ability to override reflexive emotional responses and prevent affective spillover to situations or people unrelated to the source of emotion. In the current study, we investigated whether the lateral prefrontal cortex (lPFC) causally regulates the influence of emotional information on subsequent judgments. We disrupted left lPFC function using transcranial magnetic stimulation (TMS) and recorded electroencephalography (EEG) before and after. Subjects evaluated the likeability of novel neutral faces after a brief exposure to a happy or fearful face. We found that lPFC inhibition biased evaluations of novel faces according to the previously processed emotional expression. Greater frontal EEG alpha power, reflecting increased inhibition by TMS, predicted increased behavioral bias. TMS-induced affective misattribution was long-lasting: Emotionally biased first impressions formed during lPFC inhibition were still detectable outside of the laboratory 3 days later. These findings indicate that lPFC serves an important emotion-regulation function by preventing incidental emotional encoding from automatically biasing subsequent appraisals.

Magnetoencephalography signals are influenced by skull defects.

PubMed

Lau, S; Flemming, L; Haueisen, J

2014-08-01

Magnetoencephalography (MEG) signals had previously been hypothesized to have negligible sensitivity to skull defects. The objective is to experimentally investigate the influence of conducting skull defects on MEG and EEG signals. A miniaturized electric dipole was implanted in vivo into rabbit brains. Simultaneous recording using 64-channel EEG and 16-channel MEG was conducted, first above the intact skull and then above a skull defect. Skull defects were filled with agar gels, which had been formulated to have tissue-like homogeneous conductivities. The dipole was moved beneath the skull defects, and measurements were taken at regularly spaced points. The EEG signal amplitude increased 2-10 times, whereas the MEG signal amplitude reduced by as much as 20%. The EEG signal amplitude deviated more when the source was under the edge of the defect, whereas the MEG signal amplitude deviated more when the source was central under the defect. The change in MEG field-map topography (relative difference measure, RDM(∗)=0.15) was geometrically related to the skull defect edge. MEG and EEG signals can be substantially affected by skull defects. MEG source modeling requires realistic volume conductor head models that incorporate skull defects. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Neural basis of postural instability identified by VTC and EEG

PubMed Central

Cao, Cheng; Jaiswal, Niharika; Newell, Karl M.

2010-01-01

In this study, we investigated the neural basis of virtual time to contact (VTC) and the hypothesis that VTC provides predictive information for future postural instability. A novel approach to differentiate stable pre-falling and transition-to-instability stages within a single postural trial while a subject was performing a challenging single leg stance with eyes closed was developed. Specifically, we utilized wavelet transform and stage segmentation algorithms using VTC time series data set as an input. The VTC time series was time-locked with multichannel (n = 64) EEG signals to examine its underlying neural substrates. To identify the focal sources of neural substrates of VTC, a two-step approach was designed combining the independent component analysis (ICA) and low-resolution tomography (LORETA) of multichannel EEG. There were two major findings: (1) a significant increase of VTC minimal values (along with enhanced variability of VTC) was observed during the transition-to-instability stage with progression to ultimate loss of balance and falling; and (2) this VTC dynamics was associated with pronounced modulation of EEG predominantly within theta, alpha and gamma frequency bands. The sources of this EEG modulation were identified at the cingulate cortex (ACC) and the junction of precuneus and parietal lobe, as well as at the occipital cortex. The findings support the hypothesis that the systematic increase of minimal values of VTC concomitant with modulation of EEG signals at the frontal-central and parietal–occipital areas serve collectively to predict the future instability in posture. PMID:19655130

Localization Accuracy of Distributed Inverse Solutions for Electric and Magnetic Source Imaging of Interictal Epileptic Discharges in Patients with Focal Epilepsy.

PubMed

Heers, Marcel; Chowdhury, Rasheda A; Hedrich, Tanguy; Dubeau, François; Hall, Jeffery A; Lina, Jean-Marc; Grova, Christophe; Kobayashi, Eliane

2016-01-01

Distributed inverse solutions aim to realistically reconstruct the origin of interictal epileptic discharges (IEDs) from noninvasively recorded electroencephalography (EEG) and magnetoencephalography (MEG) signals. Our aim was to compare the performance of different distributed inverse solutions in localizing IEDs: coherent maximum entropy on the mean (cMEM), hierarchical Bayesian implementations of independent identically distributed sources (IID, minimum norm prior) and spatially coherent sources (COH, spatial smoothness prior). Source maxima (i.e., the vertex with the maximum source amplitude) of IEDs in 14 EEG and 19 MEG studies from 15 patients with focal epilepsy were analyzed. We visually compared their concordance with intracranial EEG (iEEG) based on 17 cortical regions of interest and their spatial dispersion around source maxima. Magnetic source imaging (MSI) maxima from cMEM were most often confirmed by iEEG (cMEM: 14/19, COH: 9/19, IID: 8/19 studies). COH electric source imaging (ESI) maxima co-localized best with iEEG (cMEM: 8/14, COH: 11/14, IID: 10/14 studies). In addition, cMEM was less spatially spread than COH and IID for ESI and MSI (p < 0.001 Bonferroni-corrected post hoc t test). Highest positive predictive values for cortical regions with IEDs in iEEG could be obtained with cMEM for MSI and with COH for ESI. Additional realistic EEG/MEG simulations confirmed our findings. Accurate spatially extended sources, as found in cMEM (ESI and MSI) and COH (ESI) are desirable for source imaging of IEDs because this might influence surgical decision. Our simulations suggest that COH and IID overestimate the spatial extent of the generators compared to cMEM.

Regional brain activity that determines successful and unsuccessful working memory formation.

PubMed

Teramoto, Shohei; Inaoka, Tsubasa; Ono, Yumie

2016-08-01

Using EEG source reconstruction with Multiple Sparse Priors (MSP), we investigated the regional brain activity that determines successful memory encoding in two participant groups of high and low accuracy rates. Eighteen healthy young adults performed a sequential fashion of visual Sternberg memory task. The 32-channel EEG was continuously measured during participants performed two 70 trials of memory task. The regional brain activity corresponding to the oscillatory EEG activity in the alpha band (8-13 Hz) during encoding period was analyzed by MSP implemented in SPM8. We divided the data of all participants into 2 groups (low- and highperformance group) and analyzed differences in regional brain activity between trials in which participants answered correctly and incorrectly within each of the group. Participants in low-performance group showed significant activity increase in the visual cortices in their successful trials compared to unsuccessful ones. On the other hand, those in high-performance group showed a significant activity increase in widely distributed cortical regions in the frontal, temporal, and parietal areas including those suggested as Baddeley's working memory model. Further comparison of activated cortical volumes and mean current source intensities within the cortical regions of Baddeley's model during memory encoding demonstrated that participants in high-performance group showed enhanced activity in the right premotor cortex, which plays an important role in maintaining visuospatial attention, compared to those in low performance group. Our results suggest that better ability in memory encoding is associated with distributed and stronger regional brain activities including the premotor cortex, possibly indicating efficient allocation of cognitive load and maintenance of attention.

A Wearable EEG-HEG-HRV Multimodal System With Simultaneous Monitoring of tES for Mental Health Management.

PubMed

Ha, Unsoo; Lee, Yongsu; Kim, Hyunki; Roh, Taehwan; Bae, Joonsung; Kim, Changhyeon; Yoo, Hoi-Jun

2015-12-01

A multimodal mental management system in the shape of the wearable headband and earplugs is proposed to monitor electroencephalography (EEG), hemoencephalography (HEG) and heart rate variability (HRV) for accurate mental health monitoring. It enables simultaneous transcranial electrical stimulation (tES) together with real-time monitoring. The total weight of the proposed system is less than 200 g. The multi-loop low-noise amplifier (MLLNA) achieves over 130 dB CMRR for EEG sensing and the capacitive correlated-double sampling transimpedance amplifier (CCTIA) has low-noise characteristics for HEG and HRV sensing. Measured three-physiology domains such as neural, vascular and autonomic domain signals are combined with canonical correlation analysis (CCA) and temporal kernel canonical correlation analysis (tkCCA) algorithm to find the neural-vascular-autonomic coupling. It supports highly accurate classification with the 19% maximum improvement with multimodal monitoring. For the multi-channel stimulation functionality, after-effects maximization monitoring and sympathetic nerve disorder monitoring, the stimulator is designed as reconfigurable. The 3.37 × 2.25 mm(2) chip has 2-channel EEG sensor front-end, 2-channel NIRS sensor front-end, NIRS current driver to drive dual-wavelength VCSEL and 6-b DAC current source for tES mode. It dissipates 24 mW with 2 mA stimulation current and 5 mA NIRS driver current.

Source-Modeling Auditory Processes of EEG Data Using EEGLAB and Brainstorm.

PubMed

Stropahl, Maren; Bauer, Anna-Katharina R; Debener, Stefan; Bleichner, Martin G

2018-01-01

Electroencephalography (EEG) source localization approaches are often used to disentangle the spatial patterns mixed up in scalp EEG recordings. However, approaches differ substantially between experiments, may be strongly parameter-dependent, and results are not necessarily meaningful. In this paper we provide a pipeline for EEG source estimation, from raw EEG data pre-processing using EEGLAB functions up to source-level analysis as implemented in Brainstorm. The pipeline is tested using a data set of 10 individuals performing an auditory attention task. The analysis approach estimates sources of 64-channel EEG data without the prerequisite of individual anatomies or individually digitized sensor positions. First, we show advanced EEG pre-processing using EEGLAB, which includes artifact attenuation using independent component analysis (ICA). ICA is a linear decomposition technique that aims to reveal the underlying statistical sources of mixed signals and is further a powerful tool to attenuate stereotypical artifacts (e.g., eye movements or heartbeat). Data submitted to ICA are pre-processed to facilitate good-quality decompositions. Aiming toward an objective approach on component identification, the semi-automatic CORRMAP algorithm is applied for the identification of components representing prominent and stereotypic artifacts. Second, we present a step-wise approach to estimate active sources of auditory cortex event-related processing, on a single subject level. The presented approach assumes that no individual anatomy is available and therefore the default anatomy ICBM152, as implemented in Brainstorm, is used for all individuals. Individual noise modeling in this dataset is based on the pre-stimulus baseline period. For EEG source modeling we use the OpenMEEG algorithm as the underlying forward model based on the symmetric Boundary Element Method (BEM). We then apply the method of dynamical statistical parametric mapping (dSPM) to obtain physiologically plausible EEG source estimates. Finally, we show how to perform group level analysis in the time domain on anatomically defined regions of interest (auditory scout). The proposed pipeline needs to be tailored to the specific datasets and paradigms. However, the straightforward combination of EEGLAB and Brainstorm analysis tools may be of interest to others performing EEG source localization.

Photogrammetry-Based Head Digitization for Rapid and Accurate Localization of EEG Electrodes and MEG Fiducial Markers Using a Single Digital SLR Camera.

PubMed

Clausner, Tommy; Dalal, Sarang S; Crespo-García, Maité

2017-01-01

The performance of EEG source reconstruction has benefited from the increasing use of advanced head modeling techniques that take advantage of MRI together with the precise positions of the recording electrodes. The prevailing technique for registering EEG electrode coordinates involves electromagnetic digitization. However, the procedure adds several minutes to experiment preparation and typical digitizers may not be accurate enough for optimal source reconstruction performance (Dalal et al., 2014). Here, we present a rapid, accurate, and cost-effective alternative method to register EEG electrode positions, using a single digital SLR camera, photogrammetry software, and computer vision techniques implemented in our open-source toolbox, janus3D . Our approach uses photogrammetry to construct 3D models from multiple photographs of the participant's head wearing the EEG electrode cap. Electrodes are detected automatically or semi-automatically using a template. The rigid facial features from these photo-based models are then surface-matched to MRI-based head reconstructions to facilitate coregistration to MRI space. This method yields a final electrode coregistration error of 0.8 mm, while a standard technique using an electromagnetic digitizer yielded an error of 6.1 mm. The technique furthermore reduces preparation time, and could be extended to a multi-camera array, which would make the procedure virtually instantaneous. In addition to EEG, the technique could likewise capture the position of the fiducial markers used in magnetoencephalography systems to register head position.

Photogrammetry-Based Head Digitization for Rapid and Accurate Localization of EEG Electrodes and MEG Fiducial Markers Using a Single Digital SLR Camera

PubMed Central

Clausner, Tommy; Dalal, Sarang S.; Crespo-García, Maité

2017-01-01

The performance of EEG source reconstruction has benefited from the increasing use of advanced head modeling techniques that take advantage of MRI together with the precise positions of the recording electrodes. The prevailing technique for registering EEG electrode coordinates involves electromagnetic digitization. However, the procedure adds several minutes to experiment preparation and typical digitizers may not be accurate enough for optimal source reconstruction performance (Dalal et al., 2014). Here, we present a rapid, accurate, and cost-effective alternative method to register EEG electrode positions, using a single digital SLR camera, photogrammetry software, and computer vision techniques implemented in our open-source toolbox, janus3D. Our approach uses photogrammetry to construct 3D models from multiple photographs of the participant's head wearing the EEG electrode cap. Electrodes are detected automatically or semi-automatically using a template. The rigid facial features from these photo-based models are then surface-matched to MRI-based head reconstructions to facilitate coregistration to MRI space. This method yields a final electrode coregistration error of 0.8 mm, while a standard technique using an electromagnetic digitizer yielded an error of 6.1 mm. The technique furthermore reduces preparation time, and could be extended to a multi-camera array, which would make the procedure virtually instantaneous. In addition to EEG, the technique could likewise capture the position of the fiducial markers used in magnetoencephalography systems to register head position. PMID:28559791

EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

NASA Astrophysics Data System (ADS)

Shenoy Handiru, Vikram; Vinod, A. P.; Guan, Cuntai

2017-08-01

Objective. In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. Approach. We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method. Main Results. Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction. Significance. This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.

Statistical parametric mapping of LORETA using high density EEG and individual MRI: application to mismatch negativities in schizophrenia.

PubMed

Park, Hae-Jeong; Kwon, Jun Soo; Youn, Tak; Pae, Ji Soo; Kim, Jae-Jin; Kim, Myung-Sun; Ha, Kyoo-Seob

2002-11-01

We describe a method for the statistical parametric mapping of low resolution electromagnetic tomography (LORETA) using high-density electroencephalography (EEG) and individual magnetic resonance images (MRI) to investigate the characteristics of the mismatch negativity (MMN) generators in schizophrenia. LORETA, using a realistic head model of the boundary element method derived from the individual anatomy, estimated the current density maps from the scalp topography of the 128-channel EEG. From the current density maps that covered the whole cortical gray matter (up to 20,000 points), volumetric current density images were reconstructed. Intensity normalization of the smoothed current density images was used to reduce the confounding effect of subject specific global activity. After transforming each image into a standard stereotaxic space, we carried out statistical parametric mapping of the normalized current density images. We applied this method to the source localization of MMN in schizophrenia. The MMN generators, produced by a deviant tone of 1,200 Hz (5% of 1,600 trials) under the standard tone of 1,000 Hz, 80 dB binaural stimuli with 300 msec of inter-stimulus interval, were measured in 14 right-handed schizophrenic subjects and 14 age-, gender-, and handedness-matched controls. We found that the schizophrenic group exhibited significant current density reductions of MMN in the left superior temporal gyrus and the left inferior parietal gyrus (P < 0. 0005). This study is the first voxel-by-voxel statistical mapping of current density using individual MRI and high-density EEG. Copyright 2002 Wiley-Liss, Inc.

EEG-LORETA endophenotypes of the common idiopathic generalized epilepsy syndromes.

PubMed

Clemens, B; Puskás, S; Besenyei, M; Emri, M; Opposits, G; Kis, S A; Hollódy, K; Fogarasi, A; Kondákor, I; Füle, K; Bense, K; Fekete, I

2012-05-01

We tested the hypothesis that the cortical areas with abnormal local EEG synchronization are dissimilar in the three common idiopathic generalized epilepsy (IGE) phenotypes: IGE patients with absence seizures (ABS), juvenile myoclonic epilepsy (JME) and epilepsy with generalized tonic-clonic seizures exclusively (EGTCS). Groups of unmedicated ABS, JME and EGTCS patients were investigated. Waking EEG background activity (without any epileptiform potentials) was analyzed by a source localization method, LORETA (Low Resolution Electromagnetic Tomography). Each patient group was compared to a separate, age-matched group of healthy control persons. Voxel-based, normalized broad-band (delta, theta, alpha, and beta) and very narrow band (VNB, 1Hz bandwidth, from 1 to 25Hz) LORETA activity (=current source density, A/m(2)) were computed for each person. Group comparison included subtraction (average patient data minus average control data) and group statistics (multiple t-tests, where Bonferroni-corrected p<0.05 values were accepted as statistically significant). Statistically not significant main findings were: overall increased delta and theta broad band activity in the ABS and JME groups; decrease of alpha and beta activity in the EGTCS group. Statistically significant main findings were as follows. JME group: bilaterally increased theta activity in posterior (temporal, parietal, and occipital) cortical areas; bilaterally increased activity in the medial and basal prefrontal area in the 8Hz VNB; bilaterally decreased activity in the precuneus, posterior cingulate and superior parietal lobule in the 11Hz and 21-22Hz VNBs. ABS group: bilaterally increased theta activity emerged in the basal prefrontal and medial temporal limbic areas. Decreased activity was found at 19-21Hz in the right postcentral gyrus and parts of the right superior and medial temporal gyri. EGTCS group: decreased activity was found in the frontal cortex and the postcentral gyrus at 10-11Hz, increased activity in the right parahippocampal gyrus at 16-18Hz. Increased theta activity in the posterior parts of the cortex is the endophenotype for JME. Increased theta activity in the fronto-temporal limbic areas is the endophenotype for ABS. Statistically not significant findings might indicate diffuse biochemical abnormality of the cortex in JME and ABS. EEG-LORETA endophenotypes may correspond to the selective propensity to generate absence and myoclonic seizures in the ABS and JME syndromes. Copyright © 2011 Elsevier B.V. All rights reserved.

Surface EEG-Transcranial Direct Current Stimulation (tDCS) Closed-Loop System.

PubMed

Leite, Jorge; Morales-Quezada, Leon; Carvalho, Sandra; Thibaut, Aurore; Doruk, Deniz; Chen, Chiun-Fan; Schachter, Steven C; Rotenberg, Alexander; Fregni, Felipe

2017-09-01

Conventional transcranial direct current stimulation (tDCS) protocols rely on applying electrical current at a fixed intensity and duration without using surrogate markers to direct the interventions. This has led to some mixed results; especially because tDCS induced effects may vary depending on the ongoing level of brain activity. Therefore, the objective of this preliminary study was to assess the feasibility of an EEG-triggered tDCS system based on EEG online analysis of its frequency bands. Six healthy volunteers were randomized to participate in a double-blind sham-controlled crossover design to receive a single session of 10[Formula: see text]min 2[Formula: see text]mA cathodal and sham tDCS. tDCS trigger controller was based upon an algorithm designed to detect an increase in the relative beta power of more than 200%, accompanied by a decrease of 50% or more in the relative alpha power, based on baseline EEG recordings. EEG-tDCS closed-loop-system was able to detect the predefined EEG magnitude deviation and successfully triggered the stimulation in all participants. This preliminary study represents a proof-of-concept for the development of an EEG-tDCS closed-loop system in humans. We discuss and review here different methods of closed loop system that can be considered and potential clinical applications of such system.

Transcranial direct current stimulation and power spectral parameters: a tDCS/EEG co-registration study

PubMed Central

Mangia, Anna L.; Pirini, Marco; Cappello, Angelo

2014-01-01

Transcranial direct current stimulation (tDCS) delivers low electric currents to the brain through the scalp. Constant electric currents induce shifts in neuronal membrane excitability, resulting in secondary changes in cortical activity. Concomitant electroencephalography (EEG) monitoring during tDCS can provide valuable information on the tDCS mechanisms of action. This study examined the effects of anodal tDCS on spontaneous cortical activity in a resting brain to disclose possible modulation of spontaneous oscillatory brain activity. EEG activity was measured in ten healthy subjects during and after a session of anodal stimulation of the postero-parietal cortex to detect the tDCS-induced alterations. Changes in the theta, alpha, beta, and gamma power bands were investigated. Three main findings emerged: (1) an increase in theta band activity during the first minutes of stimulation; (2) an increase in alpha and beta power during and after stimulation; (3) a widespread activation in several brain regions. PMID:25147519

Investigating social cognition in infants and adults using dense array electroencephalography ((d)EEG).

PubMed

Akano, Adekemi J; Haley, David W; Dudek, Joanna

2011-06-27

Dense array electroencephalography ((d)EEG), which provides a non-invasive window for measuring brain activity and a temporal resolution unsurpassed by any other current brain imaging technology¹, ² is being used increasingly in the study of social cognitive functioning in infants and adults. While (d)EEG is enabling researchers to examine brain activity patterns with unprecedented levels of sensitivity, conventional EEG recording systems continue to face certain limitations, including 1) poor spatial resolution and source localization³,⁴2) the physical discomfort for test subjects of enduring the individual application of numerous electrodes to the surface of the scalp, and 3) the complexity for researchers of learning to use multiple software packages to collect and process data. Here we present an overview of an established methodology that represents a significant improvement on conventional methodologies for studying EEG in infants and adults. Although several analytical software techniques can be used to establish indirect indices of source localization to improve the spatial resolution of (d)EEG, the HydroCel Geodesic Sensor Net (HCGSN) by Electrical Geodesics, Inc. (EGI), a dense sensory array that maintains equal distances among adjacent recording electrodes on all surfaces of the scalp, further enhances spatial resolution⁴,⁵(,)⁶ compared to standard (d)EEG systems. The sponge-based HCGSN can be applied rapidly and without scalp abrasion, making it ideal for use with adults⁷,⁸ children⁹,¹⁰, ¹¹,¹² and infants¹², in both research and clinical ⁴,⁵,⁶,¹³,¹⁴,¹⁵settings. This feature allows for considerable cost and time savings by decreasing the average net application time compared to other (d)EEG systems. Moreover, the HCGSN includes unified, seamless software applications for all phases of data, greatly simplifying the collection, processing, and analysis of (d)EEG data. The HCGSN features a low-profile electrode pedestal, which, when filled with electrolyte solution, creates a sealed microenvironment and an electrode-scalp interface. In all Geodesic (d;)EEG systems, EEG sensors detect changes in voltage originating from the participant's scalp, along with a small amount of electrical noise originating from the room environment. Electrical signals from all sensors of the Geodesic sensor net are received simultaneously by the amplifier, where they are automatically processed, packaged, and sent to the data-acquisition computer (DAC). Once received by the DAC, scalp electrical activity can be isolated from artifacts for analysis using the filtering and artifact detection tools included in the EGI software. Typically, the HCGSN can be used continuously for only up to two hours because the electrolyte solution dries out over time, gradually decreasing the quality of the scalp-electrode interface. In the Parent-Infant Research Lab at the University of Toronto, we are using (d)EEG to study social cognitive processes including memory, emotion, goals, intentionality, anticipation, and executive functioning in both adult and infant participants.

Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements.

PubMed

Muthuraman, Muthuraman; Hellriegel, Helge; Hoogenboom, Nienke; Anwar, Abdul Rauf; Mideksa, Kidist Gebremariam; Krause, Holger; Schnitzler, Alfons; Deuschl, Günther; Raethjen, Jan

2014-01-01

Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2-4 Hz) and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS) with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC). MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR) level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT) task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG.

Beamformer Source Analysis and Connectivity on Concurrent EEG and MEG Data during Voluntary Movements

PubMed Central

Muthuraman, Muthuraman; Hellriegel, Helge; Hoogenboom, Nienke; Anwar, Abdul Rauf; Mideksa, Kidist Gebremariam; Krause, Holger; Schnitzler, Alfons; Deuschl, Günther; Raethjen, Jan

2014-01-01

Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2–4 Hz) and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS) with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC). MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR) level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT) task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG. PMID:24618596

Artifact removal from EEG signals using adaptive filters in cascade

NASA Astrophysics Data System (ADS)

Garcés Correa, A.; Laciar, E.; Patiño, H. D.; Valentinuzzi, M. E.

2007-11-01

Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records.

A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal.

PubMed

Oosugi, Naoya; Kitajo, Keiichi; Hasegawa, Naomi; Nagasaka, Yasuo; Okanoya, Kazuo; Fujii, Naotaka

2017-09-01

Blind source separation (BSS) algorithms extract neural signals from electroencephalography (EEG) data. However, it is difficult to quantify source separation performance because there is no criterion to dissociate neural signals and noise in EEG signals. This study develops a method for evaluating BSS performance. The idea is neural signals in EEG can be estimated by comparison with simultaneously measured electrocorticography (ECoG). Because the ECoG electrodes cover the majority of the lateral cortical surface and should capture most of the original neural sources in the EEG signals. We measured real EEG and ECoG data and developed an algorithm for evaluating BSS performance. First, EEG signals are separated into EEG components using the BSS algorithm. Second, the EEG components are ranked using the correlation coefficients of the ECoG regression and the components are grouped into subsets based on their ranks. Third, canonical correlation analysis estimates how much information is shared between the subsets of the EEG components and the ECoG signals. We used our algorithm to compare the performance of BSS algorithms (PCA, AMUSE, SOBI, JADE, fastICA) via the EEG and ECoG data of anesthetized nonhuman primates. The results (Best case >JADE = fastICA >AMUSE = SOBI ≥ PCA >random separation) were common to the two subjects. To encourage the further development of better BSS algorithms, our EEG and ECoG data are available on our Web site (http://neurotycho.org/) as a common testing platform. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

[Digital electroencephalography in brain death diagnostics : Technical requirements and results of a survey on the compatibility with medical guidelines of digital EEG systems from providers in Germany].

PubMed

Walter, U; Noachtar, S; Hinrichs, H

2018-02-01

The guidelines of the German Medical Association and the German Society for Clinical Neurophysiology and Functional Imaging (DGKN) require a high procedural and technical standard for electroencephalography (EEG) as an ancillary method for diagnosing the irreversible cessation of brain function (brain death). Nowadays, digital EEG systems are increasingly being applied in hospitals. So far it is unclear to what extent the digital EEG systems currently marketed in Germany meet the guidelines for diagnosing brain death. In the present article, the technical und safety-related requirements for digital EEG systems and the EEG documentation for diagnosing brain death are described in detail. On behalf of the DGKN, the authors sent out a questionnaire to all identified distributors of digital EEG systems in Germany with respect to the following technical demands: repeated recording of the calibration signals during an ongoing EEG recording, repeated recording of all electrode impedances during an ongoing EEG recording, assessability of intrasystem noise and galvanic isolation of measurement earthing from earthing conductor (floating input). For 15 of the identified 20 different digital EEG systems the specifications were provided by the distributors (among them all distributors based in Germany). All of these EEG systems are provided with a galvanic isolation (floating input). The internal noise can be tested with all systems; however, some systems do not allow repeated recording of the calibration signals and/or the electrode impedances during an ongoing EEG recording. The majority but not all of the currently available digital EEG systems offered for clinical use are eligible for use in brain death diagnostics as per German guidelines.

Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy - A comparison with scalp EEG monitoring.

PubMed

Zibrandtsen, I C; Kidmose, P; Christensen, C B; Kjaer, T W

2017-12-01

Ear-EEG is recording of electroencephalography from a small device in the ear. This is the first study to compare ictal and interictal abnormalities recorded with ear-EEG and simultaneous scalp-EEG in an epilepsy monitoring unit. We recorded and compared simultaneous ear-EEG and scalp-EEG from 15 patients with suspected temporal lobe epilepsy. EEGs were compared visually by independent neurophysiologists. Correlation and time-frequency analysis was used to quantify the similarity between ear and scalp electrodes. Spike-averages were used to assess similarity of interictal spikes. There were no differences in sensitivity or specificity for seizure detection. Mean correlation coefficient between ear-EEG and nearest scalp electrode was above 0.6 with a statistically significant decreasing trend with increasing distance away from the ear. Ictal morphology and frequency dynamics can be observed from visual inspection and time-frequency analysis. Spike averages derived from ear-EEG electrodes yield a recognizable spike appearance. Our results suggest that ear-EEG can reliably detect electroencephalographic patterns associated with focal temporal lobe seizures. Interictal spike morphology from sufficiently large temporal spike sources can be sampled using ear-EEG. Ear-EEG is likely to become an important tool in clinical epilepsy monitoring and diagnosis. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Generator localization by current source density (CSD): Implications of volume conduction and field closure at intracranial and scalp resolutions

PubMed Central

Tenke, Craig E.; Kayser, Jürgen

2012-01-01

The topographic ambiguity and reference-dependency that has plagued EEG/ERP research throughout its history are largely attributable to volume conduction, which may be concisely described by a vector form of Ohm’s Law. This biophysical relationship is common to popular algorithms that infer neuronal generators via inverse solutions. It may be further simplified as Poisson’s source equation, which identifies underlying current generators from estimates of the second spatial derivative of the field potential (Laplacian transformation). Intracranial current source density (CSD) studies have dissected the “cortical dipole” into intracortical sources and sinks, corresponding to physiologically-meaningful patterns of neuronal activity at a sublaminar resolution, much of which is locally cancelled (i.e., closed field). By virtue of the macroscopic scale of the scalp-recorded EEG, a surface Laplacian reflects the radial projections of these underlying currents, representing a unique, unambiguous measure of neuronal activity at scalp. Although the surface Laplacian requires minimal assumptions compared to complex, model-sensitive inverses, the resulting waveform topographies faithfully summarize and simplify essential constraints that must be placed on putative generators of a scalp potential topography, even if they arise from deep or partially-closed fields. CSD methods thereby provide a global empirical and biophysical context for generator localization, spanning scales from intracortical to scalp recordings. PMID:22796039

Source localization of small sharp spikes: low resolution electromagnetic tomography (LORETA) reveals two distinct cortical sources.

PubMed

Zumsteg, Dominik; Andrade, Danielle M; Wennberg, Richard A

2006-06-01

We have investigated the cortical sources and electroencephalographic (EEG) characteristics of small sharp spikes (SSS) by using statistical non-parametric mapping (SNPM) of low resolution electromagnetic tomography (LORETA). We analyzed 7 SSS patterns (501 individual SSS) in 6 patients who underwent sleep EEG studies with 29 or 23 scalp electrodes. The scalp signals were averaged time-locked to the SSS peak activity and subjected to SNPM of LORETA values. All 7 SSS patterns (mean 72 individual SSS, range 11-200) revealed a very similar and highly characteristic transhemispheric oblique scalp voltage distribution comprising a first negative field maximum over ipsilateral lateral temporal areas, followed by a second negative field maximum over the contralateral subtemporal region approximately 30 ms later. SNPM-LORETA consistently localized the first component into the ipsilateral posterior insular region, and the second component into ipsilateral posterior mesial temporo-occipital structures. SSS comprise an amalgam of two sequential, distinct cortical components, showing a very uniform and peculiar EEG pattern and cortical source solutions. As such, they must be clearly distinguished from interictal epileptiform discharges in patients with epilepsy. The awareness of these peculiar EEG characteristics may increase our ability to differentiate SSS from interictal epileptiform activity. The finding of a posterior insular source might serve as an inspiration for new physiological considerations regarding these enigmatic waveforms.

Reversing pathologically increased EEG power by acoustic coordinated reset neuromodulation

PubMed Central

Adamchic, Ilya; Toth, Timea; Hauptmann, Christian; Tass, Peter Alexander

2014-01-01

Acoustic Coordinated Reset (CR) neuromodulation is a patterned stimulation with tones adjusted to the patient's dominant tinnitus frequency, which aims at desynchronizing pathological neuronal synchronization. In a recent proof-of-concept study, CR therapy, delivered 4–6 h/day more than 12 weeks, induced a significant clinical improvement along with a significant long-lasting decrease of pathological oscillatory power in the low frequency as well as γ band and an increase of the α power in a network of tinnitus-related brain areas. As yet, it remains unclear whether CR shifts the brain activity toward physiological levels or whether it induces clinically beneficial, but nonetheless abnormal electroencephalographic (EEG) patterns, for example excessively decreased δ and/or γ. Here, we compared the patients' spontaneous EEG data at baseline as well as after 12 weeks of CR therapy with the spontaneous EEG of healthy controls by means of Brain Electrical Source Analysis source montage and standardized low-resolution brain electromagnetic tomography techniques. The relationship between changes in EEG power and clinical scores was investigated using a partial least squares approach. In this way, we show that acoustic CR neuromodulation leads to a normalization of the oscillatory power in the tinnitus-related network of brain areas, most prominently in temporal regions. A positive association was found between the changes in tinnitus severity and the normalization of δ and γ power in the temporal, parietal, and cingulate cortical regions. Our findings demonstrate a widespread CR-induced normalization of EEG power, significantly associated with a reduction of tinnitus severity. PMID:23907785

Combined EEG/MEG Can Outperform Single Modality EEG or MEG Source Reconstruction in Presurgical Epilepsy Diagnosis

PubMed Central

Aydin, Ümit; Vorwerk, Johannes; Dümpelmann, Matthias; Küpper, Philipp; Kugel, Harald; Heers, Marcel; Wellmer, Jörg; Kellinghaus, Christoph; Haueisen, Jens; Rampp, Stefan; Stefan, Hermann; Wolters, Carsten H.

2015-01-01

We investigated two important means for improving source reconstruction in presurgical epilepsy diagnosis. The first investigation is about the optimal choice of the number of epileptic spikes in averaging to (1) sufficiently reduce the noise bias for an accurate determination of the center of gravity of the epileptic activity and (2) still get an estimation of the extent of the irritative zone. The second study focuses on the differences in single modality EEG (80-electrodes) or MEG (275-gradiometers) and especially on the benefits of combined EEG/MEG (EMEG) source analysis. Both investigations were validated with simultaneous stereo-EEG (sEEG) (167-contacts) and low-density EEG (ldEEG) (21-electrodes). To account for the different sensitivity profiles of EEG and MEG, we constructed a six-compartment finite element head model with anisotropic white matter conductivity, and calibrated the skull conductivity via somatosensory evoked responses. Our results show that, unlike single modality EEG or MEG, combined EMEG uses the complementary information of both modalities and thereby allows accurate source reconstructions also at early instants in time (epileptic spike onset), i.e., time points with low SNR, which are not yet subject to propagation and thus supposed to be closer to the origin of the epileptic activity. EMEG is furthermore able to reveal the propagation pathway at later time points in agreement with sEEG, while EEG or MEG alone reconstructed only parts of it. Subaveraging provides important and accurate information about both the center of gravity and the extent of the epileptogenic tissue that neither single nor grand-averaged spike localizations can supply. PMID:25761059

Reduced integration and improved segregation of functional brain networks in Alzheimer’s disease

NASA Astrophysics Data System (ADS)

Kabbara, A.; Eid, H.; El Falou, W.; Khalil, M.; Wendling, F.; Hassan, M.

2018-04-01

Objective. Emerging evidence shows that cognitive deficits in Alzheimer’s disease (AD) are associated with disruptions in brain functional connectivity. Thus, the identification of alterations in AD functional networks has become a topic of increasing interest. However, to what extent AD induces disruption of the balance of local and global information processing in the human brain remains elusive. The main objective of this study is to explore the dynamic topological changes of AD networks in terms of brain network segregation and integration. Approach. We used electroencephalography (EEG) data recorded from 20 participants (10 AD patients and 10 healthy controls) during resting state. Functional brain networks were reconstructed using EEG source connectivity computed in different frequency bands. Graph theoretical analyses were performed assess differences between both groups. Main results. Results revealed that AD networks, compared to networks of age-matched healthy controls, are characterized by lower global information processing (integration) and higher local information processing (segregation). Results showed also significant correlation between the alterations in the AD patients’ functional brain networks and their cognitive scores. Significance. These findings may contribute to the development of EEG network-based test that could strengthen results obtained from currently-used neurophysiological tests in neurodegenerative diseases.

Reduced integration and improved segregation of functional brain networks in Alzheimer's disease.

PubMed

Kabbara, A; Eid, H; El Falou, W; Khalil, M; Wendling, F; Hassan, M

2018-04-01

Emerging evidence shows that cognitive deficits in Alzheimer's disease (AD) are associated with disruptions in brain functional connectivity. Thus, the identification of alterations in AD functional networks has become a topic of increasing interest. However, to what extent AD induces disruption of the balance of local and global information processing in the human brain remains elusive. The main objective of this study is to explore the dynamic topological changes of AD networks in terms of brain network segregation and integration. We used electroencephalography (EEG) data recorded from 20 participants (10 AD patients and 10 healthy controls) during resting state. Functional brain networks were reconstructed using EEG source connectivity computed in different frequency bands. Graph theoretical analyses were performed assess differences between both groups. Results revealed that AD networks, compared to networks of age-matched healthy controls, are characterized by lower global information processing (integration) and higher local information processing (segregation). Results showed also significant correlation between the alterations in the AD patients' functional brain networks and their cognitive scores. These findings may contribute to the development of EEG network-based test that could strengthen results obtained from currently-used neurophysiological tests in neurodegenerative diseases.

Simultaneous EEG and diffuse optical imaging of seizure-related hemodynamic activity in the newborn infant brain

NASA Astrophysics Data System (ADS)

Hebden, Jeremy C.; Cooper, Robert J.; Gibson, Adam; Everdell, Nick; Austin, Topun

2012-06-01

An optical imaging system has been developed which uses measurements of diffusely reflected near-infrared light to produce maps of changes in blood flow and oxygenation occurring within the cerebral cortex. Optical sources and detectors are coupled to the head via an array of optical fibers, on a probe held in contact with the scalp, and data is collected at a rate of 10 Hz. A clinical electroencephalography (EEG) system has been integrated with the optical system to enable simultaneous observation of electrical and hemodynamic activity in the cortex of neurologically compromised newborn infants diagnosed with seizures. Studies have made a potentially critically important discovery of previously unknown transient hemodynamic events in infants treated with anticonvulsant medication. We observed repeated episodes of small increases in cortical oxyhemoglobin concentration followed by a profound decrease in 3 of 4 infants studied, each with cerebral injury who presented with neonatal seizures. This was not accompanied by clinical or EEG seizure activity and was not present in nineteen matched controls. The underlying cause of these changes is currently unknown. We tentatively suggest that our results may be associated with a phenomenon known as cortical spreading depolarization, not previously observed in the infant brain.

The Discontinuous Galerkin Finite Element Method for Solving the MEG and the Combined MEG/EEG Forward Problem

PubMed Central

Piastra, Maria Carla; Nüßing, Andreas; Vorwerk, Johannes; Bornfleth, Harald; Oostenveld, Robert; Engwer, Christian; Wolters, Carsten H.

2018-01-01

In Electro- (EEG) and Magnetoencephalography (MEG), one important requirement of source reconstruction is the forward model. The continuous Galerkin finite element method (CG-FEM) has become one of the dominant approaches for solving the forward problem over the last decades. Recently, a discontinuous Galerkin FEM (DG-FEM) EEG forward approach has been proposed as an alternative to CG-FEM (Engwer et al., 2017). It was shown that DG-FEM preserves the property of conservation of charge and that it can, in certain situations such as the so-called skull leakages, be superior to the standard CG-FEM approach. In this paper, we developed, implemented, and evaluated two DG-FEM approaches for the MEG forward problem, namely a conservative and a non-conservative one. The subtraction approach was used as source model. The validation and evaluation work was done in statistical investigations in multi-layer homogeneous sphere models, where an analytic solution exists, and in a six-compartment realistically shaped head volume conductor model. In agreement with the theory, the conservative DG-FEM approach was found to be superior to the non-conservative DG-FEM implementation. This approach also showed convergence with increasing resolution of the hexahedral meshes. While in the EEG case, in presence of skull leakages, DG-FEM outperformed CG-FEM, in MEG, DG-FEM achieved similar numerical errors as the CG-FEM approach, i.e., skull leakages do not play a role for the MEG modality. In particular, for the finest mesh resolution of 1 mm sources with a distance of 1.59 mm from the brain-CSF surface, DG-FEM yielded mean topographical errors (relative difference measure, RDM%) of 1.5% and mean magnitude errors (MAG%) of 0.1% for the magnetic field. However, if the goal is a combined source analysis of EEG and MEG data, then it is highly desirable to employ the same forward model for both EEG and MEG data. Based on these results, we conclude that the newly presented conservative DG-FEM can at least complement and in some scenarios even outperform the established CG-FEM approaches in EEG or combined MEG/EEG source analysis scenarios, which motivates a further evaluation of DG-FEM for applications in bioelectromagnetism. PMID:29456487

Saccadic spike potentials in gamma-band EEG: characterization, detection and suppression.

PubMed

Keren, Alon S; Yuval-Greenberg, Shlomit; Deouell, Leon Y

2010-02-01

Analysis of high-frequency (gamma-band) neural activity by means of non-invasive EEG is gaining increasing interest. However, we have recently shown that a saccade-related spike potential (SP) seriously confounds the analysis of EEG induced gamma-band responses (iGBR), as the SP eludes traditional EEG artifact rejection methods. Here we provide a comprehensive profile of the SP and evaluate methods for its detection and suppression, aiming to unveil true cerebral gamma-band activity. The SP appears consistently as a sharp biphasic deflection of about 22 ms starting at the saccade onset, with a frequency band of approximately 20-90 Hz. On the average, larger saccades elicit higher SP amplitudes. The SP amplitude gradually changes from the extra-ocular channels towards posterior sites with the steepest gradients around the eyes, indicating its ocular source. Although the amplitude and the sign of the SP depend on the choice of reference channel, the potential gradients remain the same and non-zero for all references. The scalp topography is modulated almost exclusively by the direction of saccades, with steeper gradients ipsilateral to the saccade target. We discuss how the above characteristics impede attempts to remove these SPs from the EEG by common temporal filtering, choice of different references, or rejection of contaminated trials. We examine the extent to which SPs can be reliably detected without an eye tracker, assess the degree to which scalp current density derivation attenuates the effect of the SP, and propose a tailored ICA procedure for minimizing the effect of the SP. Copyright (c) 2009 Elsevier Inc. All rights reserved.

Source localization of intermittent rhythmic delta activity in a patient with acute confusional migraine: cross-spectral analysis using standardized low-resolution brain electromagnetic tomography (sLORETA).

PubMed

Kim, Dae-Eun; Shin, Jung-Hyun; Kim, Young-Hoon; Eom, Tae-Hoon; Kim, Sung-Hun; Kim, Jung-Min

2016-01-01

Acute confusional migraine (ACM) shows typical electroencephalography (EEG) patterns of diffuse delta slowing and frontal intermittent rhythmic delta activity (FIRDA). The pathophysiology of ACM is still unclear but these patterns suggest neuronal dysfunction in specific brain areas. We performed source localization analysis of IRDA (in the frequency band of 1-3.5 Hz) to better understand the ACM mechanism. Typical IRDA EEG patterns were recorded in a patient with ACM during the acute stage. A second EEG was obtained after recovery from ACM. To identify source localization of IRDA, statistical non-parametric mapping using standardized low-resolution brain electromagnetic tomography was performed for the delta frequency band comparisons between ACM attack and non-attack periods. A difference in the current density maximum was found in the dorsal anterior cingulated cortex (ACC). The significant differences were widely distributed over the frontal, parietal, temporal and limbic lobe, paracentral lobule and insula and were predominant in the left hemisphere. Dorsal ACC dysfunction was demonstrated for the first time in a patient with ACM in this source localization analysis of IRDA. The ACC plays an important role in the frontal attentional control system and acute confusion. This dysfunction of the dorsal ACC might represent an important ACM pathophysiology.

Cerebrospinal Fluid Biomarkers of Alzheimer's Disease Correlate With Electroencephalography Parameters Assessed by Exact Low-Resolution Electromagnetic Tomography (eLORETA).

PubMed

Hata, Masahiro; Tanaka, Toshihisa; Kazui, Hiroaki; Ishii, Ryouhei; Canuet, Leonides; Pascual-Marqui, Roberto D; Aoki, Yasunori; Ikeda, Shunichiro; Sato, Shunsuke; Suzuki, Yukiko; Kanemoto, Hideki; Yoshiyama, Kenji; Iwase, Masao

2017-09-01

Recently, cerebrospinal fluid (CSF) biomarkers related to Alzheimer's disease (AD) have garnered a lot of clinical attention. To explore neurophysiological traits of AD and parameters for its clinical diagnosis, we examined the association between CSF biomarkers and electroencephalography (EEG) parameters in 14 probable AD patients. Using exact low-resolution electromagnetic tomography (eLORETA), artifact-free 40-sesond EEG data were estimated with current source density (CSD) and lagged phase synchronization (LPS) as the EEG parameters. Correlations between CSF biomarkers and the EEG parameters were assessed. Patients with AD showed significant negative correlation between CSF beta-amyloid (Aβ)-42 concentration and the logarithms of CSD over the right temporal area in the theta band. Total tau concentration was negatively correlated with the LPS between the left frontal eye field and the right auditory area in the alpha-2 band in patients with AD. Our study results suggest that AD biomarkers, in particular CSF Aβ42 and total tau concentrations are associated with the EEG parameters CSD and LPS, respectively. Our results could yield more insights into the complicated pathology of AD.

Review on solving the forward problem in EEG source analysis

PubMed Central

Hallez, Hans; Vanrumste, Bart; Grech, Roberta; Muscat, Joseph; De Clercq, Wim; Vergult, Anneleen; D'Asseler, Yves; Camilleri, Kenneth P; Fabri, Simon G; Van Huffel, Sabine; Lemahieu, Ignace

2007-01-01

Background The aim of electroencephalogram (EEG) source localization is to find the brain areas responsible for EEG waves of interest. It consists of solving forward and inverse problems. The forward problem is solved by starting from a given electrical source and calculating the potentials at the electrodes. These evaluations are necessary to solve the inverse problem which is defined as finding brain sources which are responsible for the measured potentials at the EEG electrodes. Methods While other reviews give an extensive summary of the both forward and inverse problem, this review article focuses on different aspects of solving the forward problem and it is intended for newcomers in this research field. Results It starts with focusing on the generators of the EEG: the post-synaptic potentials in the apical dendrites of pyramidal neurons. These cells generate an extracellular current which can be modeled by Poisson's differential equation, and Neumann and Dirichlet boundary conditions. The compartments in which these currents flow can be anisotropic (e.g. skull and white matter). In a three-shell spherical head model an analytical expression exists to solve the forward problem. During the last two decades researchers have tried to solve Poisson's equation in a realistically shaped head model obtained from 3D medical images, which requires numerical methods. The following methods are compared with each other: the boundary element method (BEM), the finite element method (FEM) and the finite difference method (FDM). In the last two methods anisotropic conducting compartments can conveniently be introduced. Then the focus will be set on the use of reciprocity in EEG source localization. It is introduced to speed up the forward calculations which are here performed for each electrode position rather than for each dipole position. Solving Poisson's equation utilizing FEM and FDM corresponds to solving a large sparse linear system. Iterative methods are required to solve these sparse linear systems. The following iterative methods are discussed: successive over-relaxation, conjugate gradients method and algebraic multigrid method. Conclusion Solving the forward problem has been well documented in the past decades. In the past simplified spherical head models are used, whereas nowadays a combination of imaging modalities are used to accurately describe the geometry of the head model. Efforts have been done on realistically describing the shape of the head model, as well as the heterogenity of the tissue types and realistically determining the conductivity. However, the determination and validation of the in vivo conductivity values is still an important topic in this field. In addition, more studies have to be done on the influence of all the parameters of the head model and of the numerical techniques on the solution of the forward problem. PMID:18053144

Attention-Induced Deactivations in Very Low Frequency EEG Oscillations: Differential Localisation According to ADHD Symptom Status

PubMed Central

Broyd, Samantha J.; Helps, Suzannah K.; Sonuga-Barke, Edmund J. S.

2011-01-01

Background The default-mode network (DMN) is characterised by coherent very low frequency (VLF) brain oscillations. The cognitive significance of this VLF profile remains unclear, partly because of the temporally constrained nature of the blood oxygen-level dependent (BOLD) signal. Previously we have identified a VLF EEG network of scalp locations that shares many features of the DMN. Here we explore the intracranial sources of VLF EEG and examine their overlap with the DMN in adults with high and low ADHD ratings. Methodology/Principal Findings DC-EEG was recorded using an equidistant 66 channel electrode montage in 25 adult participants with high- and 25 participants with low-ratings of ADHD symptoms during a rest condition and an attention demanding Eriksen task. VLF EEG power was calculated in the VLF band (0.02 to 0.2 Hz) for the rest and task condition and compared for high and low ADHD participants. sLORETA was used to identify brain sources associated with the attention-induced deactivation of VLF EEG power, and to examine these sources in relation to ADHD symptoms. There was significant deactivation of VLF EEG power between the rest and task condition for the whole sample. Using s-LORETA the sources of this deactivation were localised to medial prefrontal regions, posterior cingulate cortex/precuneus and temporal regions. However, deactivation sources were different for high and low ADHD groups: In the low ADHD group attention-induced VLF EEG deactivation was most significant in medial prefrontal regions while for the high ADHD group this deactivation was predominantly localised to the temporal lobes. Conclusions/Significance Attention-induced VLF EEG deactivations have intracranial sources that appear to overlap with those of the DMN. Furthermore, these seem to be related to ADHD symptom status, with high ADHD adults failing to significantly deactivate medial prefrontal regions while at the same time showing significant attenuation of VLF EEG power in temporal lobes. PMID:21408092

A preliminary study of muscular artifact cancellation in single-channel EEG.

PubMed

Chen, Xun; Liu, Aiping; Peng, Hu; Ward, Rabab K

2014-10-01

Electroencephalogram (EEG) recordings are often contaminated with muscular artifacts that strongly obscure the EEG signals and complicates their analysis. For the conventional case, where the EEG recordings are obtained simultaneously over many EEG channels, there exists a considerable range of methods for removing muscular artifacts. In recent years, there has been an increasing trend to use EEG information in ambulatory healthcare and related physiological signal monitoring systems. For practical reasons, a single EEG channel system must be used in these situations. Unfortunately, there exist few studies for muscular artifact cancellation in single-channel EEG recordings. To address this issue, in this preliminary study, we propose a simple, yet effective, method to achieve the muscular artifact cancellation for the single-channel EEG case. This method is a combination of the ensemble empirical mode decomposition (EEMD) and the joint blind source separation (JBSS) techniques. We also conduct a study that compares and investigates all possible single-channel solutions and demonstrate the performance of these methods using numerical simulations and real-life applications. The proposed method is shown to significantly outperform all other methods. It can successfully remove muscular artifacts without altering the underlying EEG activity. It is thus a promising tool for use in ambulatory healthcare systems.

ICA-Derived EEG Correlates to Mental Fatigue, Effort, and Workload in a Realistically Simulated Air Traffic Control Task.

PubMed

Dasari, Deepika; Shou, Guofa; Ding, Lei

2017-01-01

Electroencephalograph (EEG) has been increasingly studied to identify distinct mental factors when persons perform cognitively demanding tasks. However, most of these studies examined EEG correlates at channel domain, which suffers the limitation that EEG signals are the mixture of multiple underlying neuronal sources due to the volume conduction effect. Moreover, few studies have been conducted in real-world tasks. To precisely probe EEG correlates with specific neural substrates to mental factors in real-world tasks, the present study examined EEG correlates to three mental factors, i.e., mental fatigue [also known as time-on-task (TOT) effect], workload and effort, in EEG component signals, which were obtained using an independent component analysis (ICA) on high-density EEG data. EEG data were recorded when subjects performed a realistically simulated air traffic control (ATC) task for 2 h. Five EEG independent component (IC) signals that were associated with specific neural substrates (i.e., the frontal, central medial, motor, parietal, occipital areas) were identified. Their spectral powers at their corresponding dominant bands, i.e., the theta power of the frontal IC and the alpha power of the other four ICs, were detected to be correlated to mental workload and effort levels, measured by behavioral metrics. Meanwhile, a linear regression analysis indicated that spectral powers at five ICs significantly increased with TOT. These findings indicated that different levels of mental factors can be sensitively reflected in EEG signals associated with various brain functions, including visual perception, cognitive processing, and motor outputs, in real-world tasks. These results can potentially aid in the development of efficient operational interfaces to ensure productivity and safety in ATC and beyond.

EEG-MEG Integration Enhances the Characterization of Functional and Effective Connectivity in the Resting State Network

PubMed Central

Mideksa, Kidist Gebremariam; Anwar, Abdul Rauf; Stephani, Ulrich; Deuschl, Günther; Freitag, Christine M.; Siniatchkin, Michael

2015-01-01

At the sensor level many aspects, such as spectral power, functional and effective connectivity as well as relative-power-ratio ratio (RPR) and spatial resolution have been comprehensively investigated through both electroencephalography (EEG) and magnetoencephalography (MEG). Despite this, differences between both modalities have not yet been systematically studied by direct comparison. It remains an open question as to whether the integration of EEG and MEG data would improve the information obtained from the above mentioned parameters. Here, EEG (64-channel system) and MEG (275 sensor system) were recorded simultaneously in conditions with eyes open (EO) and eyes closed (EC) in 29 healthy adults. Spectral power, functional and effective connectivity, RPR, and spatial resolution were analyzed at five different frequency bands (delta, theta, alpha, beta and gamma). Networks of functional and effective connectivity were described using a spatial filter approach called the dynamic imaging of coherent sources (DICS) followed by the renormalized partial directed coherence (RPDC). Absolute mean power at the sensor level was significantly higher in EEG than in MEG data in both EO and EC conditions. At the source level, there was a trend towards a better performance of the combined EEG+MEG analysis compared with separate EEG or MEG analyses for the source mean power, functional correlation, effective connectivity for both EO and EC. The network of coherent sources and the spatial resolution were similar for both the EEG and MEG data if they were analyzed separately. Results indicate that the combined approach has several advantages over the separate analyses of both EEG and MEG. Moreover, by a direct comparison of EEG and MEG, EEG was characterized by significantly higher values in all measured parameters in both sensor and source level. All the above conclusions are specific to the resting state task and the specific analysis used in this study to have general conclusion multi-center studies would be helpful. PMID:26509448

EEG source analysis of data from paralysed subjects

NASA Astrophysics Data System (ADS)

Carabali, Carmen A.; Willoughby, John O.; Fitzgibbon, Sean P.; Grummett, Tyler; Lewis, Trent; DeLosAngeles, Dylan; Pope, Kenneth J.

2015-12-01

One of the limitations of Encephalography (EEG) data is its quality, as it is usually contaminated with electric signal from muscle. This research intends to study results of two EEG source analysis methods applied to scalp recordings taken in paralysis and in normal conditions during the performance of a cognitive task. The aim is to determinate which types of analysis are appropriate for dealing with EEG data containing myogenic components. The data used are the scalp recordings of six subjects in normal conditions and during paralysis while performing different cognitive tasks including the oddball task which is the object of this research. The data were pre-processed by filtering it and correcting artefact, then, epochs of one second long for targets and distractors were extracted. Distributed source analysis was performed in BESA Research 6.0, using its results and information from the literature, 9 ideal locations for source dipoles were identified. The nine dipoles were used to perform discrete source analysis, fitting them to the averaged epochs for obtaining source waveforms. The results were statistically analysed comparing the outcomes before and after the subjects were paralysed. Finally, frequency analysis was performed for better explain the results. The findings were that distributed source analysis could produce confounded results for EEG contaminated with myogenic signals, conversely, statistical analysis of the results from discrete source analysis showed that this method could help for dealing with EEG data contaminated with muscle electrical signal.

The FieldTrip-SimBio pipeline for EEG forward solutions.

PubMed

Vorwerk, Johannes; Oostenveld, Robert; Piastra, Maria Carla; Magyari, Lilla; Wolters, Carsten H

2018-03-27

Accurately solving the electroencephalography (EEG) forward problem is crucial for precise EEG source analysis. Previous studies have shown that the use of multicompartment head models in combination with the finite element method (FEM) can yield high accuracies both numerically and with regard to the geometrical approximation of the human head. However, the workload for the generation of multicompartment head models has often been too high and the use of publicly available FEM implementations too complicated for a wider application of FEM in research studies. In this paper, we present a MATLAB-based pipeline that aims to resolve this lack of easy-to-use integrated software solutions. The presented pipeline allows for the easy application of five-compartment head models with the FEM within the FieldTrip toolbox for EEG source analysis. The FEM from the SimBio toolbox, more specifically the St. Venant approach, was integrated into the FieldTrip toolbox. We give a short sketch of the implementation and its application, and we perform a source localization of somatosensory evoked potentials (SEPs) using this pipeline. We then evaluate the accuracy that can be achieved using the automatically generated five-compartment hexahedral head model [skin, skull, cerebrospinal fluid (CSF), gray matter, white matter] in comparison to a highly accurate tetrahedral head model that was generated on the basis of a semiautomatic segmentation with very careful and time-consuming manual corrections. The source analysis of the SEP data correctly localizes the P20 component and achieves a high goodness of fit. The subsequent comparison to the highly detailed tetrahedral head model shows that the automatically generated five-compartment head model performs about as well as a highly detailed four-compartment head model (skin, skull, CSF, brain). This is a significant improvement in comparison to a three-compartment head model, which is frequently used in praxis, since the importance of modeling the CSF compartment has been shown in a variety of studies. The presented pipeline facilitates the use of five-compartment head models with the FEM for EEG source analysis. The accuracy with which the EEG forward problem can thereby be solved is increased compared to the commonly used three-compartment head models, and more reliable EEG source reconstruction results can be obtained.

Spatiotemporal source analysis in scalp EEG vs. intracerebral EEG and SPECT: a case study in a 2-year-old child.

PubMed

Aarabi, A; Grebe, R; Berquin, P; Bourel Ponchel, E; Jalin, C; Fohlen, M; Bulteau, C; Delalande, O; Gondry, C; Héberlé, C; Moullart, V; Wallois, F

2012-06-01

This case study aims to demonstrate that spatiotemporal spike discrimination and source analysis are effective to monitor the development of sources of epileptic activity in time and space. Therefore, they can provide clinically useful information allowing a better understanding of the pathophysiology of individual seizures with time- and space-resolved characteristics of successive epileptic states, including interictal, preictal, postictal, and ictal states. High spatial resolution scalp EEGs (HR-EEG) were acquired from a 2-year-old girl with refractory central epilepsy and single-focus seizures as confirmed by intracerebral EEG recordings and ictal single-photon emission computed tomography (SPECT). Evaluation of HR-EEG consists of the following three global steps: (1) creation of the initial head model, (2) automatic spike and seizure detection, and finally (3) source localization. During the source localization phase, epileptic states are determined to allow state-based spike detection and localization of underlying sources for each spike. In a final cluster analysis, localization results are integrated to determine the possible sources of epileptic activity. The results were compared with the cerebral locations identified by intracerebral EEG recordings and SPECT. The results obtained with this approach were concordant with those of MRI, SPECT and distribution of intracerebral potentials. Dipole cluster centres found for spikes in interictal, preictal, ictal and postictal states were situated an average of 6.3mm from the intracerebral contacts with the highest voltage. Both amplitude and shape of spikes change between states. Dispersion of the dipoles was higher in the preictal state than in the postictal state. Two clusters of spikes were identified. The centres of these clusters changed position periodically during the various epileptic states. High-resolution surface EEG evaluated by an advanced algorithmic approach can be used to investigate the spatiotemporal characteristics of sources located in the epileptic focus. The results were validated by standard methods, ensuring good spatial resolution by MRI and SPECT and optimal temporal resolution by intracerebral EEG. Surface EEG can be used to identify different spike clusters and sources of the successive epileptic states. The method that was used in this study will provide physicians with a better understanding of the pathophysiological characteristics of epileptic activities. In particular, this method may be useful for more effective positioning of implantable intracerebral electrodes. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

A physiologically motivated sparse, compact, and smooth (SCS) approach to EEG source localization.

PubMed

Cao, Cheng; Akalin Acar, Zeynep; Kreutz-Delgado, Kenneth; Makeig, Scott

2012-01-01

Here, we introduce a novel approach to the EEG inverse problem based on the assumption that principal cortical sources of multi-channel EEG recordings may be assumed to be spatially sparse, compact, and smooth (SCS). To enforce these characteristics of solutions to the EEG inverse problem, we propose a correlation-variance model which factors a cortical source space covariance matrix into the multiplication of a pre-given correlation coefficient matrix and the square root of the diagonal variance matrix learned from the data under a Bayesian learning framework. We tested the SCS method using simulated EEG data with various SNR and applied it to a real ECOG data set. We compare the results of SCS to those of an established SBL algorithm.

[EEG source localization using LORETA (low resolution electromagnetic tomography)].

PubMed

Puskás, Szilvia

2011-03-30

Eledctroencephalography (EEG) has excellent temporal resolution, but the spatial resolution is poor. Different source localization methods exist to solve the so-called inverse problem, thus increasing the accuracy of spatial localization. This paper provides an overview of the history of source localization and the main categories of techniques are discussed. LORETA (low resolution electromagnetic tomography) is introduced in details: technical informations are discussed and localization properties of LORETA method are compared to other inverse solutions. Validation of the method with different imaging techniques is also discussed. This paper reviews several publications using LORETA both in healthy persons and persons with different neurological and psychiatric diseases. Finally future possible applications are discussed.

Source localization of rhythmic ictal EEG activity: a study of diagnostic accuracy following STARD criteria.

PubMed

Beniczky, Sándor; Lantz, Göran; Rosenzweig, Ivana; Åkeson, Per; Pedersen, Birthe; Pinborg, Lars H; Ziebell, Morten; Jespersen, Bo; Fuglsang-Frederiksen, Anders

2013-10-01

Although precise identification of the seizure-onset zone is an essential element of presurgical evaluation, source localization of ictal electroencephalography (EEG) signals has received little attention. The aim of our study was to estimate the accuracy of source localization of rhythmic ictal EEG activity using a distributed source model. Source localization of rhythmic ictal scalp EEG activity was performed in 42 consecutive cases fulfilling inclusion criteria. The study was designed according to recommendations for studies on diagnostic accuracy (STARD). The initial ictal EEG signals were selected using a standardized method, based on frequency analysis and voltage distribution of the ictal activity. A distributed source model-local autoregressive average (LAURA)-was used for the source localization. Sensitivity, specificity, and measurement of agreement (kappa) were determined based on the reference standard-the consensus conclusion of the multidisciplinary epilepsy surgery team. Predictive values were calculated from the surgical outcome of the operated patients. To estimate the clinical value of the ictal source analysis, we compared the likelihood ratios of concordant and discordant results. Source localization was performed blinded to the clinical data, and before the surgical decision. Reference standard was available for 33 patients. The ictal source localization had a sensitivity of 70% and a specificity of 76%. The mean measurement of agreement (kappa) was 0.61, corresponding to substantial agreement (95% confidence interval (CI) 0.38-0.84). Twenty patients underwent resective surgery. The positive predictive value (PPV) for seizure freedom was 92% and the negative predictive value (NPV) was 43%. The likelihood ratio was nine times higher for the concordant results, as compared with the discordant ones. Source localization of rhythmic ictal activity using a distributed source model (LAURA) for the ictal EEG signals selected with a standardized method is feasible in clinical practice and has a good diagnostic accuracy. Our findings encourage clinical neurophysiologists assessing ictal EEGs to include this method in their armamentarium. Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.

Change in Mean Frequency of Resting-State Electroencephalography after Transcranial Direct Current Stimulation

PubMed Central

Boonstra, Tjeerd W.; Nikolin, Stevan; Meisener, Ann-Christin; Martin, Donel M.; Loo, Colleen K.

2016-01-01

Transcranial direct current stimulation (tDCS) is proposed as a tool to investigate cognitive functioning in healthy people and as a treatment for various neuropathological disorders. However, the underlying cortical mechanisms remain poorly understood. We aim to investigate whether resting-state electroencephalography (EEG) can be used to monitor the effects of tDCS on cortical activity. To this end we tested whether the spectral content of ongoing EEG activity is significantly different after a single session of active tDCS compared to sham stimulation. Twenty participants were tested in a sham-controlled, randomized, crossover design. Resting-state EEG was acquired before, during and after active tDCS to the left dorsolateral prefrontal cortex (15 min of 2 mA tDCS) and sham stimulation. Electrodes with a diameter of 3.14 cm2 were used for EEG and tDCS. Partial least squares (PLS) analysis was used to examine differences in power spectral density (PSD) and the EEG mean frequency to quantify the slowing of EEG activity after stimulation. PLS revealed a significant increase in spectral power at frequencies below 15 Hz and a decrease at frequencies above 15 Hz after active tDCS (P = 0.001). The EEG mean frequency was significantly reduced after both active tDCS (P < 0.0005) and sham tDCS (P = 0.001), though the decrease in mean frequency was smaller after sham tDCS than after active tDCS (P = 0.073). Anodal tDCS of the left DLPFC using a high current density bi-frontal electrode montage resulted in general slowing of resting-state EEG. The similar findings observed following sham stimulation question whether the standard sham protocol is an appropriate control condition for tDCS. PMID:27375462

Recording event-related activity under hostile magnetic resonance environment: Is multimodal EEG/ERP-MRI recording possible?

PubMed

Karakaş, H M; Karakaş, S; Ozkan Ceylan, A; Tali, E T

2009-08-01

Event-related potentials (ERPs) have high temporal resolution, but insufficient spatial resolution; the converse is true for the functional imaging techniques. The purpose of the study was to test the utility of a multimodal EEG/ERP-MRI technique which combines electroencephalography (EEG) and magnetic resonance imaging (MRI) for a simultaneously high temporal and spatial resolution. The sample consisted of 32 healthy young adults of both sexes. Auditory stimuli were delivered according to the active and passive oddball paradigms in the MRI environment (MRI-e) and in the standard conditions of the electrophysiology laboratory environment (Lab-e). Tasks were presented in a fixed order. Participants were exposed to the recording environments in a counterbalanced order. EEG data were preprocessed for MRI-related artifacts. Source localization was made using a current density reconstruction technique. The ERP waveforms for the MRI-e were morphologically similar to those for the Lab-e. The effect of the recording environment, experimental paradigm and electrode location were analyzed using a 2x2x3 analysis of variance for repeated measures. The ERP components in the two environments showed parametric variations and characteristic topographical distributions. The calculated sources were in line with the related literature. The findings indicated effortful cognitive processing in MRI-e. The study provided preliminary data on the feasibility of the multimodal EEG/ERP-MRI technique. It also indicated lines of research that are to be pursued for a decisive testing of this technique and its implementation to clinical practice.

Functional neurotoxicity evaluation of noribogaine using video-EEG in cynomolgus monkeys.

PubMed

Authier, Simon; Accardi, Michael V; Paquette, Dominique; Pouliot, Mylène; Arezzo, Joseph; Stubbs, R John; Gerson, Ronald J; Friedhoff, Lawrence T; Weis, Holger

2016-01-01

Continuous video-electroencephalographic (EEG) monitoring remains the gold standard for seizure liability assessments in preclinical drug safety assessments. EEG monitored by telemetry was used to assess the behavioral and EEG effects of noribogaine hydrochloride (noribogaine) in cynomolgus monkeys. Noribogaine is an iboga alkaloid being studied for the treatment of opioid dependence. Six cynomolgus monkeys (3 per gender) were instrumented with EEG telemetry transmitters. Noribogaine was administered to each monkey at both doses (i.e., 160 and 320mg/kg, PO) with an interval between dosing of at least 6days, and the resulting behavioral and EEG effects were evaluated. IV pentylenetetrazol (PTZ), served as a positive control for induced seizures. The administration of noribogaine at either of the doses evaluated was not associated with EEG evidence of seizure or with EEG signals known to be premonitory signs of increased seizure risk (e.g., sharp waves, unusual synchrony, shifts to high-frequency patterns). Noribogaine was associated with a mild reduction in activity levels, increased scratching, licking and chewing, and some degree of poor coordination and related clinical signs. A single monkey exhibited brief myoclonic movements that increased in frequency at the high dose, but which did not appear to generalize, cluster or to be linked with EEG abnormalities. Noribogaine was also associated with emesis and partial anorexia. In contrast, PTZ was associated with substantial pre-ictal EEG patterns including large amplitude, repetitive sharp waves leading to generalized seizures and to typical post-ictal EEG frequency attenuation. EEG patterns were within normal limits following administration of noribogaine at doses up to 320mg/kg with concurrent clinical signs that correlated with plasma exposures and resolved by the end of the monitoring period. PTZ was invariably associated with EEG paroxysmal activity leading to ictal EEG. In the current study, a noribogaine dose of 320mg/kg was considered to be the EEG no observed adverse effect level (NOAEL) in conscious freely moving cynomolgus monkeys. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

EEG gamma coherence and other correlates of subjective reports during ayahuasca experiences.

PubMed

Stuckey, David E; Lawson, Robert; Luna, Luis Eduardo

2005-06-01

The current study examined QEEG power and coherence of ayahuasca experiences with two experienced participants in a Brazilian jungle setting. An exploratory case series design was adopted for naturalistic field research. EEGs recorded during visual imagery was compared to eyes-closed baselines. The most important findings were increases in global EEG coherence in the 36-44 Hz and 50-64 Hz frequency bands for both subjects. Widely distributed cortical hyper-coherence seems reasonable given the intense synesthesia during ayahuasca experiences. Other findings include increased modal EEG alpha frequency and global power decreases across the cortex in most frequency bands, which concur with the EEG of psychedelics literature. Exploratory analysis revealed the usefulness of analyzing single Hz bins over the standard wide-band analysis. The discovery-oriented naturalistic approach developed for this study resulted in potentially important findings. We believe that finding increases in global gamma coherence during peak psychedelic experiences might contribute to the discussion of binding theory. Also, in light of recent research with gamma coherence during advanced meditative conditions, our findings might further the comparison of shamanic psychedelic practices with meditation.

Induction and separation of motion artifacts in EEG data using a mobile phantom head device.

PubMed

Oliveira, Anderson S; Schlink, Bryan R; Hairston, W David; König, Peter; Ferris, Daniel P

2016-06-01

Electroencephalography (EEG) can assess brain activity during whole-body motion in humans but head motion can induce artifacts that obfuscate electrocortical signals. Definitive solutions for removing motion artifact from EEG have yet to be found, so creating methods to assess signal processing routines for removing motion artifact are needed. We present a novel method for investigating the influence of head motion on EEG recordings as well as for assessing the efficacy of signal processing approaches intended to remove motion artifact. We used a phantom head device to mimic electrical properties of the human head with three controlled dipolar sources of electrical activity embedded in the phantom. We induced sinusoidal vertical motions on the phantom head using a custom-built platform and recorded EEG signals with three different acquisition systems while the head was both stationary and in varied motion conditions. Recordings showed up to 80% reductions in signal-to-noise ratio (SNR) and up to 3600% increases in the power spectrum as a function of motion amplitude and frequency. Independent component analysis (ICA) successfully isolated the three dipolar sources across all conditions and systems. There was a high correlation (r > 0.85) and marginal increase in the independent components' (ICs) power spectrum (∼15%) when comparing stationary and motion parameters. The SNR of the IC activation was 400%-700% higher in comparison to the channel data SNR, attenuating the effects of motion on SNR. Our results suggest that the phantom head and motion platform can be used to assess motion artifact removal algorithms and compare different EEG systems for motion artifact sensitivity. In addition, ICA is effective in isolating target electrocortical events and marginally improving SNR in relation to stationary recordings.

Induction and separation of motion artifacts in EEG data using a mobile phantom head device

NASA Astrophysics Data System (ADS)

Oliveira, Anderson S.; Schlink, Bryan R.; Hairston, W. David; König, Peter; Ferris, Daniel P.

2016-06-01

Objective. Electroencephalography (EEG) can assess brain activity during whole-body motion in humans but head motion can induce artifacts that obfuscate electrocortical signals. Definitive solutions for removing motion artifact from EEG have yet to be found, so creating methods to assess signal processing routines for removing motion artifact are needed. We present a novel method for investigating the influence of head motion on EEG recordings as well as for assessing the efficacy of signal processing approaches intended to remove motion artifact. Approach. We used a phantom head device to mimic electrical properties of the human head with three controlled dipolar sources of electrical activity embedded in the phantom. We induced sinusoidal vertical motions on the phantom head using a custom-built platform and recorded EEG signals with three different acquisition systems while the head was both stationary and in varied motion conditions. Main results. Recordings showed up to 80% reductions in signal-to-noise ratio (SNR) and up to 3600% increases in the power spectrum as a function of motion amplitude and frequency. Independent component analysis (ICA) successfully isolated the three dipolar sources across all conditions and systems. There was a high correlation (r > 0.85) and marginal increase in the independent components’ (ICs) power spectrum (˜15%) when comparing stationary and motion parameters. The SNR of the IC activation was 400%-700% higher in comparison to the channel data SNR, attenuating the effects of motion on SNR. Significance. Our results suggest that the phantom head and motion platform can be used to assess motion artifact removal algorithms and compare different EEG systems for motion artifact sensitivity. In addition, ICA is effective in isolating target electrocortical events and marginally improving SNR in relation to stationary recordings.

Application of high-frequency Granger causality to analysis of epileptic seizures and surgical decision making.

PubMed

Epstein, Charles M; Adhikari, Bhim M; Gross, Robert; Willie, Jon; Dhamala, Mukesh

2014-12-01

In recent decades intracranial EEG (iEEG) recordings using increasing numbers of electrodes, higher sampling rates, and a variety of visual and quantitative analyses have indicated the presence of widespread, high frequency ictal and preictal oscillations (HFOs) associated with regions of seizure onset. Seizure freedom has been correlated with removal of brain regions generating pathologic HFOs. However, quantitative analysis of preictal HFOs has seldom been applied to the clinical problem of planning the surgical resection. We performed Granger causality (GC) analysis of iEEG recordings to analyze features of preictal seizure networks and to aid in surgical decision making. Ten retrospective and two prospective patients were chosen on the basis of individually stereotyped seizure patterns by visual criteria. Prospective patients were selected, additionally, for failure of those criteria to resolve apparent multilobar ictal onsets. iEEG was recorded at 500 or 1,000 Hz, using up to 128 surface and depth electrodes. Preictal and early ictal GC from individual electrodes was characterized by the strength of causal outflow, spatial distribution, and hierarchical causal relationships. In all patients we found significant, widespread preictal GC network activity at peak frequencies from 80 to 250 Hz, beginning 2-42 s before visible electrographic onset. In the two prospective patients, GC source/sink comparisons supported the exclusion of early ictal regions that were not the dominant causal sources, and contributed to planning of more limited surgical resections. Both patients have a class 1 outcome at 1 year. GC analysis of iEEG has the potential to increase understanding of preictal network activity, and to help improve surgical outcomes in cases of otherwise ambiguous iEEG onset. Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.

Frequency domain beamforming of magnetoencephalographic beta band activity in epilepsy patients with focal cortical dysplasia.

PubMed

Heers, Marcel; Hirschmann, Jan; Jacobs, Julia; Dümpelmann, Matthias; Butz, Markus; von Lehe, Marec; Elger, Christian E; Schnitzler, Alfons; Wellmer, Jörg

2014-09-01

Spike-based magnetoencephalography (MEG) source localization is an established method in the presurgical evaluation of epilepsy patients. Focal cortical dysplasias (FCDs) are associated with focal epileptic discharges of variable morphologies in the beta frequency band in addition to single epileptic spikes. Therefore, we investigated the potential diagnostic value of MEG-based localization of spike-independent beta band (12-30Hz) activity generated by epileptogenic lesions. Five patients with FCD IIB underwent MEG. In one patient, invasive EEG (iEEG) was recorded simultaneously with MEG. In two patients, iEEG succeeded MEG, and two patients had MEG only. MEG and iEEG were evaluated for epileptic spikes. Two minutes of iEEG data and MEG epochs with no spikes as well as MEG epochs with epileptic spikes were analyzed in the frequency domain. MEG oscillatory beta band activity was localized using Dynamic Imaging of Coherent Sources. Intralesional beta band activity was coherent between simultaneous MEG and iEEG recordings. Continuous 14Hz beta band power correlated with the rate of interictal epileptic discharges detected in iEEG. In cases where visual MEG evaluation revealed epileptic spikes, the sources of beta band activity localized within <2cm of the epileptogenic lesion as shown on magnetic resonance imaging. This result held even when visually marked epileptic spikes were deselected. When epileptic spikes were detectable in iEEG but not MEG, MEG beta band activity source localization failed. Source localization of beta band activity has the potential to contribute to the identification of epileptic foci in addition to source localization of visually marked epileptic spikes. Thus, this technique may assist in the localization of epileptic foci in patients with suspected FCD. Copyright © 2014 Elsevier B.V. All rights reserved.

Corrected Four-Sphere Head Model for EEG Signals.

PubMed

Næss, Solveig; Chintaluri, Chaitanya; Ness, Torbjørn V; Dale, Anders M; Einevoll, Gaute T; Wójcik, Daniel K

2017-01-01

The EEG signal is generated by electrical brain cell activity, often described in terms of current dipoles. By applying EEG forward models we can compute the contribution from such dipoles to the electrical potential recorded by EEG electrodes. Forward models are key both for generating understanding and intuition about the neural origin of EEG signals as well as inverse modeling, i.e., the estimation of the underlying dipole sources from recorded EEG signals. Different models of varying complexity and biological detail are used in the field. One such analytical model is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF), skull, and scalp, respectively. While conceptually clear, the mathematical expression for the electric potentials in the four-sphere model is cumbersome, and we observed that the formulas presented in the literature contain errors. Here, we derive and present the correct analytical formulas with a detailed derivation. A useful application of the analytical four-sphere model is that it can serve as ground truth to test the accuracy of numerical schemes such as the Finite Element Method (FEM). We performed FEM simulations of the four-sphere head model and showed that they were consistent with the corrected analytical formulas. For future reference we provide scripts for computing EEG potentials with the four-sphere model, both by means of the correct analytical formulas and numerical FEM simulations.

Corrected Four-Sphere Head Model for EEG Signals

PubMed Central

Næss, Solveig; Chintaluri, Chaitanya; Ness, Torbjørn V.; Dale, Anders M.; Einevoll, Gaute T.; Wójcik, Daniel K.

2017-01-01

The EEG signal is generated by electrical brain cell activity, often described in terms of current dipoles. By applying EEG forward models we can compute the contribution from such dipoles to the electrical potential recorded by EEG electrodes. Forward models are key both for generating understanding and intuition about the neural origin of EEG signals as well as inverse modeling, i.e., the estimation of the underlying dipole sources from recorded EEG signals. Different models of varying complexity and biological detail are used in the field. One such analytical model is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF), skull, and scalp, respectively. While conceptually clear, the mathematical expression for the electric potentials in the four-sphere model is cumbersome, and we observed that the formulas presented in the literature contain errors. Here, we derive and present the correct analytical formulas with a detailed derivation. A useful application of the analytical four-sphere model is that it can serve as ground truth to test the accuracy of numerical schemes such as the Finite Element Method (FEM). We performed FEM simulations of the four-sphere head model and showed that they were consistent with the corrected analytical formulas. For future reference we provide scripts for computing EEG potentials with the four-sphere model, both by means of the correct analytical formulas and numerical FEM simulations. PMID:29093671

Recovering TMS-evoked EEG responses masked by muscle artifacts.

PubMed

Mutanen, Tuomas P; Kukkonen, Matleena; Nieminen, Jaakko O; Stenroos, Matti; Sarvas, Jukka; Ilmoniemi, Risto J

2016-10-01

Combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) often suffers from large muscle artifacts. Muscle artifacts can be removed using signal-space projection (SSP), but this can make the visual interpretation of the remaining EEG data difficult. We suggest to use an additional step after SSP that we call source-informed reconstruction (SIR). SSP-SIR improves substantially the signal quality of artifactual TMS-EEG data, causing minimal distortion in the neuronal signal components. In the SSP-SIR approach, we first project out the muscle artifact using SSP. Utilizing an anatomical model and the remaining signal, we estimate an equivalent source distribution in the brain. Finally, we map the obtained source estimate onto the original signal space, again using anatomical information. This approach restores the neuronal signals in the sensor space and interpolates EEG traces onto the completely rejected channels. The introduced algorithm efficiently suppresses TMS-related muscle artifacts in EEG while retaining well the neuronal EEG topographies and signals. With the presented method, we can remove muscle artifacts from TMS-EEG data and recover the underlying brain responses without compromising the readability of the signals of interest. Copyright © 2016 Elsevier Inc. All rights reserved.

Effects of adaptive refinement on the inverse EEG solution

NASA Astrophysics Data System (ADS)

Weinstein, David M.; Johnson, Christopher R.; Schmidt, John A.

1995-10-01

One of the fundamental problems in electroencephalography can be characterized by an inverse problem. Given a subset of electrostatic potentials measured on the surface of the scalp and the geometry and conductivity properties within the head, calculate the current vectors and potential fields within the cerebrum. Mathematically the generalized EEG problem can be stated as solving Poisson's equation of electrical conduction for the primary current sources. The resulting problem is mathematically ill-posed i.e., the solution does not depend continuously on the data, such that small errors in the measurement of the voltages on the scalp can yield unbounded errors in the solution, and, for the general treatment of a solution of Poisson's equation, the solution is non-unique. However, if accurate solutions the general treatment of a solution of Poisson's equation, the solution is non-unique. However, if accurate solutions to such problems could be obtained, neurologists would gain noninvasive accesss to patient-specific cortical activity. Access to such data would ultimately increase the number of patients who could be effectively treated for pathological cortical conditions such as temporal lobe epilepsy. In this paper, we present the effects of spatial adaptive refinement on the inverse EEG problem and show that the use of adaptive methods allow for significantly better estimates of electric and potential fileds within the brain through an inverse procedure. To test these methods, we have constructed several finite element head models from magneteic resonance images of a patient. The finite element meshes ranged in size from 2724 nodes and 12,812 elements to 5224 nodes and 29,135 tetrahedral elements, depending on the level of discretization. We show that an adaptive meshing algorithm minimizes the error in the forward problem due to spatial discretization and thus increases the accuracy of the inverse solution.

Deblurring

NASA Technical Reports Server (NTRS)

Gevins, A.; Le, J.; Leong, H.; McEvoy, L. K.; Smith, M. E.

1999-01-01

In most instances, traditional EEG methodology provides insufficient spatial detail to identify relationships between brain electrical events and structures and functions visualized by magnetic resonance imaging or positron emission tomography. This article describes a method called Deblurring for increasing the spatial detail of the EEG and for fusing neurophysiologic and neuroanatomic data. Deblurring estimates potentials near the outer convexity of the cortex using a realistic finite element model of the structure of a subject's head determined from their magnetic resonance images. Deblurring is not a source localization technique and thus makes no assumptions about the number or type of generator sources. The validity of Deblurring has been initially tested by comparing deblurred data with potentials measured with subdural grid recordings. Results suggest that deblurred topographic maps, registered with a subject's magnetic resonance imaging and rendered in three dimensions, provide better spatial detail than has heretofore been obtained with scalp EEG recordings. Example results are presented from research studies of somatosensory stimulation, movement, language, attention and working memory. Deblurred ictal EEG data are also presented, indicating that this technique may have future clinical application as an aid to seizure localization and surgical planning.

Minimum-norm cortical source estimation in layered head models is robust against skull conductivity error☆☆☆

PubMed Central

Stenroos, Matti; Hauk, Olaf

2013-01-01

The conductivity profile of the head has a major effect on EEG signals, but unfortunately the conductivity for the most important compartment, skull, is only poorly known. In dipole modeling studies, errors in modeled skull conductivity have been considered to have a detrimental effect on EEG source estimation. However, as dipole models are very restrictive, those results cannot be generalized to other source estimation methods. In this work, we studied the sensitivity of EEG and combined MEG + EEG source estimation to errors in skull conductivity using a distributed source model and minimum-norm (MN) estimation. We used a MEG/EEG modeling set-up that reflected state-of-the-art practices of experimental research. Cortical surfaces were segmented and realistically-shaped three-layer anatomical head models were constructed, and forward models were built with Galerkin boundary element method while varying the skull conductivity. Lead-field topographies and MN spatial filter vectors were compared across conductivities, and the localization and spatial spread of the MN estimators were assessed using intuitive resolution metrics. The results showed that the MN estimator is robust against errors in skull conductivity: the conductivity had a moderate effect on amplitudes of lead fields and spatial filter vectors, but the effect on corresponding morphologies was small. The localization performance of the EEG or combined MEG + EEG MN estimator was only minimally affected by the conductivity error, while the spread of the estimate varied slightly. Thus, the uncertainty with respect to skull conductivity should not prevent researchers from applying minimum norm estimation to EEG or combined MEG + EEG data. Comparing our results to those obtained earlier with dipole models shows that general judgment on the performance of an imaging modality should not be based on analysis with one source estimation method only. PMID:23639259

Comparison of corrected QT interval as measured on electroencephalography versus 12-lead electrocardiography in children with a history of syncope.

PubMed

Massey, Shavonne L; Wise, Marshall S; Madan, Nandini; Carvalho, Karen; Khurana, Divya; Legido, Agustin; Valencia, Ignacio

2011-11-01

Long QT syndrome can present with neurological manifestations, including syncope and seizure-like activity. These patients often receive an initial neurologic evaluation, including electroencephalography (EEG). Our previous retrospective study suggested an increased prevalence of prolonged corrected QT interval (QTc) measured during the EEG of patients with syncope. The aim of the current study is to assess the accuracy of the EEG QTc reading compared with the nonsimultaneous 12-lead electrocardiography (ECG) in children with syncope. Abnormal QTc was defined as ≥450 ms in boys, ≥460 ms in girls. Forty-two children were included. There was no significant correlation between QTc readings in the EEG and ECG. EEG failed to identify 2 children with prolonged QTc in the ECG and overestimated the QTc in 3 children with normal QTc in the ECG. This study suggests that interpretation of the QTc segment during an EEG is limited. Further studies with simultaneous EEG and 12-lead ECG are warranted.

Infant phantom head circuit board for EEG head phantom and pediatric brain simulation

NASA Astrophysics Data System (ADS)

Almohsen, Safa

The infant's skull differs from an adult skull because of the characteristic features of the human skull during early development. The fontanels and the conductivity of the infant skull influence surface currents, generated by neurons, which underlie electroencephalography (EEG) signals. An electric circuit was built to power a set of simulated neural sources for an infant brain activity simulator. Also, in the simulator, three phantom tissues were created using saline solution plus Agarose gel to mimic the conductivity of each layer in the head [scalp, skull brain]. The conductivity measurement was accomplished by two different techniques: using the four points' measurement technique, and a conductivity meter. Test results showed that the optimized phantom tissues had appropriate conductivities to simulate each tissue layer to fabricate a physical head phantom. In this case, the best results should be achieved by testing the electrical neural circuit with the sample physical model to generate simulated EEG data and use that to solve both the forward and the inverse problems for the purpose of localizing the neural sources in the head phantom.

Brain-computer interaction research at the Computer Vision and Multimedia Laboratory, University of Geneva.

PubMed

Pun, Thierry; Alecu, Teodor Iulian; Chanel, Guillaume; Kronegg, Julien; Voloshynovskiy, Sviatoslav

2006-06-01

This paper describes the work being conducted in the domain of brain-computer interaction (BCI) at the Multimodal Interaction Group, Computer Vision and Multimedia Laboratory, University of Geneva, Geneva, Switzerland. The application focus of this work is on multimodal interaction rather than on rehabilitation, that is how to augment classical interaction by means of physiological measurements. Three main research topics are addressed. The first one concerns the more general problem of brain source activity recognition from EEGs. In contrast with classical deterministic approaches, we studied iterative robust stochastic based reconstruction procedures modeling source and noise statistics, to overcome known limitations of current techniques. We also developed procedures for optimal electroencephalogram (EEG) sensor system design in terms of placement and number of electrodes. The second topic is the study of BCI protocols and performance from an information-theoretic point of view. Various information rate measurements have been compared for assessing BCI abilities. The third research topic concerns the use of EEG and other physiological signals for assessing a user's emotional status.

Independent EEG Sources Are Dipolar

PubMed Central

Delorme, Arnaud; Palmer, Jason; Onton, Julie; Oostenveld, Robert; Makeig, Scott

2012-01-01

Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition ‘dipolarity’ defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison). PMID:22355308

[Magnetoencephalography in the presurgical evaluation of patients with drug-resistant epilepsy].

PubMed

Koptelova, A M; Arkhipova, N A; Golovteev, A L; Chadaev, V A; Grinenko, O A; Kozlova, A B; Novikova, S I; Stepanenko, A Iu; Melikian, A G; Stroganova, T A

2013-01-01

Magnetoencephalography (MEG) in combination with structural MRI (magnetic source imaging, MSI) plays an increasingly important role as one of the tools for presurgical evaluation of medically intractable focal epilepsy. The aim of the study was to compare the MSI and commonly used video EEG monitoring method (vEEG) in their sensitivity to interictal epileptic discharges (IED) in 22 patients with drug resistant epilepsy. Furthermore, the detection and localization results obtained by both methods were verified using the data of electrocorticography (ECoG) and postsurgical outcome in 13 patients who underwent invasive EEG monitoring and surgery. The results showed that MSI was superior to vEEC in terms of sensitivity to IED with difference in sensitivity of 22%. The data also suggested that MSI superiority to vEEG in detecting epileptic discharges might, at least partly, arise from better MEG responsiveness to epileptic events coming from the medial, opercular and basal aspects of cortical lobes. MSI localization estimates were in the same cortical lobe and at the same lobar aspects as the epileptic foci detected by ECoG in all patients. Thus, magnetic source imaging can provide critical localization information that is not available when other noninvasive methods, such as vEEG and MRI, are used.

Demonstrating Test-Retest Reliability of Electrophysiological Measures for Healthy Adults in a Multisite Study of Biomarkers of Antidepressant Treatment Response

PubMed Central

Tenke, Craig E.; Kayser, Jürgen; Pechtel, Pia; Webb, Christian A.; Dillon, Daniel G.; Goer, Franziska; Murray, Laura; Deldin, Patricia; Kurian, Benji T.; McGrath, Patrick J.; Parsey, Ramin; Trivedi, Madhukar; Fava, Maurizio; Weissman, Myrna M.; McInnis, Melvin; Abraham, Karen; Alvarenga, Jorge; Alschuler, Daniel M.; Cooper, Crystal; Pizzagalli, Diego A.; Bruder, Gerard E.

2016-01-01

Growing evidence suggests that loudness dependency of auditory evoked potentials (LDAEP) and resting EEG alpha and theta may be biological markers for predicting response to antidepressants. In spite of this promise, little is known about the joint reliability of these markers, and thus their clinical applicability. New, standardized procedures were developed to improve the compatibility of data acquired with different EEG platforms, and used to examine test-retest reliability for the three electrophysiological measures selected for a multisite project—Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC). Thirty nine healthy controls across four clinical research sites were tested in two sessions separated by about one week. Resting EEG (eyes-open and eyes-closed conditions) was recorded and LDAEP measured using binaural tones (1000 Hz, 40 ms) at five intensities (60–100 dB SPL). Principal components analysis (PCA) of current source density (CSD) waveforms reduced volume conduction and provided reference-free measures of resting EEG alpha and N1 dipole activity to tones from auditory cortex. Low Resolution Electromagnetic Tomography (LORETA) extracted resting theta current density measures corresponding to rostral anterior cingulate (rACC), which has been implicated in treatment response. There were no significant differences in posterior alpha, N1 dipole or rACC theta across sessions. Test-retest reliability was .84 for alpha, .87 for N1 dipole, and .70 for theta rACC current density. The demonstration of good-to-excellent reliability for these measures provides a template for future EEG/ERP studies from multiple testing sites, and an important step for evaluating them as biomarkers for predicting treatment response. PMID:28000259

Demonstrating test-retest reliability of electrophysiological measures for healthy adults in a multisite study of biomarkers of antidepressant treatment response.

PubMed

Tenke, Craig E; Kayser, Jürgen; Pechtel, Pia; Webb, Christian A; Dillon, Daniel G; Goer, Franziska; Murray, Laura; Deldin, Patricia; Kurian, Benji T; McGrath, Patrick J; Parsey, Ramin; Trivedi, Madhukar; Fava, Maurizio; Weissman, Myrna M; McInnis, Melvin; Abraham, Karen; E Alvarenga, Jorge; Alschuler, Daniel M; Cooper, Crystal; Pizzagalli, Diego A; Bruder, Gerard E

2017-01-01

Growing evidence suggests that loudness dependency of auditory evoked potentials (LDAEP) and resting EEG alpha and theta may be biological markers for predicting response to antidepressants. In spite of this promise, little is known about the joint reliability of these markers, and thus their clinical applicability. New standardized procedures were developed to improve the compatibility of data acquired with different EEG platforms, and used to examine test-retest reliability for the three electrophysiological measures selected for a multisite project-Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC). Thirty-nine healthy controls across four clinical research sites were tested in two sessions separated by about 1 week. Resting EEG (eyes-open and eyes-closed conditions) was recorded and LDAEP measured using binaural tones (1000 Hz, 40 ms) at five intensities (60-100 dB SPL). Principal components analysis of current source density waveforms reduced volume conduction and provided reference-free measures of resting EEG alpha and N1 dipole activity to tones from auditory cortex. Low-resolution electromagnetic tomography (LORETA) extracted resting theta current density measures corresponding to rostral anterior cingulate (rACC), which has been implicated in treatment response. There were no significant differences in posterior alpha, N1 dipole, or rACC theta across sessions. Test-retest reliability was .84 for alpha, .87 for N1 dipole, and .70 for theta rACC current density. The demonstration of good-to-excellent reliability for these measures provides a template for future EEG/ERP studies from multiple testing sites, and an important step for evaluating them as biomarkers for predicting treatment response. © 2016 Society for Psychophysiological Research.

Comparison of imaging modalities and source-localization algorithms in locating the induced activity during deep brain stimulation of the STN.

PubMed

Mideksa, K G; Singh, A; Hoogenboom, N; Hellriegel, H; Krause, H; Schnitzler, A; Deuschl, G; Raethjen, J; Schmidt, G; Muthuraman, M

2016-08-01

One of the most commonly used therapy to treat patients with Parkinson's disease (PD) is deep brain stimulation (DBS) of the subthalamic nucleus (STN). Identifying the most optimal target area for the placement of the DBS electrodes have become one of the intensive research area. In this study, the first aim is to investigate the capabilities of different source-analysis techniques in detecting deep sources located at the sub-cortical level and validating it using the a-priori information about the location of the source, that is, the STN. Secondly, we aim at an investigation of whether EEG or MEG is best suited in mapping the DBS-induced brain activity. To do this, simultaneous EEG and MEG measurement were used to record the DBS-induced electromagnetic potentials and fields. The boundary-element method (BEM) have been used to solve the forward problem. The position of the DBS electrodes was then estimated using the dipole (moving, rotating, and fixed MUSIC), and current-density-reconstruction (CDR) (minimum-norm and sLORETA) approaches. The source-localization results from the dipole approaches demonstrated that the fixed MUSIC algorithm best localizes deep focal sources, whereas the moving dipole detects not only the region of interest but also neighboring regions that are affected by stimulating the STN. The results from the CDR approaches validated the capability of sLORETA in detecting the STN compared to minimum-norm. Moreover, the source-localization results using the EEG modality outperformed that of the MEG by locating the DBS-induced activity in the STN.

Simultaneous head tissue conductivity and EEG source location estimation.

PubMed

Akalin Acar, Zeynep; Acar, Can E; Makeig, Scott

2016-01-01

Accurate electroencephalographic (EEG) source localization requires an electrical head model incorporating accurate geometries and conductivity values for the major head tissues. While consistent conductivity values have been reported for scalp, brain, and cerebrospinal fluid, measured brain-to-skull conductivity ratio (BSCR) estimates have varied between 8 and 80, likely reflecting both inter-subject and measurement method differences. In simulations, mis-estimation of skull conductivity can produce source localization errors as large as 3cm. Here, we describe an iterative gradient-based approach to Simultaneous tissue Conductivity And source Location Estimation (SCALE). The scalp projection maps used by SCALE are obtained from near-dipolar effective EEG sources found by adequate independent component analysis (ICA) decomposition of sufficient high-density EEG data. We applied SCALE to simulated scalp projections of 15cm(2)-scale cortical patch sources in an MR image-based electrical head model with simulated BSCR of 30. Initialized either with a BSCR of 80 or 20, SCALE estimated BSCR as 32.6. In Adaptive Mixture ICA (AMICA) decompositions of (45-min, 128-channel) EEG data from two young adults we identified sets of 13 independent components having near-dipolar scalp maps compatible with a single cortical source patch. Again initialized with either BSCR 80 or 25, SCALE gave BSCR estimates of 34 and 54 for the two subjects respectively. The ability to accurately estimate skull conductivity non-invasively from any well-recorded EEG data in combination with a stable and non-invasively acquired MR imaging-derived electrical head model could remove a critical barrier to using EEG as a sub-cm(2)-scale accurate 3-D functional cortical imaging modality. Copyright © 2015 Elsevier Inc. All rights reserved.

Simultaneous head tissue conductivity and EEG source location estimation

PubMed Central

Acar, Can E.; Makeig, Scott

2015-01-01

Accurate electroencephalographic (EEG) source localization requires an electrical head model incorporating accurate geometries and conductivity values for the major head tissues. While consistent conductivity values have been reported for scalp, brain, and cerebrospinal fluid, measured brain-to-skull conductivity ratio (BSCR) estimates have varied between 8 and 80, likely reflecting both inter-subject and measurement method differences. In simulations, mis-estimation of skull conductivity can produce source localization errors as large as 3 cm. Here, we describe an iterative gradient-based approach to Simultaneous tissue Conductivity And source Location Estimation (SCALE). The scalp projection maps used by SCALE are obtained from near-dipolar effective EEG sources found by adequate independent component analysis (ICA) decomposition of sufficient high-density EEG data. We applied SCALE to simulated scalp projections of 15 cm2-scale cortical patch sources in an MR image-based electrical head model with simulated BSCR of 30. Initialized either with a BSCR of 80 or 20, SCALE estimated BSCR as 32.6. In Adaptive Mixture ICA (AMICA) decompositions of (45-min, 128-channel) EEG data from two young adults we identified sets of 13 independent components having near-dipolar scalp maps compatible with a single cortical source patch. Again initialized with either BSCR 80 or 25, SCALE gave BSCR estimates of 34 and 54 for the two subjects respectively. The ability to accurately estimate skull conductivity non-invasively from any well-recorded EEG data in combination with a stable and non-invasively acquired MR imaging-derived electrical head model could remove a critical barrier to using EEG as a sub-cm2-scale accurate 3-D functional cortical imaging modality. PMID:26302675

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

NASA Astrophysics Data System (ADS)

Safieddine, Doha; Kachenoura, Amar; Albera, Laurent; Birot, Gwénaël; Karfoul, Ahmad; Pasnicu, Anca; Biraben, Arnaud; Wendling, Fabrice; Senhadji, Lotfi; Merlet, Isabelle

2012-12-01

Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. The efficiency of CCA, ICA, EMD, and WT to correct the muscular artifact was evaluated both by calculating the normalized mean-squared error between denoised and original signals and by comparing the results of source localization obtained from artifact-free as well as noisy signals, before and after artifact correction. Tests on real data recorded in an epileptic patient are also presented. The results obtained in the context of simulations and real data show that EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity. For less noisy data, and when spikes arose from a single cortical source, the myogenic artifact was best corrected with CCA and ICA. Otherwise when spikes originated from two distinct sources, either EMD or ICA offered the most reliable denoising result for highly noisy data, while WT offered the better denoising result for less noisy data. These results suggest that the performance of muscle artifact correction methods strongly depend on the level of data contamination, and of the source configuration underlying EEG signals. Eventually, some insights into the numerical complexity of these four algorithms are given.

Miniaturized, on-head, invasive electrode connector integrated EEG data acquisition system.

PubMed

Ives, John R; Mirsattari, Seyed M; Jones, D

2007-07-01

Intracranial electroencephalogram (EEG) monitoring involves recording multi-contact electrodes. The current systems require separate wires from each recording contact to the data acquisition unit resulting in many connectors and cables. To overcome limitations of such systems such as noise, restrictions in patient mobility and compliance, we developed a miniaturized EEG monitoring system with the amplifiers and multiplexers integrated into the electrode connectors and mounted on the head. Small, surface-mounted instrumentation amplifiers, coupled with 8:1 analog multiplexers, were assembled into 8-channel modular units to connect to 16:1 analog multiplexer manifold to create a small (55 cm(3)) head-mounted 128-channel system. A 6-conductor, 30 m long cable was used to transmit the EEG signals from the patient to the remote data acquisition system. Miniaturized EEG amplifiers and analog multiplexers were integrated directly into the electrode connectors. Up to 128-channels of EEG were amplified and analog multiplexed directly on the patient's head. The amplified EEG data were obtained over one long wire. A miniaturized system of invasive EEG recording has the potential to reduce artefact, simplify trouble-shooting, lower nursing care and increase patient compliance. Miniaturization technology improves intracranial EEG monitoring and leads to >128-channel capacity.

Beta activity in the premotor cortex is increased during stabilized as compared to normal walking

PubMed Central

Bruijn, Sjoerd M.; Van Dieën, Jaap H.; Daffertshofer, Andreas

2015-01-01

Walking on two legs is inherently unstable. Still, we humans perform remarkable well at it, mostly without falling. To gain more understanding of the role of the brain in controlling gait stability we measured brain activity using electro-encephalography (EEG) during stabilized and normal walking. Subjects walked on a treadmill in two conditions, each lasting 10 min; normal, and while being laterally stabilized by elastic cords. Kinematics of trunk and feet, electro-myography (EMG) of neck muscles, as well as 64-channel EEG were recorded. To assess gait stability the local divergence exponent, step width, and trunk range of motion were calculated from the kinematic data. We used independent component (IC) analysis to remove movement, EMG, and eyeblink artifacts from the EEG, after which dynamic imaging of coherent sources beamformers were determined to identify cortical sources that showed a significant difference between conditions. Stabilized walking led to a significant increase in gait stability, i.e., lower local divergence exponents. Beamforming analysis of the beta band activity revealed significant sources in bilateral pre-motor cortices. Projection of sensor data on these sources showed a significant difference only in the left premotor area, with higher beta power during stabilized walking, specifically around push-off, although only significant around contralateral push-off. It appears that even during steady gait the cortex is involved in the control of stability. PMID:26578937

Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior.

PubMed

Hansen, Sofie Therese; Hansen, Lars Kai

2017-03-01

Electroencephalography (EEG) can capture brain dynamics in high temporal resolution. By projecting the scalp EEG signal back to its origin in the brain also high spatial resolution can be achieved. Source localized EEG therefore has potential to be a very powerful tool for understanding the functional dynamics of the brain. Solving the inverse problem of EEG is however highly ill-posed as there are many more potential locations of the EEG generators than EEG measurement points. Several well-known properties of brain dynamics can be exploited to alleviate this problem. More short ranging connections exist in the brain than long ranging, arguing for spatially focal sources. Additionally, recent work (Delorme et al., 2012) argues that EEG can be decomposed into components having sparse source distributions. On the temporal side both short and long term stationarity of brain activation are seen. We summarize these insights in an inverse solver, the so-called "Variational Garrote" (Kappen and Gómez, 2013). Using a Markov prior we can incorporate flexible degrees of temporal stationarity. Through spatial basis functions spatially smooth distributions are obtained. Sparsity of these are inherent to the Variational Garrote solver. We name our method the MarkoVG and demonstrate its ability to adapt to the temporal smoothness and spatial sparsity in simulated EEG data. Finally a benchmark EEG dataset is used to demonstrate MarkoVG's ability to recover non-stationary brain dynamics. Copyright © 2016 Elsevier Inc. All rights reserved.

Activity of left inferior frontal gyrus related to word repetition effects: LORETA imaging with 128-channel EEG and individual MRI.

PubMed

Kim, Young Youn; Lee, Boreom; Shin, Yong Wook; Kwon, Jun Soo; Kim, Myung-Sun

2006-02-01

We investigated the brain substrate of word repetition effects on the implicit memory task using low-resolution electromagnetic tomography (LORETA) with high-density 128-channel EEG and individual MRI as a realistic head model. Thirteen right-handed, healthy subjects performed a word/non-word discrimination task, in which the words and non-words were presented visually, and some of the words appeared twice with a lag of one or five items. All of the subjects exhibited word repetition effects with respect to the behavioral data, in which a faster reaction time was observed to the repeated word (old word) than to the first presentation of the word (new word). The old words elicited more positive-going potentials than the new words, beginning at 200 ms and lasting until 500 ms post-stimulus. We conducted source reconstruction using LORETA at a latency of 400 ms with the peak mean global field potentials and used statistical parametric mapping for the statistical analysis. We found that the source elicited by the old words exhibited a statistically significant current density reduction in the left inferior frontal gyrus. This is the first study to investigate the generators of word repetition effects using voxel-by-voxel statistical mapping of the current density with individual MRI and high-density EEG.

MAO-A Phenotype Effects Response Sensitivity and the Parietal Old/New Effect during Recognition Memory

PubMed Central

Ross, Robert S.; Smolen, Andrew; Curran, Tim; Nyhus, Erika

2018-01-01

A critical problem for developing personalized treatment plans for cognitive disruptions is the lack of understanding how individual differences influence cognition. Recognition memory is one cognitive ability that varies from person to person and that variation may be related to different genetic phenotypes. One gene that may impact recognition memory is the monoamine oxidase A gene (MAO-A), which influences the transcription rate of MAO-A. Examination of how MAO-A phenotypes impact behavioral and event-related potentials (ERPs) correlates of recognition memory may help explain individual differences in recognition memory performance. Therefore, the current study uses electroencephalography (EEG) in combination with genetic phenotyping of the MAO-A gene to determine how well-characterized ERP components of recognition memory, the early frontal old/new effect, left parietal old/new effect, late frontal old/new effect, and the late posterior negativity (LPN) are impacted by MAO-A phenotype during item and source memory. Our results show that individuals with the MAO-A phenotype leading to increased transcription have lower response sensitivity during both item and source memory. Additionally, during item memory the left parietal old/new effect is not present due to increased ERP amplitude for correct rejections. The results suggest that MAO-A phenotype changes EEG correlates of recognition memory and influences how well individuals differentiate between old and new items. PMID:29487517

Sparsity enables estimation of both subcortical and cortical activity from MEG and EEG

PubMed Central

Krishnaswamy, Pavitra; Obregon-Henao, Gabriel; Ahveninen, Jyrki; Khan, Sheraz; Iglesias, Juan Eugenio; Hämäläinen, Matti S.; Purdon, Patrick L.

2017-01-01

Subcortical structures play a critical role in brain function. However, options for assessing electrophysiological activity in these structures are limited. Electromagnetic fields generated by neuronal activity in subcortical structures can be recorded noninvasively, using magnetoencephalography (MEG) and electroencephalography (EEG). However, these subcortical signals are much weaker than those generated by cortical activity. In addition, we show here that it is difficult to resolve subcortical sources because distributed cortical activity can explain the MEG and EEG patterns generated by deep sources. We then demonstrate that if the cortical activity is spatially sparse, both cortical and subcortical sources can be resolved with M/EEG. Building on this insight, we develop a hierarchical sparse inverse solution for M/EEG. We assess the performance of this algorithm on realistic simulations and auditory evoked response data, and show that thalamic and brainstem sources can be correctly estimated in the presence of cortical activity. Our work provides alternative perspectives and tools for characterizing electrophysiological activity in subcortical structures in the human brain. PMID:29138310

An EEG blind source separation algorithm based on a weak exclusion principle.

PubMed

Lan Ma; Blu, Thierry; Wang, William S-Y

2016-08-01

The question of how to separate individual brain and non-brain signals, mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings, is a significant problem in contemporary neuroscience. This study proposes and evaluates a novel EEG Blind Source Separation (BSS) algorithm based on a weak exclusion principle (WEP). The chief point in which it differs from most previous EEG BSS algorithms is that the proposed algorithm is not based upon the hypothesis that the sources are statistically independent. Our first step was to investigate algorithm performance on simulated signals which have ground truth. The purpose of this simulation is to illustrate the proposed algorithm's efficacy. The results show that the proposed algorithm has good separation performance. Then, we used the proposed algorithm to separate real EEG signals from a memory study using a revised version of Sternberg Task. The results show that the proposed algorithm can effectively separate the non-brain and brain sources.

Wake and Sleep EEG in Patients With Huntington Disease: An eLORETA Study and Review of the Literature.

PubMed

Piano, Carla; Mazzucchi, Edoardo; Bentivoglio, Anna Rita; Losurdo, Anna; Calandra Buonaura, Giovanna; Imperatori, Claudio; Cortelli, Pietro; Della Marca, Giacomo

2017-01-01

The aim of the study was to evaluate the EEG modifications in patients with Huntington disease (HD) compared with controls, by means of the exact LOw REsolution Tomography (eLORETA) software. We evaluated EEG changes during wake, non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. Moreover, we reviewed the literature concerning EEG modifications in HD. Twenty-three consecutive adult patients affected by HD were enrolled, 14 women and 9 men, mean age was 57.0 ± 12.4 years. Control subjects were healthy volunteers (mean age 58.2 ± 14.6 years). EEG and polygraphic recordings were performed during wake (before sleep) and during sleep. Sources of EEG activities were determined using the eLORETA software. In wake EEG, significant differences between patients and controls were detected in the delta frequency band (threshold T = ±4.606; P < .01) in the Brodmann areas (BAs) 3, 4, and 6 bilaterally. In NREM sleep, HD patients showed increased alpha power (T = ±4.516; P < .01) in BAs 4 and 6 bilaterally; decreased theta power (T = ±4.516; P < .01) in the BAs 23, 29, and 30; and decreased beta power (T = ±4.516; P < .01) in the left BA 30. During REM, HD patients presented decreased theta and alpha power (threshold T = ±4.640; P < .01) in the BAs 23, 29, 30, and 31 bilaterally. In conclusion, EEG data suggest a motor cortex dysfunction during wake and sleep in HD patients, which correlates with the clinical and polysomnographic evidence of increased motor activity during wake and NREM, and nearly absent motor abnormalities in REM. © EEG and Clinical Neuroscience Society (ECNS) 2016.

Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics.

PubMed

He, Bin; Sohrabpour, Abbas; Brown, Emery; Liu, Zhongming

2018-06-04

Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.

Enabling computer decisions based on EEG input.

PubMed

Culpepper, Benjamin J; Keller, Robert M

2003-12-01

Multilayer neural networks were successfully trained to classify segments of 12-channel electroencephalogram (EEG) data into one of five classes corresponding to five cognitive tasks performed by a subject. Independent component analysis (ICA) was used to segregate obvious artifact EEG components from other sources, and a frequency-band representation was used to represent the sources computed by ICA. Examples of results include an 85% accuracy rate on differentiation between two tasks, using a segment of EEG only 0.05 s long and a 95% accuracy rate using a 0.5-s-long segment.

Enabling computer decisions based on EEG input

NASA Technical Reports Server (NTRS)

Culpepper, Benjamin J.; Keller, Robert M.

2003-01-01

Multilayer neural networks were successfully trained to classify segments of 12-channel electroencephalogram (EEG) data into one of five classes corresponding to five cognitive tasks performed by a subject. Independent component analysis (ICA) was used to segregate obvious artifact EEG components from other sources, and a frequency-band representation was used to represent the sources computed by ICA. Examples of results include an 85% accuracy rate on differentiation between two tasks, using a segment of EEG only 0.05 s long and a 95% accuracy rate using a 0.5-s-long segment.

Emotion processing biases and resting EEG activity in depressed adolescents

PubMed Central

Auerbach, Randy P.; Stewart, Jeremy G.; Stanton, Colin H.; Mueller, Erik M.; Pizzagalli, Diego A.

2015-01-01

Background While theorists have posited that adolescent depression is characterized by emotion processing biases (greater propensity to identify sad than happy facial expressions), findings have been mixed. Additionally, the neural correlates associated with putative emotion processing biases remain largely unknown. Our aim was to identify emotion processing biases in depressed adolescents and examine neural abnormalities related to these biases using high-density resting EEG and source localization. Methods Healthy (n = 36) and depressed (n = 23) female adolescents, aged 13–18 years, completed a facial recognition task in which they identified happy, sad, fear, and angry expressions across intensities from 10% (low) to 100% (high). Additionally, 128-channel resting (i.e., task-free) EEG was recorded and analyzed using a distributed source localization technique (LORETA). Given research implicating the dorsolateral prefrontal cortex (DLPFC) in depression and emotion processing, analyses focused on this region. Results Relative to healthy youth, depressed adolescents were more accurate for sad and less accurate for happy, particularly low-intensity happy faces. No differences emerged for fearful or angry facial expressions. Further, LORETA analyses revealed greater theta and alpha current density (i.e., reduced brain activity) in depressed versus healthy adolescents, particularly in the left DLPFC (BA9/BA46). Theta and alpha current density were positively correlated, and greater current density predicted reduced accuracy for happy faces. Conclusion Depressed female adolescents were characterized by emotion processing biases in favor of sad emotions and reduced recognition of happiness, especially when cues of happiness were subtle. Blunted recognition of happy was associated with left DLPFC resting hypoactivity. PMID:26032684

Conforming discretizations of boundary element solutions to the electroencephalography forward problem

NASA Astrophysics Data System (ADS)

Rahmouni, Lyes; Adrian, Simon B.; Cools, Kristof; Andriulli, Francesco P.

2018-01-01

In this paper, we present a new discretization strategy for the boundary element formulation of the Electroencephalography (EEG) forward problem. Boundary integral formulations, classically solved with the Boundary Element Method (BEM), are widely used in high resolution EEG imaging because of their recognized advantages, in several real case scenarios, in terms of numerical stability and effectiveness when compared with other differential equation based techniques. Unfortunately, however, it is widely reported in literature that the accuracy of standard BEM schemes for the forward EEG problem is often limited, especially when the current source density is dipolar and its location approaches one of the brain boundary surfaces. This is a particularly limiting problem given that during an high-resolution EEG imaging procedure, several EEG forward problem solutions are required, for which the source currents are near or on top of a boundary surface. This work will first present an analysis of standardly and classically discretized EEG forward problem operators, reporting on a theoretical issue of some of the formulations that have been used so far in the community. We report on the fact that several standardly used discretizations of these formulations are consistent only with an L2-framework, requiring the expansion term to be a square integrable function (i.e., in a Petrov-Galerkin scheme with expansion and testing functions). Instead, those techniques are not consistent when a more appropriate mapping in terms of fractional-order Sobolev spaces is considered. Such a mapping allows the expansion function term to be a less regular function, thus sensibly reducing the need for mesh refinements and low-precisions handling strategies that are currently required. These more favorable mappings, however, require a different and conforming discretization, which must be suitably adapted to them. In order to appropriately fulfill this requirement, we adopt a mixed discretization based on dual boundary elements residing on a suitably defined dual mesh. We devote also a particular attention to implementation-oriented details of our new technique that will allow the rapid incorporation of our finding in one's own EEG forward solution technology. We conclude by showing how the resulting forward EEG problems show favorable properties with respect to previously proposed schemes, and we show their applicability to real-case modeling scenarios obtained from Magnetic Resonance Imaging (MRI) data. xml:lang="fr" Malheureusement, il est également reconnu dans la littérature que leur précision diminue particulièrement lorsque la source de courant est dipolaire et se situe près de la surface du cerveau. Ce défaut constitue une importante limitation, étant donné qu'au cours d'une session d'imagerie EEG à haute résolution, plusieurs solutions du problème direct EEG sont requises, pour lesquelles les sources de courant sont proches ou sur la surface de cerveau. Ce travail présente d'abord une analyse des opérateurs intervenant dans le problème direct et leur discrétisation. Nous montrons que plusieurs discrétisations couramment utilisées ne conviennent que dans un cadre L2, nécessitant que le terme d'expansion soit une fonction de carré intégrable. Dès lors, ces techniques ne sont pas cohérentes avec les propriétés spectrales des opérateurs en termes d'espaces de Sobolev d'ordre fractionnaire. Nous développons ensuite une nouvelle stratégie de discrétisation conforme aux espaces de Sobolev avec des fonctions d'expansion moins régulières, donnant lieu à une nouvelle formulation intégrale. Le solveur résultant présente des propriétés favorables par rapport aux méthodes existantes et réduit sensiblement le recours à un maillage adaptatif et autres stratégies actuellement requises pour améliorer la précision du calcul. Les résultats numériques présentés corroborent les développements théoriques et mettent en évidence l'impact positif de la nouvelle approche.

Localization of extended brain sources from EEG/MEG: the ExSo-MUSIC approach.

PubMed

Birot, Gwénaël; Albera, Laurent; Wendling, Fabrice; Merlet, Isabelle

2011-05-01

We propose a new MUSIC-like method, called 2q-ExSo-MUSIC (q ≥ 1). This method is an extension of the 2q-MUSIC (q ≥ 1) approach for solving the EEG/MEG inverse problem, when spatially-extended neocortical sources ("ExSo") are considered. It introduces a novel ExSo-MUSIC principle. The novelty is two-fold: i) the parameterization of the spatial source distribution that leads to an appropriate metric in the context of distributed brain sources and ii) the introduction of an original, efficient and low-cost way of optimizing this metric. In 2q-ExSo-MUSIC, the possible use of higher order statistics (q ≥ 2) offers a better robustness with respect to Gaussian noise of unknown spatial coherence and modeling errors. As a result we reduced the penalizing effects of both the background cerebral activity that can be seen as a Gaussian and spatially correlated noise, and the modeling errors induced by the non-exact resolution of the forward problem. Computer results on simulated EEG signals obtained with physiologically-relevant models of both the sources and the volume conductor show a highly increased performance of our 2q-ExSo-MUSIC method as compared to the classical 2q-MUSIC algorithms. Copyright © 2011 Elsevier Inc. All rights reserved.

Monitoring alert and drowsy states by modeling EEG source nonstationarity

NASA Astrophysics Data System (ADS)

Hsu, Sheng-Hsiou; Jung, Tzyy-Ping

2017-10-01

Objective. As a human brain performs various cognitive functions within ever-changing environments, states of the brain characterized by recorded brain activities such as electroencephalogram (EEG) are inevitably nonstationary. The challenges of analyzing the nonstationary EEG signals include finding neurocognitive sources that underlie different brain states and using EEG data to quantitatively assess the state changes. Approach. This study hypothesizes that brain activities under different states, e.g. levels of alertness, can be modeled as distinct compositions of statistically independent sources using independent component analysis (ICA). This study presents a framework to quantitatively assess the EEG source nonstationarity and estimate levels of alertness. The framework was tested against EEG data collected from 10 subjects performing a sustained-attention task in a driving simulator. Main results. Empirical results illustrate that EEG signals under alert versus drowsy states, indexed by reaction speeds to driving challenges, can be characterized by distinct ICA models. By quantifying the goodness-of-fit of each ICA model to the EEG data using the model deviation index (MDI), we found that MDIs were significantly correlated with the reaction speeds (r  =  -0.390 with alertness models and r  =  0.449 with drowsiness models) and the opposite correlations indicated that the two models accounted for sources in the alert and drowsy states, respectively. Based on the observed source nonstationarity, this study also proposes an online framework using a subject-specific ICA model trained with an initial (alert) state to track the level of alertness. For classification of alert against drowsy states, the proposed online framework achieved an averaged area-under-curve of 0.745 and compared favorably with a classic power-based approach. Significance. This ICA-based framework provides a new way to study changes of brain states and can be applied to monitoring cognitive or mental states of human operators in attention-critical settings or in passive brain-computer interfaces.

Levetiracetam versus phenytoin for seizure prophylaxis in severe traumatic brain injury

PubMed Central

Jones, Kristen E.; Puccio, Ava M.; Harshman, Kathy J.; Falcione, Bonnie; Benedict, Neal; Jankowitz, Brian T.; Stippler, Martina; Fischer, Michael; Sauber-Schatz, Erin K.; Fabio, Anthony; Darby, Joseph M.; Okonkwo, David O.

2013-01-01

Object Current standard of care for patients with severe traumatic brain injury (TBI) is prophylactic treatment with phenytoin for 7 days to decrease the risk of early posttraumatic seizures. Phenytoin alters drug metabolism, induces fever, and requires therapeutic-level monitoring. Alternatively, levetiracetam (Keppra) does not require serum monitoring or have significant pharmacokinetic interactions. In the current study, the authors compare the EEG findings in patients receiving phenytoin with those receiving levetiracetam monotherapy for seizure prophylaxis following severe TBI. Methods Data were prospectively collected in 32 cases in which patients received levetiracetam for the first 7 days after severe TBI and compared with data from a historical cohort of 41 cases in which patients received phenytoin monotherapy. Patients underwent 1-hour electroencephalographic (EEG) monitoring if they displayed persistent coma, decreased mental status, or clinical signs of seizures. The EEG results were grouped into normal and abnormal findings, with abnormal EEG findings further categorized as seizure activity or seizure tendency. Results Fifteen of 32 patients in the levetiracetam group warranted EEG monitoring. In 7 of these 15 cases the results were normal and in 8 abnormal; 1 patient had seizure activity, whereas 7 had seizure tendency. Twelve of 41 patients in the phenytoin group received EEG monitoring, with all results being normal. Patients treated with levetiracetam and phenytoin had equivalent incidence of seizure activity (p = 0.556). Patients receiving levetiracetam had a higher incidence of abnormal EEG findings (p = 0.003). Conclusions Levetiracetam is as effective as phenytoin in preventing early posttraumatic seizures but is associated with an increased seizure tendency on EEG analysis. PMID:18828701

Functional Connectivity Changes in Resting-State EEG as Potential Biomarker for Amyotrophic Lateral Sclerosis.

PubMed

Iyer, Parameswaran Mahadeva; Egan, Catriona; Pinto-Grau, Marta; Burke, Tom; Elamin, Marwa; Nasseroleslami, Bahman; Pender, Niall; Lalor, Edmund C; Hardiman, Orla

2015-01-01

Amyotrophic Lateral Sclerosis (ALS) is heterogeneous and overlaps with frontotemporal dementia. Spectral EEG can predict damage in structural and functional networks in frontotemporal dementia but has never been applied to ALS. 18 incident ALS patients with normal cognition and 17 age matched controls underwent 128 channel EEG and neuropsychology assessment. The EEG data was analyzed using FieldTrip software in MATLAB to calculate simple connectivity measures and scalp network measures. sLORETA was used in nodal analysis for source localization and same methods were applied as above to calculate nodal network measures. Graph theory measures were used to assess network integrity. Cross spectral density in alpha band was higher in patients. In ALS patients, increased degree values of the network nodes was noted in the central and frontal regions in the theta band across seven of the different connectivity maps (p<0.0005). Among patients, clustering coefficient in alpha and gamma bands was increased in all regions of the scalp and connectivity were significantly increased (p=0.02). Nodal network showed increased assortativity in alpha band in the patients group. The Clustering Coefficient in Partial Directed Connectivity (PDC) showed significantly higher values for patients in alpha, beta, gamma, theta and delta frequencies (p=0.05). There is increased connectivity in the fronto-central regions of the scalp and areas corresponding to Salience and Default Mode network in ALS, suggesting a pathologic disruption of neuronal networking in early disease states. Spectral EEG has potential utility as a biomarker in ALS.

Information-Theoretical Analysis of EEG Microstate Sequences in Python.

PubMed

von Wegner, Frederic; Laufs, Helmut

2018-01-01

We present an open-source Python package to compute information-theoretical quantities for electroencephalographic data. Electroencephalography (EEG) measures the electrical potential generated by the cerebral cortex and the set of spatial patterns projected by the brain's electrical potential on the scalp surface can be clustered into a set of representative maps called EEG microstates. Microstate time series are obtained by competitively fitting the microstate maps back into the EEG data set, i.e., by substituting the EEG data at a given time with the label of the microstate that has the highest similarity with the actual EEG topography. As microstate sequences consist of non-metric random variables, e.g., the letters A-D, we recently introduced information-theoretical measures to quantify these time series. In wakeful resting state EEG recordings, we found new characteristics of microstate sequences such as periodicities related to EEG frequency bands. The algorithms used are here provided as an open-source package and their use is explained in a tutorial style. The package is self-contained and the programming style is procedural, focusing on code intelligibility and easy portability. Using a sample EEG file, we demonstrate how to perform EEG microstate segmentation using the modified K-means approach, and how to compute and visualize the recently introduced information-theoretical tests and quantities. The time-lagged mutual information function is derived as a discrete symbolic alternative to the autocorrelation function for metric time series and confidence intervals are computed from Markov chain surrogate data. The software package provides an open-source extension to the existing implementations of the microstate transform and is specifically designed to analyze resting state EEG recordings.

EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome.

PubMed

Hassan, Mahmoud; Shamas, Mohamad; Khalil, Mohamad; El Falou, Wassim; Wendling, Fabrice

2015-01-01

The brain is a large-scale complex network often referred to as the "connectome". Exploring the dynamic behavior of the connectome is a challenging issue as both excellent time and space resolution is required. In this context Magneto/Electroencephalography (M/EEG) are effective neuroimaging techniques allowing for analysis of the dynamics of functional brain networks at scalp level and/or at reconstructed sources. However, a tool that can cover all the processing steps of identifying brain networks from M/EEG data is still missing. In this paper, we report a novel software package, called EEGNET, running under MATLAB (Math works, inc), and allowing for analysis and visualization of functional brain networks from M/EEG recordings. EEGNET is developed to analyze networks either at the level of scalp electrodes or at the level of reconstructed cortical sources. It includes i) Basic steps in preprocessing M/EEG signals, ii) the solution of the inverse problem to localize / reconstruct the cortical sources, iii) the computation of functional connectivity among signals collected at surface electrodes or/and time courses of reconstructed sources and iv) the computation of the network measures based on graph theory analysis. EEGNET is the unique tool that combines the M/EEG functional connectivity analysis and the computation of network measures derived from the graph theory. The first version of EEGNET is easy to use, flexible and user friendly. EEGNET is an open source tool and can be freely downloaded from this webpage: https://sites.google.com/site/eegnetworks/.

EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome

PubMed Central

Hassan, Mahmoud; Shamas, Mohamad; Khalil, Mohamad; El Falou, Wassim; Wendling, Fabrice

2015-01-01

The brain is a large-scale complex network often referred to as the “connectome”. Exploring the dynamic behavior of the connectome is a challenging issue as both excellent time and space resolution is required. In this context Magneto/Electroencephalography (M/EEG) are effective neuroimaging techniques allowing for analysis of the dynamics of functional brain networks at scalp level and/or at reconstructed sources. However, a tool that can cover all the processing steps of identifying brain networks from M/EEG data is still missing. In this paper, we report a novel software package, called EEGNET, running under MATLAB (Math works, inc), and allowing for analysis and visualization of functional brain networks from M/EEG recordings. EEGNET is developed to analyze networks either at the level of scalp electrodes or at the level of reconstructed cortical sources. It includes i) Basic steps in preprocessing M/EEG signals, ii) the solution of the inverse problem to localize / reconstruct the cortical sources, iii) the computation of functional connectivity among signals collected at surface electrodes or/and time courses of reconstructed sources and iv) the computation of the network measures based on graph theory analysis. EEGNET is the unique tool that combines the M/EEG functional connectivity analysis and the computation of network measures derived from the graph theory. The first version of EEGNET is easy to use, flexible and user friendly. EEGNET is an open source tool and can be freely downloaded from this webpage: https://sites.google.com/site/eegnetworks/. PMID:26379232

[Neurofeedback for the treatment of chronic tinnitus : Review and future perspectives].

PubMed

Kleinjung, T; Thüring, C; Güntensperger, D; Neff, P; Meyer, M

2018-03-01

Neurofeedback is a noninvasive neuromodulation technique employing real-time display of brain activity in terms of electroencephalography (EEG) signals to teach self-regulation of distinct patterns of brain activity or influence brain activity in a targeted manner. The benefit of this approach for control of symptoms in attention deficit disorders, hyperactivity, depression, and migraine has been proven. Studies in recent years have also repeatedly shown this treatment to improve tinnitus symptoms, although it has not become established as routine therapy. The primary focus of this review is the rational of EEG neurofeedback for tinnitus treatment and the currently available data from published studies. Furthermore, alternative neurofeedback protocols using real-time functional magnetic resonance imaging (fMRI) measurements for tinnitus control are considered. Finally, this article highlights how modern EEG analysis (source localization, connectivity) and the improving understanding of tinnitus pathology can contribute to development of more focused neurofeedback protocols for more sustainable control of tinnitus.

A mesostate-space model for EEG and MEG.

PubMed

Daunizeau, Jean; Friston, Karl J

2007-10-15

We present a multi-scale generative model for EEG, that entails a minimum number of assumptions about evoked brain responses, namely: (1) bioelectric activity is generated by a set of distributed sources, (2) the dynamics of these sources can be modelled as random fluctuations about a small number of mesostates, (3) mesostates evolve in a temporal structured way and are functionally connected (i.e. influence each other), and (4) the number of mesostates engaged by a cognitive task is small (e.g. between one and a few). A Variational Bayesian learning scheme is described that furnishes the posterior density on the models parameters and its evidence. Since the number of meso-sources specifies the model, the model evidence can be used to compare models and find the optimum number of meso-sources. In addition to estimating the dynamics at each cortical dipole, the mesostate-space model and its inversion provide a description of brain activity at the level of the mesostates (i.e. in terms of the dynamics of meso-sources that are distributed over dipoles). The inclusion of a mesostate level allows one to compute posterior probability maps of each dipole being active (i.e. belonging to an active mesostate). Critically, this model accommodates constraints on the number of meso-sources, while retaining the flexibility of distributed source models in explaining data. In short, it bridges the gap between standard distributed and equivalent current dipole models. Furthermore, because it is explicitly spatiotemporal, the model can embed any stochastic dynamical causal model (e.g. a neural mass model) as a Markov process prior on the mesostate dynamics. The approach is evaluated and compared to standard inverse EEG techniques, using synthetic data and real data. The results demonstrate the added-value of the mesostate-space model and its variational inversion.

Brainstorm: A User-Friendly Application for MEG/EEG Analysis

PubMed Central

Tadel, François; Baillet, Sylvain; Mosher, John C.; Pantazis, Dimitrios; Leahy, Richard M.

2011-01-01

Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI). PMID:21584256

LORETA analysis of three-dimensional distribution of δ band activity in schizophrenia: relation to negative symptoms.

PubMed

Itoh, Toru; Sumiyoshi, Tomiki; Higuchi, Yuko; Suzuki, Michio; Kawasaki, Yasuhiro

2011-08-01

We sought to determine if altered electroencephalography (EEG) activities, such as delta band activity, in specific brain regions are associated with psychotic symptoms. Data were obtained from 17 neuroleptic-naive patients with schizophrenia and age- and sex-matched 17 healthy control subjects. Low Resolution Brain Electromagnetic Tomography (LORETA) was used to generate current source density images of delta, theta, alpha, and beta activities. Localization of the difference in EEG activity between the two groups was assessed by voxel-by-voxel non-paired t-test of the LORETA images. Spearman's correlation coefficient was obtained to relate LORETA values of EEG current density in brain regions showing a significant between-group difference and psychopathology scores. Delta band activity, represented by LORETA current density, was greater for patients in the following areas; the left inferior temporal gyrus, right middle frontal gyrus, right superior frontal gyrus, right inferior frontal gyrus, and right parahippocampal gyrus. LORETA values for delta band activity in the above five brain regions were negatively correlated with negative, but not positive symptoms. The results of this study suggest the role for electrophysiological changes in some of the brain regions, e.g. prefrontal cortex, in the manifestation of negative symptoms. Copyright © 2011 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Validation of Regression-Based Myogenic Correction Techniques for Scalp and Source-Localized EEG

PubMed Central

McMenamin, Brenton W.; Shackman, Alexander J.; Maxwell, Jeffrey S.; Greischar, Lawrence L.; Davidson, Richard J.

2008-01-01

EEG and EEG source-estimation are susceptible to electromyographic artifacts (EMG) generated by the cranial muscles. EMG can mask genuine effects or masquerade as a legitimate effect - even in low frequencies, such as alpha (8–13Hz). Although regression-based correction has been used previously, only cursory attempts at validation exist and the utility for source-localized data is unknown. To address this, EEG was recorded from 17 participants while neurogenic and myogenic activity were factorially varied. We assessed the sensitivity and specificity of four regression-based techniques: between-subjects, between-subjects using difference-scores, within-subjects condition-wise, and within-subject epoch-wise on the scalp and in data modeled using the LORETA algorithm. Although within-subject epoch-wise showed superior performance on the scalp, no technique succeeded in the source-space. Aside from validating the novel epoch-wise methods on the scalp, we highlight methods requiring further development. PMID:19298626

Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization

PubMed Central

Liu, Quanying; Ganzetti, Marco; Wenderoth, Nicole; Mantini, Dante

2018-01-01

Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis. PMID:29551969

LORETA imaging of P300 in schizophrenia with individual MRI and 128-channel EEG.

PubMed

Pae, Ji Soo; Kwon, Jun Soo; Youn, Tak; Park, Hae-Jeong; Kim, Myung Sun; Lee, Boreom; Park, Kwang Suk

2003-11-01

We investigated the characteristics of P300 generators in schizophrenics by using voxel-based statistical parametric mapping of current density images. P300 generators, produced by a rare target tone of 1500 Hz (15%) under a frequent nontarget tone of 1000 Hz (85%), were measured in 20 right-handed schizophrenics and 21 controls. Low-resolution electromagnetic tomography (LORETA), using a realistic head model of the boundary element method based on individual MRI, was applied to the 128-channel EEG. Three-dimensional current density images were reconstructed from the LORETA intensity maps that covered the whole cortical gray matter. Spatial normalization and intensity normalization of the smoothed current density images were used to reduce anatomical variance and subject-specific global activity and statistical parametric mapping (SPM) was applied for the statistical analysis. We found that the sources of P300 were consistently localized at the left superior parietal area in normal subjects, while those of schizophrenics were diversely distributed. Upon statistical comparison, schizophrenics, with globally reduced current densities, showed a significant P300 current density reduction in the left medial temporal area and in the left inferior parietal area, while both left prefrontal and right orbitofrontal areas were relatively activated. The left parietotemporal area was found to correlate negatively with Positive and Negative Syndrome Scale total scores of schizophrenic patients. In conclusion, the reduced and increased areas of current density in schizophrenic patients suggest that the medial temporal and frontal areas contribute to the pathophysiology of schizophrenia, the frontotemporal circuitry abnormality.

LORETA EEG phase reset of the default mode network.

PubMed

Thatcher, Robert W; North, Duane M; Biver, Carl J

2014-01-01

The purpose of this study was to explore phase reset of 3-dimensional current sources in Brodmann areas located in the human default mode network (DMN) using Low Resolution Electromagnetic Tomography (LORETA) of the human electroencephalogram (EEG). The EEG was recorded from 19 scalp locations from 70 healthy normal subjects ranging in age from 13 to 20 years. A time point by time point computation of LORETA current sources were computed for 14 Brodmann areas comprising the DMN in the delta frequency band. The Hilbert transform of the LORETA time series was used to compute the instantaneous phase differences between all pairs of Brodmann areas. Phase shift and lock durations were calculated based on the 1st and 2nd derivatives of the time series of phase differences. Phase shift duration exhibited three discrete modes at approximately: (1) 25 ms, (2) 50 ms, and (3) 65 ms. Phase lock duration present primarily at: (1) 300-350 ms and (2) 350-450 ms. Phase shift and lock durations were inversely related and exhibited an exponential change with distance between Brodmann areas. The results are explained by local neural packing density of network hubs and an exponential decrease in connections with distance from a hub. The results are consistent with a discrete temporal model of brain function where anatomical hubs behave like a "shutter" that opens and closes at specific durations as nodes of a network giving rise to temporarily phase locked clusters of neurons for specific durations.

The New York Head—A precise standardized volume conductor model for EEG source localization and tES targeting

PubMed Central

Huang, Yu; Parra, Lucas C.; Haufe, Stefan

2018-01-01

In source localization of electroencephalograpic (EEG) signals, as well as in targeted transcranial electric current stimulation (tES), a volume conductor model is required to describe the flow of electric currents in the head. Boundary element models (BEM) can be readily computed to represent major tissue compartments, but cannot encode detailed anatomical information within compartments. Finite element models (FEM) can capture more tissue types and intricate anatomical structures, but with the higher precision also comes the need for semiautomated segmentation, and a higher computational cost. In either case, adjusting to the individual human anatomy requires costly magnetic resonance imaging (MRI), and thus head modeling is often based on the anatomy of an ‘arbitrary’ individual (e.g. Colin27). Additionally, existing reference models for the human head often do not include the cerebrospinal fluid (CSF), and their field of view excludes portions of the head and neck—two factors that demonstrably affect current-flow patterns. Here we present a highly detailed FEM, which we call ICBM-NY, or “New York Head”. It is based on the ICBM152 anatomical template (a non-linear average of the MRI of 152 adult human brains) defined in MNI coordinates, for which we extended the field of view to the neck and performed a detailed segmentation of six tissue types (scalp, skull, CSF, gray matter, white matter, air cavities) at 0.5 mm 3 resolution. The model was solved for 231 electrode locations. To evaluate its performance, additional FEMs and BEMs were constructed for four individual subjects. Each of the four individual FEMs (regarded as the ‘ground truth’) is compared to its BEM counterpart, the ICBM-NY, a BEM of the ICBM anatomy, an ‘individualized’ BEM of the ICBM anatomy warped to the individual head surface, and FEMs of the other individuals. Performance is measured in terms of EEG source localization and tES targeting errors. Results show that the ICBM-NY outperforms FEMs of mismatched individual anatomies as well as the BEM of the ICBM anatomy according to both criteria. We therefore propose the New York Head as a new standard head model to be used in future EEG and tES studies whenever an individual MRI is not available. We release all model data online at neuralengr.com/nyhead/ to facilitate broad adoption. PMID:26706450

The New York Head-A precise standardized volume conductor model for EEG source localization and tES targeting.

PubMed

Huang, Yu; Parra, Lucas C; Haufe, Stefan

2016-10-15

In source localization of electroencephalograpic (EEG) signals, as well as in targeted transcranial electric current stimulation (tES), a volume conductor model is required to describe the flow of electric currents in the head. Boundary element models (BEM) can be readily computed to represent major tissue compartments, but cannot encode detailed anatomical information within compartments. Finite element models (FEM) can capture more tissue types and intricate anatomical structures, but with the higher precision also comes the need for semi-automated segmentation, and a higher computational cost. In either case, adjusting to the individual human anatomy requires costly magnetic resonance imaging (MRI), and thus head modeling is often based on the anatomy of an 'arbitrary' individual (e.g. Colin27). Additionally, existing reference models for the human head often do not include the cerebro-spinal fluid (CSF), and their field of view excludes portions of the head and neck-two factors that demonstrably affect current-flow patterns. Here we present a highly detailed FEM, which we call ICBM-NY, or "New York Head". It is based on the ICBM152 anatomical template (a non-linear average of the MRI of 152 adult human brains) defined in MNI coordinates, for which we extended the field of view to the neck and performed a detailed segmentation of six tissue types (scalp, skull, CSF, gray matter, white matter, air cavities) at 0.5mm(3) resolution. The model was solved for 231 electrode locations. To evaluate its performance, additional FEMs and BEMs were constructed for four individual subjects. Each of the four individual FEMs (regarded as the 'ground truth') is compared to its BEM counterpart, the ICBM-NY, a BEM of the ICBM anatomy, an 'individualized' BEM of the ICBM anatomy warped to the individual head surface, and FEMs of the other individuals. Performance is measured in terms of EEG source localization and tES targeting errors. Results show that the ICBM-NY outperforms FEMs of mismatched individual anatomies as well as the BEM of the ICBM anatomy according to both criteria. We therefore propose the New York Head as a new standard head model to be used in future EEG and tES studies whenever an individual MRI is not available. We release all model data online at neuralengr.com/nyhead/ to facilitate broad adoption. Published by Elsevier Inc.

Measure Projection Analysis: A Probabilistic Approach to EEG Source Comparison and Multi-Subject Inference

PubMed Central

Bigdely-Shamlo, Nima; Mullen, Tim; Kreutz-Delgado, Kenneth; Makeig, Scott

2013-01-01

A crucial question for the analysis of multi-subject and/or multi-session electroencephalographic (EEG) data is how to combine information across multiple recordings from different subjects and/or sessions, each associated with its own set of source processes and scalp projections. Here we introduce a novel statistical method for characterizing the spatial consistency of EEG dynamics across a set of data records. Measure Projection Analysis (MPA) first finds voxels in a common template brain space at which a given dynamic measure is consistent across nearby source locations, then computes local-mean EEG measure values for this voxel subspace using a statistical model of source localization error and between-subject anatomical variation. Finally, clustering the mean measure voxel values in this locally consistent brain subspace finds brain spatial domains exhibiting distinguishable measure features and provides 3-D maps plus statistical significance estimates for each EEG measure of interest. Applied to sufficient high-quality data, the scalp projections of many maximally independent component (IC) processes contributing to recorded high-density EEG data closely match the projection of a single equivalent dipole located in or near brain cortex. We demonstrate the application of MPA to a multi-subject EEG study decomposed using independent component analysis (ICA), compare the results to k-means IC clustering in EEGLAB (sccn.ucsd.edu/eeglab), and use surrogate data to test MPA robustness. A Measure Projection Toolbox (MPT) plug-in for EEGLAB is available for download (sccn.ucsd.edu/wiki/MPT). Together, MPA and ICA allow use of EEG as a 3-D cortical imaging modality with near-cm scale spatial resolution. PMID:23370059

Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals.

PubMed

Engemann, Denis A; Gramfort, Alexandre

2015-03-01

Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain-computer interfaces (BCI) systems, in nearly all source localization methods for spatial whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data. We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for spatial whitening and source localization of MEG signals. Copyright © 2015 Elsevier Inc. All rights reserved.

Recording human cortical population spikes non-invasively--An EEG tutorial.

PubMed

Waterstraat, Gunnar; Fedele, Tommaso; Burghoff, Martin; Scheer, Hans-Jürgen; Curio, Gabriel

2015-07-30

Non-invasively recorded somatosensory high-frequency oscillations (sHFOs) evoked by electric nerve stimulation are markers of human cortical population spikes. Previously, their analysis was based on massive averaging of EEG responses. Advanced neurotechnology and optimized off-line analysis can enhance the signal-to-noise ratio of sHFOs, eventually enabling single-trial analysis. The rationale for developing dedicated low-noise EEG technology for sHFOs is unfolded. Detailed recording procedures and tailored analysis principles are explained step-by-step. Source codes in Matlab and Python are provided as supplementary material online. Combining synergistic hardware and analysis improvements, evoked sHFOs at around 600 Hz ('σ-bursts') can be studied in single-trials. Additionally, optimized spatial filters increase the signal-to-noise ratio of components at about 1 kHz ('κ-bursts') enabling their detection in non-invasive surface EEG. sHFOs offer a unique possibility to record evoked human cortical population spikes non-invasively. The experimental approaches and algorithms presented here enable also non-specialized EEG laboratories to combine measurements of conventional low-frequency EEG with the analysis of concomitant cortical population spike responses. Copyright © 2014 Elsevier B.V. All rights reserved.

Organization and Execution of Current Practices of Deployment-related Mental Health Support

DTIC Science & Technology

2011-04-01

of feedback research with electro-electroencephalography ( EEG ) signals have shown that participants can be trained to influence the characteristics...Birbaumer et al., 2006). This type of training, using brain signals, is referred to as Neurofeedback , while the use of peripheral signals is often...responses in reaction to stress like increased skin conductance, hart rate or blood pressure or certain neurological characteristics captured by EEG

Spatial and temporal EEG dynamics of dual-task driving performance

PubMed Central

2011-01-01

Background Driver distraction is a significant cause of traffic accidents. The aim of this study is to investigate Electroencephalography (EEG) dynamics in relation to distraction during driving. To study human cognition under a specific driving task, simulated real driving using virtual reality (VR)-based simulation and designed dual-task events are built, which include unexpected car deviations and mathematics questions. Methods We designed five cases with different stimulus onset asynchrony (SOA) to investigate the distraction effects between the deviations and equations. The EEG channel signals are first converted into separated brain sources by independent component analysis (ICA). Then, event-related spectral perturbation (ERSP) changes of the EEG power spectrum are used to evaluate brain dynamics in time-frequency domains. Results Power increases in the theta and beta bands are observed in relation with distraction effects in the frontal cortex. In the motor area, alpha and beta power suppressions are also observed. All of the above results are consistently observed across 15 subjects. Additionally, further analysis demonstrates that response time and multiple cortical EEG power both changed significantly with different SOA. Conclusions This study suggests that theta power increases in the frontal area is related to driver distraction and represents the strength of distraction in real-life situations. PMID:21332977

Functional Connectivity Changes in Resting-State EEG as Potential Biomarker for Amyotrophic Lateral Sclerosis

PubMed Central

Iyer, Parameswaran Mahadeva; Egan, Catriona; Pinto-Grau, Marta; Burke, Tom; Elamin, Marwa; Nasseroleslami, Bahman; Pender, Niall; Lalor, Edmund C.; Hardiman, Orla

2015-01-01

Background Amyotrophic Lateral Sclerosis (ALS) is heterogeneous and overlaps with frontotemporal dementia. Spectral EEG can predict damage in structural and functional networks in frontotemporal dementia but has never been applied to ALS. Methods 18 incident ALS patients with normal cognition and 17 age matched controls underwent 128 channel EEG and neuropsychology assessment. The EEG data was analyzed using FieldTrip software in MATLAB to calculate simple connectivity measures and scalp network measures. sLORETA was used in nodal analysis for source localization and same methods were applied as above to calculate nodal network measures. Graph theory measures were used to assess network integrity. Results Cross spectral density in alpha band was higher in patients. In ALS patients, increased degree values of the network nodes was noted in the central and frontal regions in the theta band across seven of the different connectivity maps (p<0.0005). Among patients, clustering coefficient in alpha and gamma bands was increased in all regions of the scalp and connectivity were significantly increased (p=0.02). Nodal network showed increased assortativity in alpha band in the patients group. The Clustering Coefficient in Partial Directed Connectivity (PDC) showed significantly higher values for patients in alpha, beta, gamma, theta and delta frequencies (p=0.05). Discussion There is increased connectivity in the fronto-central regions of the scalp and areas corresponding to Salience and Default Mode network in ALS, suggesting a pathologic disruption of neuronal networking in early disease states. Spectral EEG has potential utility as a biomarker in ALS. PMID:26091258

Craniux: A LabVIEW-Based Modular Software Framework for Brain-Machine Interface Research

DTIC Science & Technology

2011-01-01

open-source BMI software solu- tions are currently available, we feel that the Craniux software package fills a specific need in the realm of BMI...data, such as cortical source imaging using EEG or MEG recordings. It is with these characteristics in mind that we feel the Craniux software package...S. Adee, “Dean Kamen’s ‘luke arm’ prosthesis readies for clinical trials,” IEEE Spectrum, February 2008, http://spectrum .ieee.org/biomedical

Integrity of central nervous function in diabetes mellitus assessed by resting state EEG frequency analysis and source localization.

PubMed

Frøkjær, Jens B; Graversen, Carina; Brock, Christina; Khodayari-Rostamabad, Ahmad; Olesen, Søren S; Hansen, Tine M; Søfteland, Eirik; Simrén, Magnus; Drewes, Asbjørn M

2017-02-01

Diabetes mellitus (DM) is associated with structural and functional changes of the central nervous system. We used electroencephalography (EEG) to assess resting state cortical activity and explored associations to relevant clinical features. Multichannel resting state EEG was recorded in 27 healthy controls and 24 patients with longstanding DM and signs of autonomic dysfunction. The power distribution based on wavelet analysis was summarized into frequency bands with corresponding topographic mapping. Source localization analysis was applied to explore the electrical cortical sources underlying the EEG. Compared to controls, DM patients had an overall decreased EEG power in the delta (1-4Hz) and gamma (30-45Hz) bands. Topographic analysis revealed that these changes were confined to the frontal region for the delta band and to central cortical areas for the gamma band. Source localization analysis identified sources with reduced activity in the left postcentral gyrus for the gamma band and in right superior parietal lobule for the alpha1 (8-10Hz) band. DM patients with clinical signs of autonomic dysfunction and gastrointestinal symptoms had evidence of altered resting state cortical processing. This may reflect metabolic, vascular or neuronal changes associated with diabetes. Copyright © 2017 Elsevier Inc. All rights reserved.

Continuous electroencephalogram monitoring in the intensive care unit.

PubMed

Friedman, Daniel; Claassen, Jan; Hirsch, Lawrence J

2009-08-01

Because of recent technical advances, it is now possible to record and monitor the continuous digital electroencephalogram (EEG) of many critically ill patients simultaneously. Continuous EEG monitoring (cEEG) provides dynamic information about brain function that permits early detection of changes in neurologic status, which is especially useful when the clinical examination is limited. Nonconvulsive seizures are common in comatose critically ill patients and can have multiple negative effects on the injured brain. The majority of seizures in these patients cannot be detected without cEEG. cEEG monitoring is most commonly used to detect and guide treatment of nonconvulsive seizures, including after convulsive status epilepticus. In addition, cEEG is used to guide management of pharmacological coma for treatment of increased intracranial pressure. An emerging application for cEEG is to detect new or worsening brain ischemia in patients at high risk, especially those with subarachnoid hemorrhage. Improving quantitative EEG software is helping to make it feasible for cEEG (using full scalp coverage) to provide continuous information about changes in brain function in real time at the bedside and to alert clinicians to any acute brain event, including seizures, ischemia, increasing intracranial pressure, hemorrhage, and even systemic abnormalities affecting the brain, such as hypoxia, hypotension, acidosis, and others. Monitoring using only a few electrodes or using full scalp coverage, but without expert review of the raw EEG, must be done with extreme caution as false positives and false negatives are common. Intracranial EEG recording is being performed in a few centers to better detect seizures, ischemia, and peri-injury depolarizations, all of which may contribute to secondary injury. When cEEG is combined with individualized, physiologically driven decision making via multimodality brain monitoring, intensivists can identify when the brain is at risk for injury or when neuronal injury is already occurring and intervene before there is permanent damage. The exact role and cost-effectiveness of cEEG at the current time remains unclear, but we believe it has significant potential to improve neurologic outcomes in a variety of settings.

Variability of ICA decomposition may impact EEG signals when used to remove eyeblink artifacts

PubMed Central

PONTIFEX, MATTHEW B.; GWIZDALA, KATHRYN L.; PARKS, ANDREW C.; BILLINGER, MARTIN; BRUNNER, CLEMENS

2017-01-01

Despite the growing use of independent component analysis (ICA) algorithms for isolating and removing eyeblink-related activity from EEG data, we have limited understanding of how variability associated with ICA uncertainty may be influencing the reconstructed EEG signal after removing the eyeblink artifact components. To characterize the magnitude of this ICA uncertainty and to understand the extent to which it may influence findings within ERP and EEG investigations, ICA decompositions of EEG data from 32 college-aged young adults were repeated 30 times for three popular ICA algorithms. Following each decomposition, eyeblink components were identified and removed. The remaining components were back-projected, and the resulting clean EEG data were further used to analyze ERPs. Findings revealed that ICA uncertainty results in variation in P3 amplitude as well as variation across all EEG sampling points, but differs across ICA algorithms as a function of the spatial location of the EEG channel. This investigation highlights the potential of ICA uncertainty to introduce additional sources of variance when the data are back-projected without artifact components. Careful selection of ICA algorithms and parameters can reduce the extent to which ICA uncertainty may introduce an additional source of variance within ERP/EEG studies. PMID:28026876

The inverse electroencephalography pipeline

NASA Astrophysics Data System (ADS)

Weinstein, David Michael

The inverse electroencephalography (EEG) problem is defined as determining which regions of the brain are active based on remote measurements recorded with scalp EEG electrodes. An accurate solution to this problem would benefit both fundamental neuroscience research and clinical neuroscience applications. However, constructing accurate patient-specific inverse EEG solutions requires complex modeling, simulation, and visualization algorithms, and to date only a few systems have been developed that provide such capabilities. In this dissertation, a computational system for generating and investigating patient-specific inverse EEG solutions is introduced, and the requirements for each stage of this Inverse EEG Pipeline are defined and discussed. While the requirements of many of the stages are satisfied with existing algorithms, others have motivated research into novel modeling and simulation methods. The principal technical results of this work include novel surface-based volume modeling techniques, an efficient construction for the EEG lead field, and the Open Source release of the Inverse EEG Pipeline software for use by the bioelectric field research community. In this work, the Inverse EEG Pipeline is applied to three research problems in neurology: comparing focal and distributed source imaging algorithms; separating measurements into independent activation components for multifocal epilepsy; and localizing the cortical activity that produces the P300 effect in schizophrenia.

Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives

PubMed Central

Yuan, Han; He, Bin

2014-01-01

Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems extract specific features of brain activity and translate them into control signals that drive an output. Recently, a category of BCIs that are built on the rhythmic activity recorded over the sensorimotor cortex, i.e. the sensorimotor rhythm (SMR), has attracted considerable attention among the BCIs that use noninvasive neural recordings, e.g. electroencephalography (EEG), and have demonstrated the capability of multi-dimensional prosthesis control. This article reviews the current state and future perspectives of SMR-based BCI and its clinical applications, in particular focusing on the EEG SMR. The characteristic features of SMR from the human brain are described and their underlying neural sources are discussed. The functional components of SMR-based BCI, together with its current clinical applications are reviewed. Lastly, limitations of SMR-BCIs and future outlooks are also discussed. PMID:24759276

Paying attention to attention in recognition memory: insights from models and electrophysiology.

PubMed

Dubé, Chad; Payne, Lisa; Sekuler, Robert; Rotello, Caren M

2013-12-01

Reliance on remembered facts or events requires memory for their sources, that is, the contexts in which those facts or events were embedded. Understanding of source retrieval has been stymied by the fact that uncontrolled fluctuations of attention during encoding can cloud results of key importance to theoretical development. To address this issue, we combined electrophysiology (high-density electroencephalogram, EEG, recordings) with computational modeling of behavioral results. We manipulated subjects' attention to an auditory attribute, whether the source of individual study words was a male or female speaker. Posterior alpha-band (8-14 Hz) power in subjects' EEG increased after a cue to ignore the voice of the person who was about to speak. Receiver-operating-characteristic analysis validated our interpretation of oscillatory dynamics as a marker of attention to source information. With attention under experimental control, computational modeling showed unequivocally that memory for source (male or female speaker) reflected a continuous signal detection process rather than a threshold recollection process.

Methods for artifact detection and removal from scalp EEG: A review.

PubMed

Islam, Md Kafiul; Rastegarnia, Amir; Yang, Zhi

2016-11-01

Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Bedside functional brain imaging in critically-ill children using high-density EEG source modeling and multi-modal sensory stimulation.

PubMed

Eytan, Danny; Pang, Elizabeth W; Doesburg, Sam M; Nenadovic, Vera; Gavrilovic, Bojan; Laussen, Peter; Guerguerian, Anne-Marie

2016-01-01

Acute brain injury is a common cause of death and critical illness in children and young adults. Fundamental management focuses on early characterization of the extent of injury and optimizing recovery by preventing secondary damage during the days following the primary injury. Currently, bedside technology for measuring neurological function is mainly limited to using electroencephalography (EEG) for detection of seizures and encephalopathic features, and evoked potentials. We present a proof of concept study in patients with acute brain injury in the intensive care setting, featuring a bedside functional imaging set-up designed to map cortical brain activation patterns by combining high density EEG recordings, multi-modal sensory stimulation (auditory, visual, and somatosensory), and EEG source modeling. Use of source-modeling allows for examination of spatiotemporal activation patterns at the cortical region level as opposed to the traditional scalp potential maps. The application of this system in both healthy and brain-injured participants is demonstrated with modality-specific source-reconstructed cortical activation patterns. By combining stimulation obtained with different modalities, most of the cortical surface can be monitored for changes in functional activation without having to physically transport the subject to an imaging suite. The results in patients in an intensive care setting with anatomically well-defined brain lesions suggest a topographic association between their injuries and activation patterns. Moreover, we report the reproducible application of a protocol examining a higher-level cortical processing with an auditory oddball paradigm involving presentation of the patient's own name. This study reports the first successful application of a bedside functional brain mapping tool in the intensive care setting. This application has the potential to provide clinicians with an additional dimension of information to manage critically-ill children and adults, and potentially patients not suited for magnetic resonance imaging technologies.

Biomarkers for visceral hypersensitivity identified by classification of electroencephalographic frequency alterations

NASA Astrophysics Data System (ADS)

Graversen, Carina; Brock, Christina; Mohr Drewes, Asbjørn; Farina, Dario

2011-10-01

Abdominal pain is frequently related to visceral hypersensitivity. This is associated with increased neuronal excitability in the central nervous system (CNS), which can be manifested as discrete electroencephalographic (EEG) alterations. In the current placebo-controlled study, visceral hypersensitivity was evoked by chemical irritation of the esophagus with acid and capsaicin perfusion. The resulting hyperexcitability of the CNS was evaluated by evoked brain potentials following painful electrical stimulations of a remote organ—the rectosigmoid colon. Alterations in individual EEG power distributions between baseline and after perfusion were quantified by extracting features from the evoked brain potentials using an optimized discrete wavelet transform. Visceral hypersensitivity was identified as increased EEG power in the delta, theta and alpha frequency bands. By applying a support vector machine in regression mode, the individual baseline corrected alterations after sensitization were discriminated from alterations caused by placebo perfusions. An accuracy of 91.7% was obtained (P < 0.01). The regression value representing the overall alteration of the EEG correlated with the degree of hyperalgesia (P = 0.03). In conclusion, this study showed that classification of EEG can be used to detect biomarkers reflecting central neuronal changes. In the future, this may be used in studies of pain physiology and pharmacological interventions.

[EEG frequency and regional properties in patients with paranoid schizophrenia: effects of positive and negative symptomatology prevalence].

PubMed

Bochkarev, V K; Kirenskaya, A V; Tkachenko, A A; Samylkin, D V; Novototsky-Vlasov, V Yu; Kovaleva, M E

2015-01-01

EEG changes in schizophrenic patients are caused by a multitude of factors related to clinical heterogeneity of the disease, current state of patients, and conducted therapy. EEG spectral analysis remains an actual methodical approach for the investigation of the neurophysiological mechanisms of the disease. The goal of the investigation was the study of frequency and regional EEG correlating with the intensity of productive and negative disorders. Models of summary prevalence of positive/negative disorders and evidence of concrete clinical indices of the PANSS scale were used. Spectral characteristics of background EEG in the frequency range of 1-60 Hz were studied in 35 patients with paranoid schizophrenia free from psychoactive medication and in 19 healthy volunteers. It was established that the main index of negative symptomatology in summary assessment was diffuse increase of spectral power of gamma and delta ranges. Deficient states with the predominance of volitional disorders were characterized by a lateralized increase of spectral power of beta-gamma ranges in the left hemisphere, and of delta range - in frontal areas of this hemisphere. Positive symptomatology was noticeably less reflected in EEG changes than negative ones. An analysis of psychopathological symptom complexes revealed the significance of spatially structured EEG patterns in the beta range: for the delusion disturbances with psychic automatism phenomena - in frontal areas of the left hemisphere, and for the paranoid syndrome with primary interpretative delusion - in cortical areas of the right hemisphere.

Rapidly Learned Identification of Epileptic Seizures from Sonified EEG

PubMed Central

Loui, Psyche; Koplin-Green, Matan; Frick, Mark; Massone, Michael

2014-01-01

Sonification refers to a process by which data are converted into sound, providing an auditory alternative to visual display. Currently, the prevalent method for diagnosing seizures in epilepsy is by visually reading a patient’s electroencephalogram (EEG). However, sonification of the EEG data provides certain advantages due to the nature of human auditory perception. We hypothesized that human listeners will be able to identify seizures from EEGs using the auditory modality alone, and that accuracy of seizure identification will increase after a short training session. Here, we describe an algorithm that we have used to sonify EEGs of both seizure and non-seizure activity, followed by a training study in which subjects listened to short clips of sonified EEGs and determined whether each clip was of seizure or normal activity, both before and after a short training session. Results show that before training subjects performed at chance level in differentiating seizures from non-seizures, but there was a significant improvement of accuracy after the training session. After training, subjects successfully distinguished seizures from non-seizures using the auditory modality alone. Further analyses using signal detection theory demonstrated improvement in sensitivity and reduction in response bias as a result of training. This study demonstrates the potential of sonified EEGs to be used for the detection of seizures. Future studies will attempt to increase accuracy using novel training and sonification modifications, with the goals of managing, predicting, and ultimately controlling seizures using sonification as a possible biofeedback-based intervention for epilepsy. PMID:25352802

The effect of CPAP treatment on EEG of OSAS patients.

PubMed

Zhang, Cheng; Lv, Jun; Zhou, Junhong; Su, Li; Feng, Liping; Ma, Jing; Wang, Guangfa; Zhang, Jue

2015-12-01

Continuous positive airway pressure (CPAP) is currently the most effective treatment method for obstructive sleep apnea syndrome (OSAS). The purpose of this study was to compare the sleep electroencephalogram (EEG) changes before and after the application of CPAP to OSAS patients. A retrospective study was conducted and 45 sequential patients who received both polysomnography (PSG) and CPAP titration were included. The raw data of sleep EEG were extracted and analyzed by engineers using two main factors: fractal dimension (FD) and the zero-crossing rate of detrended FD (zDFD). FD was an effective indicator reflecting the EEG complexity and zDFD was useful to reflect the variability of the EEG complexity. The FD and zDFD indexes of sleep EEG of 45 OSAS patients before and after CPAP titration were analyzed. The age of 45 OSAS patients was 52.7 ± 5.6 years old and the patients include 12 females and 33 males. After CPAP treatment, FD of EEG in non-rapid eye movement (NREM) sleep decreased significantly (P < 0.05), while FD of EEG increased in rapid eye movement (REM) sleep (P < 0.05). Meanwhile, zDFD were decreased remarkably in both NREM and REM sleep after CPAP therapy (P < 0.05, respectively). CPAP therapy had a significant influence on sleep EEG in patients with OSAHS, which lead to a more stable EEG pattern. This may be one of the mechanisms that CPAP could improve sleep quality and brain function of OSAS patients.

Hyperedge bundling: A practical solution to spurious interactions in MEG/EEG source connectivity analyses.

PubMed

Wang, Sheng H; Lobier, Muriel; Siebenhühner, Felix; Puoliväli, Tuomas; Palva, Satu; Palva, J Matias

2018-06-01

Inter-areal functional connectivity (FC), neuronal synchronization in particular, is thought to constitute a key systems-level mechanism for coordination of neuronal processing and communication between brain regions. Evidence to support this hypothesis has been gained largely using invasive electrophysiological approaches. In humans, neuronal activity can be non-invasively recorded only with magneto- and electroencephalography (MEG/EEG), which have been used to assess FC networks with high temporal resolution and whole-scalp coverage. However, even in source-reconstructed MEG/EEG data, signal mixing, or "source leakage", is a significant confounder for FC analyses and network localization. Signal mixing leads to two distinct kinds of false-positive observations: artificial interactions (AI) caused directly by mixing and spurious interactions (SI) arising indirectly from the spread of signals from true interacting sources to nearby false loci. To date, several interaction metrics have been developed to solve the AI problem, but the SI problem has remained largely intractable in MEG/EEG all-to-all source connectivity studies. Here, we advance a novel approach for correcting SIs in FC analyses using source-reconstructed MEG/EEG data. Our approach is to bundle observed FC connections into hyperedges by their adjacency in signal mixing. Using realistic simulations, we show here that bundling yields hyperedges with good separability of true positives and little loss in the true positive rate. Hyperedge bundling thus significantly decreases graph noise by minimizing the false-positive to true-positive ratio. Finally, we demonstrate the advantage of edge bundling in the visualization of large-scale cortical networks with real MEG data. We propose that hypergraphs yielded by bundling represent well the set of true cortical interactions that are detectable and dissociable in MEG/EEG connectivity analysis. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Human brain networks in physiological aging: a graph theoretical analysis of cortical connectivity from EEG data.

PubMed

Vecchio, Fabrizio; Miraglia, Francesca; Bramanti, Placido; Rossini, Paolo Maria

2014-01-01

Modern analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity. To test the hypothesis that aging processes modulate the brain connectivity network, EEG recording was conducted on 113 healthy volunteers. They were divided into three groups in accordance with their ages: 36 Young (15-45 years), 46 Adult (50-70 years), and 31 Elderly (>70 years). To evaluate the stability of the investigated parameters, a subgroup of 10 subjects underwent a second EEG recording two weeks later. Graph theory functions were applied to the undirected and weighted networks obtained by the lagged linear coherence evaluated by eLORETA on cortical sources. EEG frequency bands of interest were: delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz). The spectral connectivity analysis of cortical sources showed that the normalized Characteristic Path Length (λ) presented the pattern Young > Adult>Elderly in the higher alpha band. Elderly also showed a greater increase in delta and theta bands than Young. The correlation between age and λ showed that higher ages corresponded to higher λ in delta and theta and lower in the alpha2 band; this pattern reflects the age-related modulation of higher (alpha) and decreased (delta) connectivity. The Normalized Clustering coefficient (γ) and small-world network modeling (σ) showed non-significant age-modulation. Evidence from the present study suggests that graph theory can aid in the analysis of connectivity patterns estimated from EEG and can facilitate the study of the physiological and pathological brain aging features of functional connectivity networks.

Transcranial Electrical Currents to Probe EEG Brain Rhythms and Memory Consolidation during Sleep in Humans

PubMed Central

Marshall, Lisa; Kirov, Roumen; Brade, Julian; Mölle, Matthias; Born, Jan

2011-01-01

Previously the application of a weak electric anodal current oscillating with a frequency of the sleep slow oscillation (∼0.75 Hz) during non-rapid eye movement sleep (NonREM) sleep boosted endogenous slow oscillation activity and enhanced sleep-associated memory consolidation. The slow oscillations occurring during NonREM sleep and theta oscillations present during REM sleep have been considered of critical relevance for memory formation. Here transcranial direct current stimulation (tDCS) oscillating at 5 Hz, i.e., within the theta frequency range (theta-tDCS) is applied during NonREM and REM sleep. Theta-tDCS during NonREM sleep produced a global decrease in slow oscillatory activity conjoint with a local reduction of frontal slow EEG spindle power (8–12 Hz) and a decrement in consolidation of declarative memory, underlining the relevance of these cortical oscillations for sleep-dependent memory consolidation. In contrast, during REM sleep theta-tDCS appears to increase global gamma (25–45 Hz) activity, indicating a clear brain state-dependency of theta-tDCS. More generally, results demonstrate the suitability of oscillating-tDCS as a tool to analyze functions of endogenous EEG rhythms and underlying endogenous electric fields as well as the interactions between EEG rhythms of different frequencies. PMID:21340034

ARTIST: A fully automated artifact rejection algorithm for single-pulse TMS-EEG data.

PubMed

Wu, Wei; Keller, Corey J; Rogasch, Nigel C; Longwell, Parker; Shpigel, Emmanuel; Rolle, Camarin E; Etkin, Amit

2018-04-01

Concurrent single-pulse TMS-EEG (spTMS-EEG) is an emerging noninvasive tool for probing causal brain dynamics in humans. However, in addition to the common artifacts in standard EEG data, spTMS-EEG data suffer from enormous stimulation-induced artifacts, posing significant challenges to the extraction of neural information. Typically, neural signals are analyzed after a manual time-intensive and often subjective process of artifact rejection. Here we describe a fully automated algorithm for spTMS-EEG artifact rejection. A key step of this algorithm is to decompose the spTMS-EEG data into statistically independent components (ICs), and then train a pattern classifier to automatically identify artifact components based on knowledge of the spatio-temporal profile of both neural and artefactual activities. The autocleaned and hand-cleaned data yield qualitatively similar group evoked potential waveforms. The algorithm achieves a 95% IC classification accuracy referenced to expert artifact rejection performance, and does so across a large number of spTMS-EEG data sets (n = 90 stimulation sites), retains high accuracy across stimulation sites/subjects/populations/montages, and outperforms current automated algorithms. Moreover, the algorithm was superior to the artifact rejection performance of relatively novice individuals, who would be the likely users of spTMS-EEG as the technique becomes more broadly disseminated. In summary, our algorithm provides an automated, fast, objective, and accurate method for cleaning spTMS-EEG data, which can increase the utility of TMS-EEG in both clinical and basic neuroscience settings. © 2018 Wiley Periodicals, Inc.

Speech Presentation Cues Moderate Frontal EEG Asymmetry in Socially Withdrawn Young Adults

PubMed Central

Cole, Claire; Zapp, Daniel J.; Nelson, S. Katherine; Pérez-Edgar, Koraly

2011-01-01

Socially withdrawn individuals display solitary behavior across wide contexts with both unfamiliar and familiar peers. This tendency to withdraw may be driven by either past or anticipated negative social encounters. In addition, socially withdrawn individuals often exhibit right frontal electroencephalogram (EEG) asymmetry at baseline and when under stress. In the current study we examined shifts in frontal EEG activity in young adults (N=41) at baseline, as they viewed either an anxiety-provoking or a benign speech video, and as they subsequently prepared for their own speech. Results indicated that right frontal EEG activity increased, relative to the left, only for socially withdrawn participants exposed to the anxious video. These results suggest that contextual affective cues may prime an individual’s response to stress, particularly if they illustrate or substantiate an anticipated negative event. PMID:22169714

Can the Psycho-Emotional State be Optimized by Regular Use of Positive Imagery?, Psychological and Electroencephalographic Study of Self-Guided Training

PubMed Central

Velikova, Svetla; Sjaaheim, Haldor; Nordtug, Bente

2017-01-01

The guided imagery training is considered as an effective method and therefore widely used in modern cognitive psychotherapy, while less is known about the effectiveness of self-guided. The present study investigated the effects of regular use of self-guided positive imagery, applying both subjective (assessment of the psycho-emotional state) and objective (electroencephalographic, EEG) approaches to research. Thirty healthy subjects participated in the cognitive imagery-training program for 12 weeks. The schedule began with group training with an instructor for 2 days, where the participants learned various techniques of positive imagery, after which they continued their individual training at home. Psychological and EEG evaluations were applied at the baseline and at the end of the training period. The impact of training on the psycho-emotional states of the participants was evaluated through: Center for epidemiologic studies- Depression (CES-D) 20 item scale, Satisfaction with life scale (SWLS) and General Self-Efficacy scale (GSE). EEGs (19-channels) were recorded at rest with eyes closed. EEG analysis was performed using Low resolution electromagnetic tomography (LORETA) software that allows the comparison of current source density (CSD) and functional connectivity (lagged phase and coherence) in the default mode network before and after a workout. Initial assessment with CES-D indicated that 22 participants had subthreshold depression. After the training participants had less prominent depressive symptoms (CES-D, p = 0.002), were more satisfied with their lives (SWLS, p = 0.036), and also evaluated themselves as more effective (GSE, p = 0.0002). LORETA source analysis revealed an increase in the CSD in the right mPFC (Brodmann area 10) for beta-2 band after training (p = 0.038). LORETA connectivity analysis demonstrated an increase in lagged coherence between temporal gyruses of both hemispheres in the delta band, as well as between the Posterior cingulate cortex and right BA21 in the theta band after a workout. Since mPFC is involved in emotional regulation, functional changes in this region can be seen in line with the results of psychological tests and their objective validation. A possible activation of GAMK-ergic system is discussed. Self-guided positive imagery (after instructions) can be helpful for emotional selfregulation in healthy subjects and has the potential to be useful in subthreshold depression. PMID:28127281

Multimodal effective connectivity analysis reveals seizure focus and propagation in musicogenic epilepsy.

PubMed

Klamer, Silke; Rona, Sabine; Elshahabi, Adham; Lerche, Holger; Braun, Christoph; Honegger, Jürgen; Erb, Michael; Focke, Niels K

2015-06-01

Dynamic causal modeling (DCM) is a method to non-invasively assess effective connectivity between brain regions. 'Musicogenic epilepsy' is a rare reflex epilepsy syndrome in which seizures can be elicited by musical stimuli and thus represents a unique possibility to investigate complex human brain networks and test connectivity analysis tools. We investigated effective connectivity in a case of musicogenic epilepsy using DCM for fMRI, high-density (hd-) EEG and MEG and validated results with intracranial EEG recordings. A patient with musicogenic seizures was examined using hd-EEG/fMRI and simultaneous '256-channel hd-EEG'/'whole head MEG' to characterize the epileptogenic focus and propagation effects using source analysis techniques and DCM. Results were validated with invasive EEG recordings. We recorded one seizure with hd-EEG/fMRI and four auras with hd-EEG/MEG. During the seizures, increases of activity could be observed in the right mesial temporal region as well as bilateral mesial frontal regions. Effective connectivity analysis of fMRI and hd-EEG/MEG indicated that right mesial temporal neuronal activity drives changes in the frontal areas consistently in all three modalities, which was confirmed by the results of invasive EEG recordings. Seizures thus seem to originate in the right mesial temporal lobe and propagate to mesial frontal regions. Using DCM for fMRI, hd-EEG and MEG we were able to correctly localize focus and propagation of epileptic activity and thereby characterize the underlying epileptic network in a patient with musicogenic epilepsy. The concordance between all three functional modalities validated by invasive monitoring is noteworthy, both for epileptic activity spread as well as for effective connectivity analysis in general. Copyright © 2015 Elsevier Inc. All rights reserved.

High-resolution EEG techniques for brain-computer interface applications.

PubMed

Cincotti, Febo; Mattia, Donatella; Aloise, Fabio; Bufalari, Simona; Astolfi, Laura; De Vico Fallani, Fabrizio; Tocci, Andrea; Bianchi, Luigi; Marciani, Maria Grazia; Gao, Shangkai; Millan, Jose; Babiloni, Fabio

2008-01-15

High-resolution electroencephalographic (HREEG) techniques allow estimation of cortical activity based on non-invasive scalp potential measurements, using appropriate models of volume conduction and of neuroelectrical sources. In this study we propose an application of this body of technologies, originally developed to obtain functional images of the brain's electrical activity, in the context of brain-computer interfaces (BCI). Our working hypothesis predicted that, since HREEG pre-processing removes spatial correlation introduced by current conduction in the head structures, by providing the BCI with waveforms that are mostly due to the unmixed activity of a small cortical region, a more reliable classification would be obtained, at least when the activity to detect has a limited generator, which is the case in motor related tasks. HREEG techniques employed in this study rely on (i) individual head models derived from anatomical magnetic resonance images, (ii) distributed source model, composed of a layer of current dipoles, geometrically constrained to the cortical mantle, (iii) depth-weighted minimum L(2)-norm constraint and Tikhonov regularization for linear inverse problem solution and (iv) estimation of electrical activity in cortical regions of interest corresponding to relevant Brodmann areas. Six subjects were trained to learn self modulation of sensorimotor EEG rhythms, related to the imagination of limb movements. Off-line EEG data was used to estimate waveforms of cortical activity (cortical current density, CCD) on selected regions of interest. CCD waveforms were fed into the BCI computational pipeline as an alternative to raw EEG signals; spectral features are evaluated through statistical tests (r(2) analysis), to quantify their reliability for BCI control. These results are compared, within subjects, to analogous results obtained without HREEG techniques. The processing procedure was designed in such a way that computations could be split into a setup phase (which includes most of the computational burden) and the actual EEG processing phase, which was limited to a single matrix multiplication. This separation allowed to make the procedure suitable for on-line utilization, and a pilot experiment was performed. Results show that lateralization of electrical activity, which is expected to be contralateral to the imagined movement, is more evident on the estimated CCDs than in the scalp potentials. CCDs produce a pattern of relevant spectral features that is more spatially focused, and has a higher statistical significance (EEG: 0.20+/-0.114 S.D.; CCD: 0.55+/-0.16 S.D.; p=10(-5)). A pilot experiment showed that a trained subject could utilize voluntary modulation of estimated CCDs for accurate (eight targets) on-line control of a cursor. This study showed that it is practically feasible to utilize HREEG techniques for on-line operation of a BCI system; off-line analysis suggests that accuracy of BCI control is enhanced by the proposed method.

Three-dimensional localization of abnormal EEG activity in migraine: a low resolution electromagnetic tomography (LORETA) study of migraine patients in the pain-free interval.

PubMed

Clemens, Béla; Bánk, József; Piros, Pálma; Bessenyei, Mónika; Veto, Sára; Tóth, Márton; Kondákor, István

2008-09-01

Investigating the brain of migraine patients in the pain-free interval may shed light on the basic cerebral abnormality of migraine, in other words, the liability of the brain to generate migraine attacks from time to time. Twenty unmedicated "migraine without aura" patients and a matched group of healthy controls were investigated in this explorative study. 19-channel EEG was recorded against the linked ears reference and was on-line digitized. 60 x 2-s epochs of eyes-closed, waking-relaxed activity were subjected to spectral analysis and a source localization method, low resolution electromagnetic tomography (LORETA). Absolute power was computed for 19 electrodes and four frequency bands (delta: 1.5-3.5 Hz, theta: 4.0-7.5 Hz, alpha: 8.0-12.5 Hz, beta: 13.0-25.0 Hz). LORETA "activity" (=current source density, ampers/meters squared) was computed for 2394 voxels and the above specified frequency bands. Group comparison was carried out for the specified quantitative EEG variables. Activity in the two groups was compared on a voxel-by-voxel basis for each frequency band. Statistically significant (uncorrected P < 0.01) group differences were projected to cortical anatomy. Spectral findings: there was a tendency for more alpha power in the migraine that in the control group in all but two (F4, C3) derivations. However, statistically significant (P < 0.01, Bonferroni-corrected) spectral difference was only found in the right occipital region. The main LORETA-finding was that voxels with P < 0.01 differences were crowded in anatomically contiguous cortical areas. Increased alpha activity was found in a cortical area including part of the precuneus, and the posterior part of the middle temporal gyrus in the right hemisphere. Decreased alpha activity was found bilaterally in medial parts of the frontal cortex including the anterior cingulate and the superior and medial frontal gyri. Neither spectral analysis, nor LORETA revealed statistically significant differences in the delta, theta, and beta bands. LORETA revealed the anatomical distribution of the cortical sources (generators) of the EEG abnormalities in migraine. The findings characterize the state of the cerebral cortex in the pain-free interval and might be suitable for planning forthcoming investigations.

Source localization of temporal lobe epilepsy using PCA-LORETA analysis on ictal EEG recordings.

PubMed

Stern, Yaki; Neufeld, Miriam Y; Kipervasser, Svetlana; Zilberstein, Amir; Fried, Itzhak; Teicher, Mina; Adi-Japha, Esther

2009-04-01

Localizing the source of an epileptic seizure using noninvasive EEG suffers from inaccuracies produced by other generators not related to the epileptic source. The authors isolated the ictal epileptic activity, and applied a source localization algorithm to identify its estimated location. Ten ictal EEG scalp recordings from five different patients were analyzed. The patients were known to have temporal lobe epilepsy with a single epileptic focus that had a concordant MRI lesion. The patients had become seizure-free following partial temporal lobectomy. A midinterval (approximately 5 seconds) period of ictal activity was used for Principal Component Analysis starting at ictal onset. The level of epileptic activity at each electrode (i.e., the eigenvector of the component that manifest epileptic characteristic), was used as an input for low-resolution tomography analysis for EEG inverse solution (Zilberstain et al., 2004). The algorithm accurately and robustly identified the epileptic focus in these patients. Principal component analysis and source localization methods can be used in the future to monitor the progression of an epileptic seizure and its expansion to other areas.

Activation of the serotonergic system by pedaling exercise changes anterior cingulate cortex activity and improves negative emotion.

PubMed

Ohmatsu, Satoko; Nakano, Hideki; Tominaga, Takanori; Terakawa, Yuzo; Murata, Takaho; Morioka, Shu

2014-08-15

Pedaling exercise (PE) of moderate intensity has been shown to ease anxiety and discomfort; however, little is known of the changes that occur in brain activities and in the serotonergic (5-HT) system after PE. Therefore, this study was conducted for the following reasons: (1) to localize the changes in the brain activities induced by PE using a distributed source localization algorithm, (2) to examine the changes in frontal asymmetry, as used in the Davidson model, with electroencephalography (EEG) activity, and (3) to examine the effect of PE on the 5-HT system. A 32-channel EEG was used to record before and after PE. Profile of Mood States tests indicated that there was a significant decrease in tension-anxiety and a significant increase in vigor after PE. A standardized low-resolution brain electromagnetic tomography analysis showed a significant decrease in brain activities after PE in the alpha-2 band (10-12.5 Hz) in the anterior cingulate cortex (ACC). Moreover, a significant increase in frontal EEG asymmetry was observed after PE in the alpha-1 band (7.5-10 Hz). Urine 5-HT levels significantly increased after PE. Urine 5-HT levels positively correlated with the degree of frontal EEG asymmetry in the alpha-1 band and negatively correlated with brain activity in ACC. Our results suggested that PE activates the 5-HT system and consequently induces increases in frontal EEG asymmetry in the alpha-1 band and reductions of brain activity in the alpha-2 band in the ACC region. Copyright © 2014 Elsevier B.V. All rights reserved.

EEG source localization: Sensor density and head surface coverage.

PubMed

Song, Jasmine; Davey, Colin; Poulsen, Catherine; Luu, Phan; Turovets, Sergei; Anderson, Erik; Li, Kai; Tucker, Don

2015-12-30

The accuracy of EEG source localization depends on a sufficient sampling of the surface potential field, an accurate conducting volume estimation (head model), and a suitable and well-understood inverse technique. The goal of the present study is to examine the effect of sampling density and coverage on the ability to accurately localize sources, using common linear inverse weight techniques, at different depths. Several inverse methods are examined, using the popular head conductivity. Simulation studies were employed to examine the effect of spatial sampling of the potential field at the head surface, in terms of sensor density and coverage of the inferior and superior head regions. In addition, the effects of sensor density and coverage are investigated in the source localization of epileptiform EEG. Greater sensor density improves source localization accuracy. Moreover, across all sampling density and inverse methods, adding samples on the inferior surface improves the accuracy of source estimates at all depths. More accurate source localization of EEG data can be achieved with high spatial sampling of the head surface electrodes. The most accurate source localization is obtained when the voltage surface is densely sampled over both the superior and inferior surfaces. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Forward and inverse effects of the complete electrode model in neonatal EEG

PubMed Central

Lew, S.; Wolters, C. H.

2016-01-01

This paper investigates finite element method-based modeling in the context of neonatal electroencephalography (EEG). In particular, the focus lies on electrode boundary conditions. We compare the complete electrode model (CEM) with the point electrode model (PEM), which is the current standard in EEG. In the CEM, the voltage experienced by an electrode is modeled more realistically as the integral average of the potential distribution over its contact surface, whereas the PEM relies on a point value. Consequently, the CEM takes into account the subelectrode shunting currents, which are absent in the PEM. In this study, we aim to find out how the electrode voltage predicted by these two models differ, if standard size electrodes are attached to a head of a neonate. Additionally, we study voltages and voltage variation on electrode surfaces with two source locations: 1) next to the C6 electrode and 2) directly under the Fz electrode and the frontal fontanel. A realistic model of a neonatal head, including a skull with fontanels and sutures, is used. Based on the results, the forward simulation differences between CEM and PEM are in general small, but significant outliers can occur in the vicinity of the electrodes. The CEM can be considered as an integral part of the outer head model. The outcome of this study helps understanding volume conduction of neonatal EEG, since it enlightens the role of advanced skull and electrode modeling in forward and inverse computations. NEW & NOTEWORTHY The effect of the complete electrode model on electroencephalography forward and inverse computations is explored. A realistic neonatal head model, including a skull structure with fontanels and sutures, is used. The electrode and skull modeling differences are analyzed and compared with each other. The results suggest that the complete electrode model can be considered as an integral part of the outer head model. To achieve optimal source localization results, accurate electrode modeling might be necessary. PMID:27852731

On the feasibility of concurrent human TMS-EEG-fMRI measurements

PubMed Central

Reithler, Joel; Schuhmann, Teresa; de Graaf, Tom; Uludağ, Kâmil; Goebel, Rainer; Sack, Alexander T.

2013-01-01

Simultaneously combining the complementary assets of EEG, functional MRI (fMRI), and transcranial magnetic stimulation (TMS) within one experimental session provides synergetic results, offering insights into brain function that go beyond the scope of each method when used in isolation. The steady increase of concurrent EEG-fMRI, TMS-EEG, and TMS-fMRI studies further underlines the added value of such multimodal imaging approaches. Whereas concurrent EEG-fMRI enables monitoring of brain-wide network dynamics with high temporal and spatial resolution, the combination with TMS provides insights in causal interactions within these networks. Thus the simultaneous use of all three methods would allow studying fast, spatially accurate, and distributed causal interactions in the perturbed system and its functional relevance for intact behavior. Concurrent EEG-fMRI, TMS-EEG, and TMS-fMRI experiments are already technically challenging, and the three-way combination of TMS-EEG-fMRI might yield additional difficulties in terms of hardware strain or signal quality. The present study explored the feasibility of concurrent TMS-EEG-fMRI studies by performing safety and quality assurance tests based on phantom and human data combining existing commercially available hardware. Results revealed that combined TMS-EEG-fMRI measurements were technically feasible, safe in terms of induced temperature changes, allowed functional MRI acquisition with comparable image quality as during concurrent EEG-fMRI or TMS-fMRI, and provided artifact-free EEG before and from 300 ms after TMS pulse application. Based on these empirical findings, we discuss the conceptual benefits of this novel complementary approach to investigate the working human brain and list a number of precautions and caveats to be heeded when setting up such multimodal imaging facilities with current hardware. PMID:23221407

Emotion Recognition from Single-Trial EEG Based on Kernel Fisher's Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine

PubMed Central

Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

2014-01-01

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods. PMID:25061837

Emotion recognition from single-trial EEG based on kernel Fisher's emotion pattern and imbalanced quasiconformal kernel support vector machine.

PubMed

Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

2014-07-24

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.

EEG Driven tDCS Versus Bifrontal tDCS for Tinnitus

PubMed Central

De Ridder, Dirk; Vanneste, Sven

2012-01-01

Tinnitus is the perception of a sound in the absence of any objective physical sound source. Transcranial Direct Current Stimulation (tDCS) induces shifts in membrane resting potentials depending on the polarity of the stimulation: under the anode gamma band activity increases, whereas under the cathode the opposite occurs. Both single and multiple sessions of tDCS over the dorsolateral prefrontal cortex (DLPFC; anode over right DLPFC) yield a transient improvement in tinnitus intensity and tinnitus distress. The question arises whether optimization of the tDCS protocol can be obtained by using EEG driven decisions on where to place anode and cathode. Using gamma band functional connectivity could be superior to gamma band activity as functional connectivity determines the tinnitus network in many aspects of chronic tinnitus. Six-hundred-seventy-five patients were included in the study: 265 patients received tDCS with cathodal electrode placed over the left DLPFC and the anode placed overlying the right DLPFC, 380 patients received tDCS based on EEG connectivity, and 65 received no tDCS (i.e., waiting list control group). Repeated measures ANOVA revealed a significant main effect for pre versus post measurement. Bifrontal tDCS in comparison to EEG driven tDCS had a larger reduction for both tinnitus distress and tinnitus intensity. Whereas the results of the bifrontal tDCS seem to confirm previous studies, the use of gamma band functional connectivity seems not to bring any advantage to tDCS for tinnitus suppression. Using other potential biomarkers, such as gamma band activity, or theta functional connectivity could theoretically be of use. Further studies will have to elucidate whether brain state based tDCS has any advantages over “blind” bifrontal stimulation. PMID:23055986

As above, so below? Towards understanding inverse models in BCI

NASA Astrophysics Data System (ADS)

Lindgren, Jussi T.

2018-02-01

Objective. In brain-computer interfaces (BCI), measurements of the user’s brain activity are classified into commands for the computer. With EEG-based BCIs, the origins of the classified phenomena are often considered to be spatially localized in the cortical volume and mixed in the EEG. We investigate if more accurate BCIs can be obtained by reconstructing the source activities in the volume. Approach. We contrast the physiology-driven source reconstruction with data-driven representations obtained by statistical machine learning. We explain these approaches in a common linear dictionary framework and review the different ways to obtain the dictionary parameters. We consider the effect of source reconstruction on some major difficulties in BCI classification, namely information loss, feature selection and nonstationarity of the EEG. Main results. Our analysis suggests that the approaches differ mainly in their parameter estimation. Physiological source reconstruction may thus be expected to improve BCI accuracy if machine learning is not used or where it produces less optimal parameters. We argue that the considered difficulties of surface EEG classification can remain in the reconstructed volume and that data-driven techniques are still necessary. Finally, we provide some suggestions for comparing approaches. Significance. The present work illustrates the relationships between source reconstruction and machine learning-based approaches for EEG data representation. The provided analysis and discussion should help in understanding, applying, comparing and improving such techniques in the future.

EEG and MEG data analysis in SPM8.

PubMed

Litvak, Vladimir; Mattout, Jérémie; Kiebel, Stefan; Phillips, Christophe; Henson, Richard; Kilner, James; Barnes, Gareth; Oostenveld, Robert; Daunizeau, Jean; Flandin, Guillaume; Penny, Will; Friston, Karl

2011-01-01

SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.

EEG and MEG Data Analysis in SPM8

PubMed Central

Litvak, Vladimir; Mattout, Jérémie; Kiebel, Stefan; Phillips, Christophe; Henson, Richard; Kilner, James; Barnes, Gareth; Oostenveld, Robert; Daunizeau, Jean; Flandin, Guillaume; Penny, Will; Friston, Karl

2011-01-01

SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools. PMID:21437221

EEG-Annotate: Automated identification and labeling of events in continuous signals with applications to EEG.

PubMed

Su, Kyung-Min; Hairston, W David; Robbins, Kay

2018-01-01

In controlled laboratory EEG experiments, researchers carefully mark events and analyze subject responses time-locked to these events. Unfortunately, such markers may not be available or may come with poor timing resolution for experiments conducted in less-controlled naturalistic environments. We present an integrated event-identification method for identifying particular responses that occur in unlabeled continuously recorded EEG signals based on information from recordings of other subjects potentially performing related tasks. We introduce the idea of timing slack and timing-tolerant performance measures to deal with jitter inherent in such non-time-locked systems. We have developed an implementation available as an open-source MATLAB toolbox (http://github.com/VisLab/EEG-Annotate) and have made test data available in a separate data note. We applied the method to identify visual presentation events (both target and non-target) in data from an unlabeled subject using labeled data from other subjects with good sensitivity and specificity. The method also identified actual visual presentation events in the data that were not previously marked in the experiment. Although the method uses traditional classifiers for initial stages, the problem of identifying events based on the presence of stereotypical EEG responses is the converse of the traditional stimulus-response paradigm and has not been addressed in its current form. In addition to identifying potential events in unlabeled or incompletely labeled EEG, these methods also allow researchers to investigate whether particular stereotypical neural responses are present in other circumstances. Timing-tolerance has the added benefit of accommodating inter- and intra- subject timing variations. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Material and physical model for evaluation of deep brain activity contribution to EEG recordings

NASA Astrophysics Data System (ADS)

Ye, Yan; Li, Xiaoping; Wu, Tiecheng; Li, Zhe; Xie, Wenwen

2015-12-01

Deep brain activity is conventionally recorded with surgical implantation of electrodes. During the neurosurgery, brain tissue damage and the consequent side effects to patients are inevitably incurred. In order to eliminate undesired risks, we propose that deep brain activity should be measured using the noninvasive scalp electroencephalography (EEG) technique. However, the deeper the neuronal activity is located, the noisier the corresponding scalp EEG signals are. Thus, the present study aims to evaluate whether deep brain activity could be observed from EEG recordings. In the experiment, a three-layer cylindrical head model was constructed to mimic a human head. A single dipole source (sine wave, 10 Hz, altering amplitudes) was embedded inside the model to simulate neuronal activity. When the dipole source was activated, surface potential was measured via electrodes attached on the top surface of the model and raw data were recorded for signal analysis. Results show that the dipole source activity positioned at 66 mm depth in the model, equivalent to the depth of deep brain structures, is clearly observed from surface potential recordings. Therefore, it is highly possible that deep brain activity could be observed from EEG recordings and deep brain activity could be measured using the noninvasive scalp EEG technique.

MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG

PubMed Central

Dong, Li; Li, Fali; Liu, Qiang; Wen, Xin; Lai, Yongxiu; Xu, Peng; Yao, Dezhong

2017-01-01

Reference electrode standardization technique (REST) has been increasingly acknowledged and applied as a re-reference technique to transform an actual multi-channels recordings to approximately zero reference ones in electroencephalography/event-related potentials (EEG/ERPs) community around the world in recent years. However, a more easy-to-use toolbox for re-referencing scalp EEG data to zero reference is still lacking. Here, we have therefore developed two open-source MATLAB toolboxes for REST of scalp EEG. One version of REST is closely integrated into EEGLAB, which is a popular MATLAB toolbox for processing the EEG data; and another is a batch version to make it more convenient and efficient for experienced users. Both of them are designed to provide an easy-to-use for novice researchers and flexibility for experienced researchers. All versions of the REST toolboxes can be freely downloaded at http://www.neuro.uestc.edu.cn/rest/Down.html, and the detailed information including publications, comments and documents on REST can also be found from this website. An example of usage is given with comparative results of REST and average reference. We hope these user-friendly REST toolboxes could make the relatively novel technique of REST easier to study, especially for applications in various EEG studies. PMID:29163006

MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG.

PubMed

Dong, Li; Li, Fali; Liu, Qiang; Wen, Xin; Lai, Yongxiu; Xu, Peng; Yao, Dezhong

2017-01-01

Reference electrode standardization technique (REST) has been increasingly acknowledged and applied as a re-reference technique to transform an actual multi-channels recordings to approximately zero reference ones in electroencephalography/event-related potentials (EEG/ERPs) community around the world in recent years. However, a more easy-to-use toolbox for re-referencing scalp EEG data to zero reference is still lacking. Here, we have therefore developed two open-source MATLAB toolboxes for REST of scalp EEG. One version of REST is closely integrated into EEGLAB, which is a popular MATLAB toolbox for processing the EEG data; and another is a batch version to make it more convenient and efficient for experienced users. Both of them are designed to provide an easy-to-use for novice researchers and flexibility for experienced researchers. All versions of the REST toolboxes can be freely downloaded at http://www.neuro.uestc.edu.cn/rest/Down.html, and the detailed information including publications, comments and documents on REST can also be found from this website. An example of usage is given with comparative results of REST and average reference. We hope these user-friendly REST toolboxes could make the relatively novel technique of REST easier to study, especially for applications in various EEG studies.

Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG

PubMed Central

Mullen, Tim R.; Kothe, Christian A.E.; Chi, Mike; Ojeda, Alejandro; Kerth, Trevor; Makeig, Scott; Jung, Tzyy-Ping; Cauwenberghs, Gert

2015-01-01

Goal We present and evaluate a wearable high-density dry electrode EEG system and an open-source software framework for online neuroimaging and state classification. Methods The system integrates a 64-channel dry EEG form-factor with wireless data streaming for online analysis. A real-time software framework is applied, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification from connectivity features using a constrained logistic regression approach (ProxConn). We evaluate the system identification methods on simulated 64-channel EEG data. Then we evaluate system performance, using ProxConn and a benchmark ERP method, in classifying response errors in 9 subjects using the dry EEG system. Results Simulations yielded high accuracy (AUC=0.97±0.021) for real-time cortical connectivity estimation. Response error classification using cortical effective connectivity (sdDTF) was significantly above chance with similar performance (AUC) for cLORETA (0.74±0.09) and LCMV (0.72±0.08) source localization. Cortical ERP-based classification was equivalent to ProxConn for cLORETA (0.74±0.16) but significantly better for LCMV (0.82±0.12). Conclusion We demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from high-density wearable dry EEG. Significance This paper is the first validated application of these methods to 64-channel dry EEG. The work addresses a need for robust real-time measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting. Such advances can have broad impact in research, medicine, and brain-computer interfaces. The pipelines are made freely available in the open-source SIFT and BCILAB toolboxes. PMID:26415149

Abnormal cortical sources of resting state electroencephalographic rhythms in single treatment-naïve HIV individuals: A statistical z-score index.

PubMed

Babiloni, Claudio; Pennica, Alfredo; Del Percio, Claudio; Noce, Giuseppe; Cordone, Susanna; Muratori, Chiara; Ferracuti, Stefano; Donato, Nicole; Di Campli, Francesco; Gianserra, Laura; Teti, Elisabetta; Aceti, Antonio; Soricelli, Andrea; Viscione, Magdalena; Limatola, Cristina; Andreoni, Massimo; Onorati, Paolo

2016-03-01

This study tested a simple statistical procedure to recognize single treatment-naïve HIV individuals having abnormal cortical sources of resting state delta (<4 Hz) and alpha (8-13 Hz) electroencephalographic (EEG) rhythms with reference to a control group of sex-, age-, and education-matched healthy individuals. Compared to the HIV individuals with a statistically normal EEG marker, those with abnormal values were expected to show worse cognitive status. Resting state eyes-closed EEG data were recorded in 82 treatment-naïve HIV (39.8 ys.±1.2 standard error mean, SE) and 59 age-matched cognitively healthy subjects (39 ys.±2.2 SE). Low-resolution brain electromagnetic tomography (LORETA) estimated delta and alpha sources in frontal, central, temporal, parietal, and occipital cortical regions. Ratio of the activity of parietal delta and high-frequency alpha sources (EEG marker) showed the maximum difference between the healthy and the treatment-naïve HIV group. Z-score of the EEG marker was statistically abnormal in 47.6% of treatment-naïve HIV individuals with reference to the healthy group (p<0.05). Compared to the HIV individuals with a statistically normal EEG marker, those with abnormal values exhibited lower mini mental state evaluation (MMSE) score, higher CD4 count, and lower viral load (p<0.05). This statistical procedure permitted for the first time to identify single treatment-naïve HIV individuals having abnormal EEG activity. This procedure might enrich the detection and monitoring of effects of HIV on brain function in single treatment-naïve HIV individuals. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Bispectral pairwise interacting source analysis for identifying systems of cross-frequency interacting brain sources from electroencephalographic or magnetoencephalographic signals

NASA Astrophysics Data System (ADS)

Chella, Federico; Pizzella, Vittorio; Zappasodi, Filippo; Nolte, Guido; Marzetti, Laura

2016-05-01

Brain cognitive functions arise through the coordinated activity of several brain regions, which actually form complex dynamical systems operating at multiple frequencies. These systems often consist of interacting subsystems, whose characterization is of importance for a complete understanding of the brain interaction processes. To address this issue, we present a technique, namely the bispectral pairwise interacting source analysis (biPISA), for analyzing systems of cross-frequency interacting brain sources when multichannel electroencephalographic (EEG) or magnetoencephalographic (MEG) data are available. Specifically, the biPISA makes it possible to identify one or many subsystems of cross-frequency interacting sources by decomposing the antisymmetric components of the cross-bispectra between EEG or MEG signals, based on the assumption that interactions are pairwise. Thanks to the properties of the antisymmetric components of the cross-bispectra, biPISA is also robust to spurious interactions arising from mixing artifacts, i.e., volume conduction or field spread, which always affect EEG or MEG functional connectivity estimates. This method is an extension of the pairwise interacting source analysis (PISA), which was originally introduced for investigating interactions at the same frequency, to the study of cross-frequency interactions. The effectiveness of this approach is demonstrated in simulations for up to three interacting source pairs and for real MEG recordings of spontaneous brain activity. Simulations show that the performances of biPISA in estimating the phase difference between the interacting sources are affected by the increasing level of noise rather than by the number of the interacting subsystems. The analysis of real MEG data reveals an interaction between two pairs of sources of central mu and beta rhythms, localizing in the proximity of the left and right central sulci.

LORETA EEG phase reset of the default mode network

PubMed Central

Thatcher, Robert W.; North, Duane M.; Biver, Carl J.

2014-01-01

Objectives: The purpose of this study was to explore phase reset of 3-dimensional current sources in Brodmann areas located in the human default mode network (DMN) using Low Resolution Electromagnetic Tomography (LORETA) of the human electroencephalogram (EEG). Methods: The EEG was recorded from 19 scalp locations from 70 healthy normal subjects ranging in age from 13 to 20 years. A time point by time point computation of LORETA current sources were computed for 14 Brodmann areas comprising the DMN in the delta frequency band. The Hilbert transform of the LORETA time series was used to compute the instantaneous phase differences between all pairs of Brodmann areas. Phase shift and lock durations were calculated based on the 1st and 2nd derivatives of the time series of phase differences. Results: Phase shift duration exhibited three discrete modes at approximately: (1) 25 ms, (2) 50 ms, and (3) 65 ms. Phase lock duration present primarily at: (1) 300–350 ms and (2) 350–450 ms. Phase shift and lock durations were inversely related and exhibited an exponential change with distance between Brodmann areas. Conclusions: The results are explained by local neural packing density of network hubs and an exponential decrease in connections with distance from a hub. The results are consistent with a discrete temporal model of brain function where anatomical hubs behave like a “shutter” that opens and closes at specific durations as nodes of a network giving rise to temporarily phase locked clusters of neurons for specific durations. PMID:25100976

Neuronal Networks during Burst Suppression as Revealed by Source Analysis

PubMed Central

Reinicke, Christine; Moeller, Friederike; Anwar, Abdul Rauf; Mideksa, Kidist Gebremariam; Pressler, Ronit; Deuschl, Günther; Stephani, Ulrich; Siniatchkin, Michael

2015-01-01

Introduction Burst-suppression (BS) is an electroencephalography (EEG) pattern consisting of alternant periods of slow waves of high amplitude (burst) and periods of so called flat EEG (suppression). It is generally associated with coma of various etiologies (hypoxia, drug-related intoxication, hypothermia, and childhood encephalopathies, but also anesthesia). Animal studies suggest that both the cortex and the thalamus are involved in the generation of BS. However, very little is known about mechanisms of BS in humans. The aim of this study was to identify the neuronal network underlying both burst and suppression phases using source reconstruction and analysis of functional and effective connectivity in EEG. Material/Methods Dynamic imaging of coherent sources (DICS) was applied to EEG segments of 13 neonates and infants with burst and suppression EEG pattern. The brain area with the strongest power in the analyzed frequency (1–4 Hz) range was defined as the reference region. DICS was used to compute the coherence between this reference region and the entire brain. The renormalized partial directed coherence (RPDC) was used to describe the informational flow between the identified sources. Results/Conclusion Delta activity during the burst phases was associated with coherent sources in the thalamus and brainstem as well as bilateral sources in cortical regions mainly frontal and parietal, whereas suppression phases were associated with coherent sources only in cortical regions. Results of the RPDC analyses showed an upwards informational flow from the brainstem towards the thalamus and from the thalamus to cortical regions, which was absent during the suppression phases. These findings may support the theory that a “cortical deafferentiation” between the cortex and sub-cortical structures exists especially in suppression phases compared to burst phases in burst suppression EEGs. Such a deafferentiation may play a role in the poor neurological outcome of children with these encephalopathies. PMID:25927439

A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection.

PubMed

Lo, Chi-Chun; Chien, Tsung-Yi; Chen, Yu-Chun; Tsai, Shang-Ho; Fang, Wai-Chi; Lin, Bor-Shyh

2016-02-06

Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype.

Novel artefact removal algorithms for co-registered EEG/fMRI based on selective averaging and subtraction.

PubMed

de Munck, Jan C; van Houdt, Petra J; Gonçalves, Sónia I; van Wegen, Erwin; Ossenblok, Pauly P W

2013-01-01

Co-registered EEG and functional MRI (EEG/fMRI) is a potential clinical tool for planning invasive EEG in patients with epilepsy. In addition, the analysis of EEG/fMRI data provides a fundamental insight into the precise physiological meaning of both fMRI and EEG data. Routine application of EEG/fMRI for localization of epileptic sources is hampered by large artefacts in the EEG, caused by switching of scanner gradients and heartbeat effects. Residuals of the ballistocardiogram (BCG) artefacts are similarly shaped as epileptic spikes, and may therefore cause false identification of spikes. In this study, new ideas and methods are presented to remove gradient artefacts and to reduce BCG artefacts of different shapes that mutually overlap in time. Gradient artefacts can be removed efficiently by subtracting an average artefact template when the EEG sampling frequency and EEG low-pass filtering are sufficient in relation to MR gradient switching (Gonçalves et al., 2007). When this is not the case, the gradient artefacts repeat themselves at time intervals that depend on the remainder between the fMRI repetition time and the closest multiple of the EEG acquisition time. These repetitions are deterministic, but difficult to predict due to the limited precision by which these timings are known. Therefore, we propose to estimate gradient artefact repetitions using a clustering algorithm, combined with selective averaging. Clustering of the gradient artefacts yields cleaner EEG for data recorded during scanning of a 3T scanner when using a sampling frequency of 2048 Hz. It even gives clean EEG when the EEG is sampled with only 256 Hz. Current BCG artefacts-reduction algorithms based on average template subtraction have the intrinsic limitation that they fail to deal properly with artefacts that overlap in time. To eliminate this constraint, the precise timings of artefact overlaps were modelled and represented in a sparse matrix. Next, the artefacts were disentangled with a least squares procedure. The relevance of this approach is illustrated by determining the BCG artefacts in a data set consisting of 29 healthy subjects recorded in a 1.5 T scanner and 15 patients with epilepsy recorded in a 3 T scanner. Analysis of the relationship between artefact amplitude, duration and heartbeat interval shows that in 22% (1.5T data) to 30% (3T data) of the cases BCG artefacts show an overlap. The BCG artefacts of the EEG/fMRI data recorded on the 1.5T scanner show a small negative correlation between HBI and BCG amplitude. In conclusion, the proposed methodology provides a substantial improvement of the quality of the EEG signal without excessive computer power or additional hardware than standard EEG-compatible equipment. Copyright © 2012 Elsevier Inc. All rights reserved.

EEG current source density and the phenomenology of the default network.

PubMed

Cannon, Rex L; Baldwin, Debora R

2012-10-01

In recent years, there has been an increasing line of research dedicated to the investigation of the default mode network (DMN) of the brain and resting state networks. However, the mental activity of the DMN has not been rigorously assessed to date. The specific aims of the current study were 2-fold: First, we sought to determine whether the current source density (CSD) levels in the DMN would correspond to other neuroimaging techniques. Second, we sought to understand the subjective mental activity of the DMN during baseline recordings. This study was conducted with 63 nonclinical participants, 34 female and 29 males with a mean age of 19.2 years (standard deviation = 2.0). The participants were recorded in 8 conditions. First, 4-minute eyes-closed baseline (ECB) and eyes-opened baseline (EOB) were obtained. The participants then completed 3 assessment instruments and 3 image conditions while the electroencephalography (EEG) was continuously recorded. Participants completed subjective reports for baselines and image conditions. These were rated by 3 independent raters and compared for reliability using a random effects model with an absolute agreement definition. The mean CSD between all conditions differed significantly, in many but not all regions of interest in the DMN. Interestingly, as suggested by other studies, the DMN appears preferential to self-relevant, self-specific, or self-perceptive processes. The reliability analyses show α for interrater agreement for ECB at .95 and EOB at .96. The subjective reports obtained from the participants regarding the mental activities employed during baseline recordings correspond to attentional and self-regulatory processes, which may also implicate the resting state or DMN as playing a direct role in the maintenance of a complex behavior (eg, being still, attending, and self-regulating). Thus, attention and self-regulation constitute the phenomenology of the resting state (DMN) in this study. The results also demonstrate that EEG CSD is a useful method to examine the DMN during concept-specific tasks to elucidate the neural activity associated with these concepts. Standardized low-resolution electromagnetic tomography (sLORETA) can localize to 5 mm(3), which is comparable to the findings in functional magnetic resonance imaging (fMRI). However, sLORETA can provide data about the difference in activity between groups, individuals, or populations which in many cases fMRI cannot provide.

DEVELOPMENTAL CHANGES IN FACT AND SOURCE RECALL: CONTRIBUTIONS FROM EXECUTIVE FUNCTION AND BRAIN ELECTRICAL ACTIVITY

PubMed Central

Rajan, Vinaya; Bell, Martha Ann

2014-01-01

Source memory involves recollecting the contextual details surrounding a memory episode. When source information is bound together, it makes a memory episodic in nature. Unfortunately, very little is known about the factors that contribute to its formation in early development. This study examined the development of source memory in middle childhood. Measures of executive function were examined as potential sources of variation in fact and source recall. Continuous electroencephalogram (EEG) measures were collected during baseline and fact and source retrieval in order to examine memory-related changes in EEG power. Six and 8-year-old children were taught 10 novel facts from two different sources and recall for fact and source information was later tested. Older children were better on fact recall, but both ages were comparable on source recall. However, source recall performance was poor at both ages, suggesting that this ability continues to develop beyond middle childhood. Regression analyses revealed that executive function uniquely predicted variance in source recall performance. Task-related increases in theta power were observed at frontal, temporal and parietal electrode sites during fact and source retrieval. This investigation contributes to our understanding of age-related differences in source memory processing in middle childhood. PMID:25459873

Potential for unreliable interpretation of EEG recorded with microelectrodes.

PubMed

Stacey, William C; Kellis, Spencer; Greger, Bradley; Butson, Christopher R; Patel, Paras R; Assaf, Trevor; Mihaylova, Temenuzhka; Glynn, Simon

2013-08-01

Recent studies in epilepsy, cognition, and brain machine interfaces have shown the utility of recording intracranial electroencephalography (iEEG) with greater spatial resolution. Many of these studies utilize microelectrodes connected to specialized amplifiers that are optimized for such recordings. We recently measured the impedances of several commercial microelectrodes and demonstrated that they will distort iEEG signals if connected to clinical EEG amplifiers commonly used in most centers. In this study we demonstrate the clinical implications of this effect and identify some of the potential difficulties in using microelectrodes. Human iEEG data were digitally filtered to simulate the signal recorded by a hybrid grid (two macroelectrodes and eight microelectrodes) connected to a standard EEG amplifier. The filtered iEEG data were read by three trained epileptologists, and high frequency oscillations (HFOs) were detected with a well-known algorithm. The filtering method was verified experimentally by recording an injected EEG signal in a saline bath with the same physical acquisition system used to generate the model. Several electrodes underwent scanning electron microscopy (SEM). Macroelectrode recordings were unaltered compared to the source iEEG signal, but microelectrodes attenuated low frequencies. The attenuated signals were difficult to interpret: all three clinicians changed their clinical scoring of slowing and seizures when presented with the same data recorded on different sized electrodes. The HFO detection algorithm was oversensitive with microelectrodes, classifying many more HFOs than when the same data were recorded with macroelectrodes. In addition, during experimental recordings the microelectrodes produced much greater noise as well as large baseline fluctuations, creating sharply contoured transients, and superimposed "false" HFOs. SEM of these microelectrodes demonstrated marked variability in exposed electrode surface area, lead fractures, and sharp edges. Microelectrodes should not be used with low impedance (<1 GΩ) amplifiers due to severe signal attenuation and variability that changes clinical interpretations. The current method of preparing microelectrodes can leave sharp edges and nonuniform amounts of exposed wire. Even when recorded with higher impedance amplifiers, microelectrode data are highly prone to artifacts that are difficult to interpret. Great care must be taken when analyzing iEEG from high impedance microelectrodes. Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.

Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia.

PubMed

Blokland, Yvonne; Spyrou, Loukianos; Thijssen, Dick; Eijsvogels, Thijs; Colier, Willy; Floor-Westerdijk, Marianne; Vlek, Rutger; Bruhn, Jorgen; Farquhar, Jason

2014-03-01

Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the "attempted movement" condition was replaced with "actual movement." A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research.

Causality within the Epileptic Network: An EEG-fMRI Study Validated by Intracranial EEG.

PubMed

Vaudano, Anna Elisabetta; Avanzini, Pietro; Tassi, Laura; Ruggieri, Andrea; Cantalupo, Gaetano; Benuzzi, Francesca; Nichelli, Paolo; Lemieux, Louis; Meletti, Stefano

2013-01-01

Accurate localization of the Seizure Onset Zone (SOZ) is crucial in patients with drug-resistance focal epilepsy. EEG with fMRI recording (EEG-fMRI) has been proposed as a complementary non-invasive tool, which can give useful additional information in the pre-surgical work-up. However, fMRI maps related to interictal epileptiform activities (IED) often show multiple regions of signal change, or "networks," rather than highly focal ones. Effective connectivity approaches like Dynamic Causal Modeling (DCM) applied to fMRI data potentially offers a framework to address which brain regions drives the generation of seizures and IED within an epileptic network. Here, we present a first attempt to validate DCM on EEG-fMRI data in one patient affected by frontal lobe epilepsy. Pre-surgical EEG-fMRI demonstrated two distinct clusters of blood oxygenation level dependent (BOLD) signal increases linked to IED, one located in the left frontal pole and the other in the ipsilateral dorso-lateral frontal cortex. DCM of the IED-related BOLD signal favored a model corresponding to the left dorso-lateral frontal cortex as driver of changes in the fronto-polar region. The validity of DCM was supported by: (a) the results of two different non-invasive analysis obtained on the same dataset: EEG source imaging (ESI), and "psycho-physiological interaction" analysis; (b) the failure of a first surgical intervention limited to the fronto-polar region; (c) the results of the intracranial EEG monitoring performed after the first surgical intervention confirming a SOZ located over the dorso-lateral frontal cortex. These results add evidence that EEG-fMRI together with advanced methods of BOLD signal analysis is a promising tool that can give relevant information within the epilepsy surgery diagnostic work-up.

Choosing MUSE: Validation of a Low-Cost, Portable EEG System for ERP Research

PubMed Central

Krigolson, Olave E.; Williams, Chad C.; Norton, Angela; Hassall, Cameron D.; Colino, Francisco L.

2017-01-01

In recent years there has been an increase in the number of portable low-cost electroencephalographic (EEG) systems available to researchers. However, to date the validation of the use of low-cost EEG systems has focused on continuous recording of EEG data and/or the replication of large system EEG setups reliant on event-markers to afford examination of event-related brain potentials (ERP). Here, we demonstrate that it is possible to conduct ERP research without being reliant on event markers using a portable MUSE EEG system and a single computer. Specifically, we report the results of two experiments using data collected with the MUSE EEG system—one using the well-known visual oddball paradigm and the other using a standard reward-learning task. Our results demonstrate that we could observe and quantify the N200 and P300 ERP components in the visual oddball task and the reward positivity (the mirror opposite component to the feedback-related negativity) in the reward-learning task. Specifically, single sample t-tests of component existence (all p's < 0.05), computation of Bayesian credible intervals, and 95% confidence intervals all statistically verified the existence of the N200, P300, and reward positivity in all analyses. We provide with this research paper an open source website with all the instructions, methods, and software to replicate our findings and to provide researchers with an easy way to use the MUSE EEG system for ERP research. Importantly, our work highlights that with a single computer and a portable EEG system such as the MUSE one can conduct ERP research with ease thus greatly extending the possible use of the ERP methodology to a variety of novel contexts. PMID:28344546

Choosing MUSE: Validation of a Low-Cost, Portable EEG System for ERP Research.

PubMed

Krigolson, Olave E; Williams, Chad C; Norton, Angela; Hassall, Cameron D; Colino, Francisco L

2017-01-01

In recent years there has been an increase in the number of portable low-cost electroencephalographic (EEG) systems available to researchers. However, to date the validation of the use of low-cost EEG systems has focused on continuous recording of EEG data and/or the replication of large system EEG setups reliant on event-markers to afford examination of event-related brain potentials (ERP). Here, we demonstrate that it is possible to conduct ERP research without being reliant on event markers using a portable MUSE EEG system and a single computer. Specifically, we report the results of two experiments using data collected with the MUSE EEG system-one using the well-known visual oddball paradigm and the other using a standard reward-learning task. Our results demonstrate that we could observe and quantify the N200 and P300 ERP components in the visual oddball task and the reward positivity (the mirror opposite component to the feedback-related negativity) in the reward-learning task. Specifically, single sample t -tests of component existence (all p 's < 0.05), computation of Bayesian credible intervals, and 95% confidence intervals all statistically verified the existence of the N200, P300, and reward positivity in all analyses. We provide with this research paper an open source website with all the instructions, methods, and software to replicate our findings and to provide researchers with an easy way to use the MUSE EEG system for ERP research. Importantly, our work highlights that with a single computer and a portable EEG system such as the MUSE one can conduct ERP research with ease thus greatly extending the possible use of the ERP methodology to a variety of novel contexts.

Zazen meditation and no-task resting EEG compared with LORETA intracortical source localization.

PubMed

Faber, Pascal L; Lehmann, Dietrich; Gianotti, Lorena R R; Milz, Patricia; Pascual-Marqui, Roberto D; Held, Marlene; Kochi, Kieko

2015-02-01

Meditation is a self-induced and willfully initiated practice that alters the state of consciousness. The meditation practice of Zazen, like many other meditation practices, aims at disregarding intrusive thoughts while controlling body posture. It is an open monitoring meditation characterized by detached moment-to-moment awareness and reduced conceptual thinking and self-reference. Which brain areas differ in electric activity during Zazen compared to task-free resting? Since scalp electroencephalography (EEG) waveforms are reference-dependent, conclusions about the localization of active brain areas are ambiguous. Computing intracerebral source models from the scalp EEG data solves this problem. In the present study, we applied source modeling using low resolution brain electromagnetic tomography (LORETA) to 58-channel scalp EEG data recorded from 15 experienced Zen meditators during Zazen and no-task resting. Zazen compared to no-task resting showed increased alpha-1 and alpha-2 frequency activity in an exclusively right-lateralized cluster extending from prefrontal areas including the insula to parts of the somatosensory and motor cortices and temporal areas. Zazen also showed decreased alpha and beta-2 activity in the left angular gyrus and decreased beta-1 and beta-2 activity in a large bilateral posterior cluster comprising the visual cortex, the posterior cingulate cortex and the parietal cortex. The results include parts of the default mode network and suggest enhanced automatic memory and emotion processing, reduced conceptual thinking and self-reference on a less judgmental, i.e., more detached moment-to-moment basis during Zazen compared to no-task resting.

Convergence of EEG and fMRI measures of reward anticipation.

PubMed

Gorka, Stephanie M; Phan, K Luan; Shankman, Stewart A

2015-12-01

Deficits in reward anticipation are putative mechanisms for multiple psychopathologies. Research indicates that these deficits are characterized by reduced left (relative to right) frontal electroencephalogram (EEG) activity and blood oxygenation level-dependent (BOLD) signal abnormalities in mesolimbic and prefrontal neural regions during reward anticipation. Although it is often assumed that these two measures capture similar mechanisms, no study to our knowledge has directly examined the convergence between frontal EEG alpha asymmetry and functional magnetic resonance imaging (fMRI) during reward anticipation in the same sample. Therefore, the aim of the current study was to investigate if and where in the brain frontal EEG alpha asymmetry and fMRI measures were correlated in a sample of 40 adults. All participants completed two analogous reward anticipation tasks--once during EEG data collection and the other during fMRI data collection. Results indicated that the two measures do converge and that during reward anticipation, increased relative left frontal activity is associated with increased left anterior cingulate cortex (ACC)/medial prefrontal cortex (mPFC) and left orbitofrontal cortex (OFC) activation. This suggests that the two measures may similarly capture PFC functioning, which is noteworthy given the role of these regions in reward processing and the pathophysiology of disorders such as depression and schizophrenia. Copyright © 2015 Elsevier B.V. All rights reserved.

Addressing the vulnerabilities of pass-thoughts

NASA Astrophysics Data System (ADS)

Fernandez, Gabriel C.; Danko, Amanda S.

2016-05-01

As biometrics become increasingly pervasive, consumer electronics are reaping the benefits of improved authentication methods. Leveraging the physical characteristics of a user reduces the burden of setting and remembering complex passwords, while enabling stronger security. Multi-factor systems lend further credence to this model, increasing security via multiple passive data points. In recent years, brainwaves have been shown to be another feasible source for biometric authentication. Physically unique to an individual in certain circumstances, the signals can also be changed by the user at will, making them more robust than static physical characteristics. No paradigm is impervious however, and even well-established medical technologies have deficiencies. In this work, a system for biometric authentication via brainwaves is constructed with electroencephalography (EEG). The efficacy of EEG biometrics via existing consumer electronics is evaluated, and vulnerabilities of such a system are enumerated. Impersonation attacks are performed to expose the extent to which the system is vulnerable. Finally, a multimodal system combining EEG with additional factors is recommended and outlined.

Regularized two-step brain activity reconstruction from spatiotemporal EEG data

NASA Astrophysics Data System (ADS)

Alecu, Teodor I.; Voloshynovskiy, Sviatoslav; Pun, Thierry

2004-10-01

We are aiming at using EEG source localization in the framework of a Brain Computer Interface project. We propose here a new reconstruction procedure, targeting source (or equivalently mental task) differentiation. EEG data can be thought of as a collection of time continuous streams from sparse locations. The measured electric potential on one electrode is the result of the superposition of synchronized synaptic activity from sources in all the brain volume. Consequently, the EEG inverse problem is a highly underdetermined (and ill-posed) problem. Moreover, each source contribution is linear with respect to its amplitude but non-linear with respect to its localization and orientation. In order to overcome these drawbacks we propose a novel two-step inversion procedure. The solution is based on a double scale division of the solution space. The first step uses a coarse discretization and has the sole purpose of globally identifying the active regions, via a sparse approximation algorithm. The second step is applied only on the retained regions and makes use of a fine discretization of the space, aiming at detailing the brain activity. The local configuration of sources is recovered using an iterative stochastic estimator with adaptive joint minimum energy and directional consistency constraints.

Anatomically constrained dipole adjustment (ANACONDA) for accurate MEG/EEG focal source localizations

NASA Astrophysics Data System (ADS)

Im, Chang-Hwan; Jung, Hyun-Kyo; Fujimaki, Norio

2005-10-01

This paper proposes an alternative approach to enhance localization accuracy of MEG and EEG focal sources. The proposed approach assumes anatomically constrained spatio-temporal dipoles, initial positions of which are estimated from local peak positions of distributed sources obtained from a pre-execution of distributed source reconstruction. The positions of the dipoles are then adjusted on the cortical surface using a novel updating scheme named cortical surface scanning. The proposed approach has many advantages over the conventional ones: (1) as the cortical surface scanning algorithm uses spatio-temporal dipoles, it is robust with respect to noise; (2) it requires no a priori information on the numbers and initial locations of the activations; (3) as the locations of dipoles are restricted only on a tessellated cortical surface, it is physiologically more plausible than the conventional ECD model. To verify the proposed approach, it was applied to several realistic MEG/EEG simulations and practical experiments. From the several case studies, it is concluded that the anatomically constrained dipole adjustment (ANACONDA) approach will be a very promising technique to enhance accuracy of focal source localization which is essential in many clinical and neurological applications of MEG and EEG.

Neurodevelopmental Correlates of Theory of Mind in Preschool Children

ERIC Educational Resources Information Center

Sabbagh, Mark A.; Bowman, Lindsay C.; Evraire, Lyndsay E.; Ito, Jennie M. B.

2009-01-01

Baseline electroencephalogram (EEG) data were collected from twenty-nine 4-year-old children who also completed batteries of representational theory-of-mind (RTM) tasks and executive functioning (EF) tasks. Neural sources of children's EEG alpha (6-9 Hz) were estimated and analyzed to determine whether individual differences in regional EEG alpha…

Ictal and interictal electric source imaging in presurgical evaluation: a prospective study.

PubMed

Sharma, Praveen; Scherg, Michael; Pinborg, Lars H; Fabricius, Martin; Rubboli, Guido; Pedersen, Birthe; Leffers, Anne-Mette; Uldall, Peter; Jespersen, Bo; Brennum, Jannick; Mølby Henriksen, Otto; Beniczky, Sándor

2018-05-11

Accurate localization of the epileptic focus is essential for surgical treatment of patients with drug- resistant epilepsy. EEG source imaging (ESI) is increasingly used in presurgical evaluation. However, most previous studies analysed interictal discharges. Prospective studies comparing feasibility and accuracy of interictal (II) and ictal (IC) ESI are lacking. We prospectively analysed long-term video EEG recordings (LTM) of patients admitted for presurgical evaluation. We performed ESI of II and IC signals, using two methods: equivalent current dipole (ECD) and distributed source model (DSM). LTM recordings employed the standard 25-electrode array (including inferior temporal electrodes). An age-matched template head-model was used for source analysis. Results were compared with intracranial recordings (ICR), conventional neuroimaging methods (MRI, PET, SPECT) and outcome one year after surgery. Eighty-seven consecutive patients were analysed. ECD gave a significantly higher proportion of patients with localised focal abnormalities (94%) compared to MRI (70%), PET (66%) and SPECT (64%). Agreement between the ESI methods and ICR was moderate to substantial (k=0.56-0.79). Fifty-four patients were operated (47 for more than one year ago) and 62% of them became seizure-free. Localization accuracy of II-ESI was 51% for DSM and 57% for ECD; for IC-ESI this was 51% (DSM) and 62% (ECD). The differences between the ESI methods were not significant. Differences in localization accuracy between ESI and MRI (55%), PET (33%) and SPECT (40%) were not significant. II and IC ESI of LTM-data have high feasibility and their localisation accuracy is similar to the conventional neuroimaging methods. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Towards a constructionist approach to emotions: verification of the three-dimensional model of affect with EEG-independent component analysis.

PubMed

Wyczesany, Miroslaw; Ligeza, Tomasz S

2015-03-01

The locationist model of affect, which assumes separate brain structures devoted to particular discrete emotions, is currently being questioned as it has not received enough convincing experimental support. An alternative, constructionist approach suggests that our emotional states emerge from the interaction between brain functional networks, which are related to more general, continuous affective categories. In the study, we tested whether the three-dimensional model of affect based on valence, arousal, and dominance (VAD) can reflect brain activity in a more coherent way than the traditional locationist approach. Independent components of brain activity were derived from spontaneous EEG recordings and localized using the DIPFIT method. The correspondence between the spectral power of the revealed brain sources and a mood self-report quantified on the VAD space was analysed. Activation of four (out of nine) clusters of independent brain sources could be successfully explained by the specific combination of three VAD dimensions. The results support the constructionist theory of emotions.

Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data.

PubMed

Owen, Julia P; Wipf, David P; Attias, Hagai T; Sekihara, Kensuke; Nagarajan, Srikantan S

2012-03-01

In this paper, we present an extensive performance evaluation of a novel source localization algorithm, Champagne. It is derived in an empirical Bayesian framework that yields sparse solutions to the inverse problem. It is robust to correlated sources and learns the statistics of non-stimulus-evoked activity to suppress the effect of noise and interfering brain activity. We tested Champagne on both simulated and real M/EEG data. The source locations used for the simulated data were chosen to test the performance on challenging source configurations. In simulations, we found that Champagne outperforms the benchmark algorithms in terms of both the accuracy of the source localizations and the correct estimation of source time courses. We also demonstrate that Champagne is more robust to correlated brain activity present in real MEG data and is able to resolve many distinct and functionally relevant brain areas with real MEG and EEG data. Copyright © 2011 Elsevier Inc. All rights reserved.

Enhanced inter-subject brain computer interface with associative sensorimotor oscillations.

PubMed

Saha, Simanto; Ahmed, Khawza I; Mostafa, Raqibul; Khandoker, Ahsan H; Hadjileontiadis, Leontios

2017-02-01

Electroencephalography (EEG) captures electrophysiological signatures of cortical events from the scalp with high-dimensional electrode montages. Usually, excessive sources produce outliers and potentially affect the actual event related sources. Besides, EEG manifests inherent inter-subject variability of the brain dynamics, at the resting state and/or under the performance of task(s), caused probably due to the instantaneous fluctuation of psychophysiological states. A wavelet coherence (WC) analysis for optimally selecting associative inter-subject channels is proposed here and is being used to boost performances of motor imagery (MI)-based inter-subject brain computer interface (BCI). The underlying hypothesis is that optimally associative inter-subject channels can reduce the effects of outliers and, thus, eliminate dissimilar cortical patterns. The proposed approach has been tested on the dataset IVa from BCI competition III, including EEG data acquired from five healthy subjects who were given visual cues to perform 280 trials of MI for the right hand and right foot. Experimental results have shown increased classification accuracy (81.79%) using the WC-based selected 16 channels compared to the one (56.79%) achieved using all the available 118 channels. The associative channels lie mostly around the sensorimotor regions of the brain, reinforced by the previous literature, describing spatial brain dynamics during sensorimotor oscillations. Apparently, the proposed approach paves the way for optimised EEG channel selection that could boost further the efficiency and real-time performance of BCI systems.

Sparse EEG/MEG source estimation via a group lasso

PubMed Central

Lim, Michael; Ales, Justin M.; Cottereau, Benoit R.; Hastie, Trevor

2017-01-01

Non-invasive recordings of human brain activity through electroencephalography (EEG) or magnetoencelphalography (MEG) are of value for both basic science and clinical applications in sensory, cognitive, and affective neuroscience. Here we introduce a new approach to estimating the intra-cranial sources of EEG/MEG activity measured from extra-cranial sensors. The approach is based on the group lasso, a sparse-prior inverse that has been adapted to take advantage of functionally-defined regions of interest for the definition of physiologically meaningful groups within a functionally-based common space. Detailed simulations using realistic source-geometries and data from a human Visual Evoked Potential experiment demonstrate that the group-lasso method has improved performance over traditional ℓ2 minimum-norm methods. In addition, we show that pooling source estimates across subjects over functionally defined regions of interest results in improvements in the accuracy of source estimates for both the group-lasso and minimum-norm approaches. PMID:28604790

Topographic mapping of electroencephalography coherence in hypnagogic state.

PubMed

Tanaka, H; Hayashi, M; Hori, T

1998-04-01

The present study examined the topographic characteristics of hypnagogic electroencephalography (EEG), using topographic mapping of EEG power and coherence corresponding to nine EEG stages (Hori's hypnagogic EEG stages). EEG stages 1 and 2, the EEG stages 3-8, and the EEG stage 9 each correspond with standard sleep stage W, 1 and 2, respectively. The dominant topographic components of delta and theta activities increased clearly from the vertex sharp-wave stage (the EEG stages 6 and 7) in the anterior-central areas. The dominant topographic component of alpha 3 activities increased clearly from the EEG stage 9 in the anterior-central areas. The dominant topographic component of sigma activities increased clearly from the EEG stage 8 in the central-parietal area. These results suggested basic sleep process might start before the onset of sleep stage 2 or of the manually scored spindles.

Reconstructing cortical current density by exploring sparseness in the transform domain

NASA Astrophysics Data System (ADS)

Ding, Lei

2009-05-01

In the present study, we have developed a novel electromagnetic source imaging approach to reconstruct extended cortical sources by means of cortical current density (CCD) modeling and a novel EEG imaging algorithm which explores sparseness in cortical source representations through the use of L1-norm in objective functions. The new sparse cortical current density (SCCD) imaging algorithm is unique since it reconstructs cortical sources by attaining sparseness in a transform domain (the variation map of cortical source distributions). While large variations are expected to occur along boundaries (sparseness) between active and inactive cortical regions, cortical sources can be reconstructed and their spatial extents can be estimated by locating these boundaries. We studied the SCCD algorithm using numerous simulations to investigate its capability in reconstructing cortical sources with different extents and in reconstructing multiple cortical sources with different extent contrasts. The SCCD algorithm was compared with two L2-norm solutions, i.e. weighted minimum norm estimate (wMNE) and cortical LORETA. Our simulation data from the comparison study show that the proposed sparse source imaging algorithm is able to accurately and efficiently recover extended cortical sources and is promising to provide high-accuracy estimation of cortical source extents.

Quantitative EEG analysis in minimally conscious state patients during postural changes.

PubMed

Greco, A; Carboncini, M C; Virgillito, A; Lanata, A; Valenza, G; Scilingo, E P

2013-01-01

Mobilization and postural changes of patients with cognitive impairment are standard clinical practices useful for both psychic and physical rehabilitation process. During this process, several physiological signals, such as Electroen-cephalogram (EEG), Electrocardiogram (ECG), Photopletysmography (PPG), Respiration activity (RESP), Electrodermal activity (EDA), are monitored and processed. In this paper we investigated how quantitative EEG (qEEG) changes with postural modifications in minimally conscious state patients. This study is quite novel and no similar experimental data can be found in the current literature, therefore, although results are very encouraging, a quantitative analysis of the cortical area activated in such postural changes still needs to be deeply investigated. More specifically, this paper shows EEG power spectra and brain symmetry index modifications during a verticalization procedure, from 0 to 60 degrees, of three patients in Minimally Consciousness State (MCS) with focused region of impairment. Experimental results show a significant increase of the power in β band (12 - 30 Hz), commonly associated to human alertness process, thus suggesting that mobilization and postural changes can have beneficial effects in MCS patients.

The use of EEG to measure cerebral changes during computer-based motion-sickness-inducing tasks

NASA Astrophysics Data System (ADS)

Strychacz, Christopher; Viirre, Erik; Wing, Shawn

2005-05-01

Motion sickness (MS) is a stressor commonly attributed with causing serious navigational and performance errors. The distinct nature of MS suggests this state may have distinct neural markers distinguishable from other states known to affect performance (e.g., stress, fatigue, sleep deprivation, high workload). This pilot study used new high-resolution electro-encephalograph (EEG) technologies to identify distinct neuronal activation changes that occur during MS. Brain EEG activity was monitored while subjects performed a ball-tracking task and viewed stimuli on a projection screen intended to induce motion sickness/spatial disorientation. Results show the presence of EEG spectral changes in all subjects who developed motion sickness when compared to baseline levels. These changes included: 1) low frequency (1 to 10 Hz) changes that may reflect oculomotor movements rather than intra-cerebral sources; 2) increased spectral power across all frequencies (attributable to increased scalp conductivity related to sweating), 3) local increases of power spectra in the 20-50 Hz range (likely attributable to external muscles on the skull) and; 4) a central posterior (occipital) independent component that shows suppression of a 20 Hz peak in the MS condition when compared to baseline. Further research is necessary to refine neural markers, characterize their origin and physiology, to distinguish between motion sickness and other states and to enable markers to be used for operator state monitoring and the designing of interventions for motion sickness.

Dissociation of Subjectively Reported and Behaviorally Indexed Mind Wandering by EEG Rhythmic Activity

PubMed Central

Qin, Jungang; Perdoni, Christopher; He, Bin

2011-01-01

Inattention to current activity is ubiquitous in everyday situations. Mind wandering is an example of such a state, and its related brain areas have been examined in the literature. However, there is no clear evidence regarding neural rhythmic activities linked to mind wandering. Using a vigilance task with thought sampling and electroencephalography recording, the current study simultaneously examined neural oscillatory activities related to subjectively reported and behaviorally indexed mind wandering. By implementing time-frequency analysis, we found that subjectively reported mind wandering, relative to behaviorally indexed, showed increased gamma band activity at bilateral frontal-central areas. By means of beamformer source imaging, we found subjectively reported mind wandering within the gamma band to be characterized by increased activation in bilateral frontal cortices, supplemental motor area, paracentral cortex and right inferior temporal cortex in comparison to behaviorally indexed mind wandering. These findings dissociate subjectively reported and behaviorally indexed mind wandering and suggest that a higher degree of executive control processes are engaged in subjectively reported mind wandering. PMID:21915257

An EEG-Based Person Authentication System with Open-Set Capability Combining Eye Blinking Signals

PubMed Central

Wu, Qunjian; Zeng, Ying; Zhang, Chi; Tong, Li; Yan, Bin

2018-01-01

The electroencephalogram (EEG) signal represents a subject’s specific brain activity patterns and is considered as an ideal biometric given its superior forgery prevention. However, the accuracy and stability of the current EEG-based person authentication systems are still unsatisfactory in practical application. In this paper, a multi-task EEG-based person authentication system combining eye blinking is proposed, which can achieve high precision and robustness. Firstly, we design a novel EEG-based biometric evoked paradigm using self- or non-self-face rapid serial visual presentation (RSVP). The designed paradigm could obtain a distinct and stable biometric trait from EEG with a lower time cost. Secondly, the event-related potential (ERP) features and morphological features are extracted from EEG signals and eye blinking signals, respectively. Thirdly, convolutional neural network and back propagation neural network are severally designed to gain the score estimation of EEG features and eye blinking features. Finally, a score fusion technology based on least square method is proposed to get the final estimation score. The performance of multi-task authentication system is improved significantly compared to the system using EEG only, with an increasing average accuracy from 92.4% to 97.6%. Moreover, open-set authentication tests for additional imposters and permanence tests for users are conducted to simulate the practical scenarios, which have never been employed in previous EEG-based person authentication systems. A mean false accepted rate (FAR) of 3.90% and a mean false rejected rate (FRR) of 3.87% are accomplished in open-set authentication tests and permanence tests, respectively, which illustrate the open-set authentication and permanence capability of our systems. PMID:29364848

An EEG-Based Person Authentication System with Open-Set Capability Combining Eye Blinking Signals.

PubMed

Wu, Qunjian; Zeng, Ying; Zhang, Chi; Tong, Li; Yan, Bin

2018-01-24

The electroencephalogram (EEG) signal represents a subject's specific brain activity patterns and is considered as an ideal biometric given its superior forgery prevention. However, the accuracy and stability of the current EEG-based person authentication systems are still unsatisfactory in practical application. In this paper, a multi-task EEG-based person authentication system combining eye blinking is proposed, which can achieve high precision and robustness. Firstly, we design a novel EEG-based biometric evoked paradigm using self- or non-self-face rapid serial visual presentation (RSVP). The designed paradigm could obtain a distinct and stable biometric trait from EEG with a lower time cost. Secondly, the event-related potential (ERP) features and morphological features are extracted from EEG signals and eye blinking signals, respectively. Thirdly, convolutional neural network and back propagation neural network are severally designed to gain the score estimation of EEG features and eye blinking features. Finally, a score fusion technology based on least square method is proposed to get the final estimation score. The performance of multi-task authentication system is improved significantly compared to the system using EEG only, with an increasing average accuracy from 92.4% to 97.6%. Moreover, open-set authentication tests for additional imposters and permanence tests for users are conducted to simulate the practical scenarios, which have never been employed in previous EEG-based person authentication systems. A mean false accepted rate (FAR) of 3.90% and a mean false rejected rate (FRR) of 3.87% are accomplished in open-set authentication tests and permanence tests, respectively, which illustrate the open-set authentication and permanence capability of our systems.

High density scalp EEG in frontal lobe epilepsy.

PubMed

Feyissa, Anteneh M; Britton, Jeffrey W; Van Gompel, Jamie; Lagerlund, Terrance L; So, Elson; Wong-Kisiel, Lilly C; Cascino, Gregory C; Brinkman, Benjamin H; Nelson, Cindy L; Watson, Robert; Worrell, Gregory A

2017-01-01

Localization of seizures in frontal lobe epilepsy using the 10-20 system scalp EEG is often challenging because neocortical seizure can spread rapidly, significant muscle artifact, and the suboptimal spatial resolution for seizure generators involving mesial frontal lobe cortex. Our aim in this study was to determine the value of visual interpretation of 76 channel high density EEG (hdEEG) monitoring (10-10 system) in patients with suspected frontal lobe epilepsy, and to evaluate concordance with MRI, subtraction ictal SPECT co-registered to MRI (SISCOM), conventional EEG, and intracranial EEG (iEEG). We performed a retrospective cohort study of 14 consecutive patients who underwent hdEEG monitoring for suspected frontal lobe seizures. The gold standard for localization was considered to be iEEG. Concordance of hdEEG findings with MRI, subtraction ictal SPECT co-registered to MRI (SISCOM), conventional 10-20 EEG, and iEEG as well as correlation of hdEEG localization with surgical outcome were examined. hdEEG localization was concordant with iEEG in 12/14 and was superior to conventional EEG 3/14 (p<0.01) and SISCOM 3/12 (p<0.01). hdEEG correctly lateralized seizure onset in 14/14 cases, compared to 9/14 (p=0.04) cases with conventional EEG. Seven patients underwent surgical resection, of whom five were seizure free. hdEEG monitoring should be considered in patients with suspected frontal epilepsy requiring localization of epileptogenic brain. hdEEG may assist in developing a hypothesis for iEEG monitoring and could potentially augment EEG source localization. Published by Elsevier B.V.

Power Laws from Linear Neuronal Cable Theory: Power Spectral Densities of the Soma Potential, Soma Membrane Current and Single-Neuron Contribution to the EEG

PubMed Central

Pettersen, Klas H.; Lindén, Henrik; Tetzlaff, Tom; Einevoll, Gaute T.

2014-01-01

Power laws, that is, power spectral densities (PSDs) exhibiting behavior for large frequencies f, have been observed both in microscopic (neural membrane potentials and currents) and macroscopic (electroencephalography; EEG) recordings. While complex network behavior has been suggested to be at the root of this phenomenon, we here demonstrate a possible origin of such power laws in the biophysical properties of single neurons described by the standard cable equation. Taking advantage of the analytical tractability of the so called ball and stick neuron model, we derive general expressions for the PSD transfer functions for a set of measures of neuronal activity: the soma membrane current, the current-dipole moment (corresponding to the single-neuron EEG contribution), and the soma membrane potential. These PSD transfer functions relate the PSDs of the respective measurements to the PSDs of the noisy input currents. With homogeneously distributed input currents across the neuronal membrane we find that all PSD transfer functions express asymptotic high-frequency power laws with power-law exponents analytically identified as for the soma membrane current, for the current-dipole moment, and for the soma membrane potential. Comparison with available data suggests that the apparent power laws observed in the high-frequency end of the PSD spectra may stem from uncorrelated current sources which are homogeneously distributed across the neural membranes and themselves exhibit pink () noise distributions. While the PSD noise spectra at low frequencies may be dominated by synaptic noise, our findings suggest that the high-frequency power laws may originate in noise from intrinsic ion channels. The significance of this finding goes beyond neuroscience as it demonstrates how power laws with a wide range of values for the power-law exponent α may arise from a simple, linear partial differential equation. PMID:25393030

Power laws from linear neuronal cable theory: power spectral densities of the soma potential, soma membrane current and single-neuron contribution to the EEG.

PubMed

Pettersen, Klas H; Lindén, Henrik; Tetzlaff, Tom; Einevoll, Gaute T

2014-11-01

Power laws, that is, power spectral densities (PSDs) exhibiting 1/f(α) behavior for large frequencies f, have been observed both in microscopic (neural membrane potentials and currents) and macroscopic (electroencephalography; EEG) recordings. While complex network behavior has been suggested to be at the root of this phenomenon, we here demonstrate a possible origin of such power laws in the biophysical properties of single neurons described by the standard cable equation. Taking advantage of the analytical tractability of the so called ball and stick neuron model, we derive general expressions for the PSD transfer functions for a set of measures of neuronal activity: the soma membrane current, the current-dipole moment (corresponding to the single-neuron EEG contribution), and the soma membrane potential. These PSD transfer functions relate the PSDs of the respective measurements to the PSDs of the noisy input currents. With homogeneously distributed input currents across the neuronal membrane we find that all PSD transfer functions express asymptotic high-frequency 1/f(α) power laws with power-law exponents analytically identified as α∞(I) = 1/2 for the soma membrane current, α∞(p) = 3/2 for the current-dipole moment, and α∞(V) = 2 for the soma membrane potential. Comparison with available data suggests that the apparent power laws observed in the high-frequency end of the PSD spectra may stem from uncorrelated current sources which are homogeneously distributed across the neural membranes and themselves exhibit pink (1/f) noise distributions. While the PSD noise spectra at low frequencies may be dominated by synaptic noise, our findings suggest that the high-frequency power laws may originate in noise from intrinsic ion channels. The significance of this finding goes beyond neuroscience as it demonstrates how 1/f(α) power laws with a wide range of values for the power-law exponent α may arise from a simple, linear partial differential equation.

An EEG Data Investigation Using Only Artifacts

DTIC Science & Technology

2017-02-22

approach, called artifact separation, was developed to enable the consumer of the EEG data to decide how to handle artifacts. The current...mediation approach, called artifact separation, was developed to enable the consumer of the EEG data to decide how to handle artifacts. The current...contaminated. Having the spectral results flagged as containing an artifact, means that the consumer of the data has the freedom to decide how to

Wavelet-Based Artifact Identification and Separation Technique for EEG Signals during Galvanic Vestibular Stimulation

PubMed Central

Adib, Mani; Cretu, Edmond

2013-01-01

We present a new method for removing artifacts in electroencephalography (EEG) records during Galvanic Vestibular Stimulation (GVS). The main challenge in exploiting GVS is to understand how the stimulus acts as an input to brain. We used EEG to monitor the brain and elicit the GVS reflexes. However, GVS current distribution throughout the scalp generates an artifact on EEG signals. We need to eliminate this artifact to be able to analyze the EEG signals during GVS. We propose a novel method to estimate the contribution of the GVS current in the EEG signals at each electrode by combining time-series regression methods with wavelet decomposition methods. We use wavelet transform to project the recorded EEG signal into various frequency bands and then estimate the GVS current distribution in each frequency band. The proposed method was optimized using simulated signals, and its performance was compared to well-accepted artifact removal methods such as ICA-based methods and adaptive filters. The results show that the proposed method has better performance in removing GVS artifacts, compared to the others. Using the proposed method, a higher signal to artifact ratio of −1.625 dB was achieved, which outperformed other methods such as ICA-based methods, regression methods, and adaptive filters. PMID:23956786

A Robust Current Pattern for the Detection of Intraventricular Hemorrhage in Neonates Using Electrical Impedance Tomography

PubMed Central

Tang, T.; Oh, Sungho; Sadleir, R. J.

2010-01-01

We compared two 16-electrode electrical impedance tomography (EIT) current patterns on their ability to reconstruct and quantify small amounts of bleeding inside a neonatal human head using both simulated and phantom data. The current patterns used were an adjacent injection RING pattern (with electrodes located equidistantly on the equator of a sphere) and an EEG current pattern based on the 10–20 EEG electrode layout. Structures mimicking electrically important structures in the infant skull were included in a spherical numerical forward model and their effects on reconstructions were determined. The EEG pattern was found to be a better topology to localize and quantify anomalies within lateral ventricular regions. The RING electrode pattern could not reconstruct anomaly location well, as it could not distinguish different axial positions. The quantification accuracy of the RING pattern was as good as the EEG pattern in noise-free environments. However, the EEG pattern showed better quantification ability than the RING pattern when noise was added. The performance of the EEG pattern improved further with respect to the RING pattern when a fontanel was included in forward models. Significantly better resolution and contrast of reconstructed anomalies was achieved when generated from a model containing such an opening and 50 dB added noise. The EEG method was further applied to reconstruct data from a realistic neonatal head model. Overall, acceptable reconstructions and quantification results were obtained using this model and the homogeneous spherical forward model. PMID:20238166

Reward feedback stimuli elicit high-beta EEG oscillations in human dorsolateral prefrontal cortex

PubMed Central

Hosseini, Azadeh Haji; Holroyd, Clay B.

2015-01-01

Reward-related feedback stimuli have been observed to elicit a burst of power in the beta frequency range over frontal areas of the human scalp. Recent discussions have suggested possible neural sources for this activity but there is a paucity of empirical evidence on the question. Here we recorded EEG from participants while they navigated a virtual T-maze to find monetary rewards. Consistent with previous studies, we found that the reward feedback stimuli elicited an increase in beta power (20–30 Hz) over a right-frontal area of the scalp. Source analysis indicated that this signal was produced in the right dorsolateral prefrontal cortex (DLPFC). These findings align with previous observations of reward-related beta oscillations in the DLPFC in non-human primates. We speculate that increased power in the beta frequency range following reward receipt reflects the activation of task-related neural assemblies that encode the stimulus-response mapping in working memory. PMID:26278335

Combined electroencephalography-functional magnetic resonance imaging and electrical source imaging improves localization of pediatric focal epilepsy.

PubMed

Centeno, Maria; Tierney, Tim M; Perani, Suejen; Shamshiri, Elhum A; St Pier, Kelly; Wilkinson, Charlotte; Konn, Daniel; Vulliemoz, Serge; Grouiller, Frédéric; Lemieux, Louis; Pressler, Ronit M; Clark, Christopher A; Cross, J Helen; Carmichael, David W

2017-08-01

Surgical treatment in epilepsy is effective if the epileptogenic zone (EZ) can be correctly localized and characterized. Here we use simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) data to derive EEG-fMRI and electrical source imaging (ESI) maps. Their yield and their individual and combined ability to (1) localize the EZ and (2) predict seizure outcome were then evaluated. Fifty-three children with drug-resistant epilepsy underwent EEG-fMRI. Interictal discharges were mapped using both EEG-fMRI hemodynamic responses and ESI. A single localization was derived from each individual test (EEG-fMRI global maxima [GM]/ESI maximum) and from the combination of both maps (EEG-fMRI/ESI spatial intersection). To determine the localization accuracy and its predictive performance, the individual and combined test localizations were compared to the presumed EZ and to the postsurgical outcome. Fifty-two of 53 patients had significant maps: 47 of 53 for EEG-fMRI, 44 of 53 for ESI, and 34 of 53 for both. The EZ was well characterized in 29 patients; 26 had an EEG-fMRI GM localization that was correct in 11, 22 patients had ESI localization that was correct in 17, and 12 patients had combined EEG-fMRI and ESI that was correct in 11. Seizure outcome following resection was correctly predicted by EEG-fMRI GM in 8 of 20 patients, and by the ESI maximum in 13 of 16. The combined EEG-fMRI/ESI region entirely predicted outcome in 9 of 9 patients, including 3 with no lesion visible on MRI. EEG-fMRI combined with ESI provides a simple unbiased localization that may predict surgery better than each individual test, including in MRI-negative patients. Ann Neurol 2017;82:278-287. © 2017 American Neurological Association.

Effective Connectivity of Cortical Sensorimotor Networks During Finger Movement Tasks: A Simultaneous fNIRS, fMRI, EEG Study.

PubMed

Anwar, A R; Muthalib, M; Perrey, S; Galka, A; Granert, O; Wolff, S; Heute, U; Deuschl, G; Raethjen, J; Muthuraman, Muthuraman

2016-09-01

Recently, interest has been growing to understand the underlying dynamic directional relationship between simultaneously activated regions of the brain during motor task performance. Such directionality analysis (or effective connectivity analysis), based on non-invasive electrophysiological (electroencephalography-EEG) and hemodynamic (functional near infrared spectroscopy-fNIRS; and functional magnetic resonance imaging-fMRI) neuroimaging modalities can provide an estimate of the motor task-related information flow from one brain region to another. Since EEG, fNIRS and fMRI modalities achieve different spatial and temporal resolutions of motor-task related activation in the brain, the aim of this study was to determine the effective connectivity of cortico-cortical sensorimotor networks during finger movement tasks measured by each neuroimaging modality. Nine healthy subjects performed right hand finger movement tasks of different complexity (simple finger tapping-FT, simple finger sequence-SFS, and complex finger sequence-CFS). We focused our observations on three cortical regions of interest (ROIs), namely the contralateral sensorimotor cortex (SMC), the contralateral premotor cortex (PMC) and the contralateral dorsolateral prefrontal cortex (DLPFC). We estimated the effective connectivity between these ROIs using conditional Granger causality (GC) analysis determined from the time series signals measured by fMRI (blood oxygenation level-dependent-BOLD), fNIRS (oxygenated-O2Hb and deoxygenated-HHb hemoglobin), and EEG (scalp and source level analysis) neuroimaging modalities. The effective connectivity analysis showed significant bi-directional information flow between the SMC, PMC, and DLPFC as determined by the EEG (scalp and source), fMRI (BOLD) and fNIRS (O2Hb and HHb) modalities for all three motor tasks. However the source level EEG GC values were significantly greater than the other modalities. In addition, only the source level EEG showed a significantly greater forward than backward information flow between the ROIs. This simultaneous fMRI, fNIRS and EEG study has shown through independent GC analysis of the respective time series that a bi-directional effective connectivity occurs within a cortico-cortical sensorimotor network (SMC, PMC and DLPFC) during finger movement tasks.

Non-linear Parameter Estimates from Non-stationary MEG Data

PubMed Central

Martínez-Vargas, Juan D.; López, Jose D.; Baker, Adam; Castellanos-Dominguez, German; Woolrich, Mark W.; Barnes, Gareth

2016-01-01

We demonstrate a method to estimate key electrophysiological parameters from resting state data. In this paper, we focus on the estimation of head-position parameters. The recovery of these parameters is especially challenging as they are non-linearly related to the measured field. In order to do this we use an empirical Bayesian scheme to estimate the cortical current distribution due to a range of laterally shifted head-models. We compare different methods of approaching this problem from the division of M/EEG data into stationary sections and performing separate source inversions, to explaining all of the M/EEG data with a single inversion. We demonstrate this through estimation of head position in both simulated and empirical resting state MEG data collected using a head-cast. PMID:27597815

Graph theory findings in the pathophysiology of temporal lobe epilepsy

PubMed Central

Chiang, Sharon; Haneef, Zulfi

2014-01-01

Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy. Accumulating evidence has shown that TLE is a disorder of abnormal epileptogenic networks, rather than focal sources. Graph theory allows for a network-based representation of TLE brain networks, and has potential to illuminate characteristics of brain topology conducive to TLE pathophysiology, including seizure initiation and spread. We review basic concepts which we believe will prove helpful in interpreting results rapidly emerging from graph theory research in TLE. In addition, we summarize the current state of graph theory findings in TLE as they pertain its pathophysiology. Several common findings have emerged from the many modalities which have been used to study TLE using graph theory, including structural MRI, diffusion tensor imaging, surface EEG, intracranial EEG, magnetoencephalography, functional MRI, cell cultures, simulated models, and mouse models, involving increased regularity of the interictal network configuration, altered local segregation and global integration of the TLE network, and network reorganization of temporal lobe and limbic structures. As different modalities provide different views of the same phenomenon, future studies integrating data from multiple modalities are needed to clarify findings and contribute to the formation of a coherent theory on the pathophysiology of TLE. PMID:24831083

The dorsal medial frontal cortex mediates automatic motor inhibition in uncertain contexts: evidence from combined fMRI and EEG studies.

PubMed

Albares, Marion; Lio, Guillaume; Criaud, Marion; Anton, Jean-Luc; Desmurget, Michel; Boulinguez, Philippe

2014-11-01

Response inhibition is commonly thought to rely on voluntary, reactive, selective, and relatively slow prefrontal mechanisms. In contrast, we suggest here that response inhibition is achieved automatically, nonselectively, within very short delays in uncertain environments. We modified a classical go/nogo protocol to probe context-dependent inhibitory mechanisms. Because no single neuroimaging method can definitely disentangle neural excitation and inhibition, we combined fMRI and EEG recordings in healthy humans. Any stimulus (go or nogo) presented in an uncertain context requiring action restraint was found to evoke activity changes in the supplementary motor complex (SMC) with respect to a control condition in which no response inhibition was required. These changes included: (1) An increase in event-related BOLD activity, (2) an attenuation of the early (170 ms) event related potential generated by a single, consistent source isolated by advanced blind source separation, and (3) an increase in the evoked-EEG Alpha power of this source. Considered together, these results suggest that the BOLD signal evoked by any stimulus in the SMC when the situation is unpredictable can be driven by automatic, nonselective, context-dependent inhibitory activities. This finding reveals the paradoxical mechanisms by which voluntary control of action may be achieved. The ability to provide controlled responses in unpredictable environments would require setting-up the automatic self-inhibitory circuitry within the SMC. Conversely, enabling automatic behavior when the environment becomes predictable would require top-down control to deactivate anticipatorily and temporarily the inhibitory set. Copyright © 2014 Wiley Periodicals, Inc.

The Effect of Electroencephalogram (EEG) Reference Choice on Information-Theoretic Measures of the Complexity and Integration of EEG Signals

PubMed Central

Trujillo, Logan T.; Stanfield, Candice T.; Vela, Ruben D.

2017-01-01

Converging evidence suggests that human cognition and behavior emerge from functional brain networks interacting on local and global scales. We investigated two information-theoretic measures of functional brain segregation and integration—interaction complexity CI(X), and integration I(X)—as applied to electroencephalographic (EEG) signals and how these measures are affected by choice of EEG reference. CI(X) is a statistical measure of the system entropy accounted for by interactions among its elements, whereas I(X) indexes the overall deviation from statistical independence of the individual elements of a system. We recorded 72 channels of scalp EEG from human participants who sat in a wakeful resting state (interleaved counterbalanced eyes-open and eyes-closed blocks). CI(X) and I(X) of the EEG signals were computed using four different EEG references: linked-mastoids (LM) reference, average (AVG) reference, a Laplacian (LAP) “reference-free” transformation, and an infinity (INF) reference estimated via the Reference Electrode Standardization Technique (REST). Fourier-based power spectral density (PSD), a standard measure of resting state activity, was computed for comparison and as a check of data integrity and quality. We also performed dipole source modeling in order to assess the accuracy of neural source CI(X) and I(X) estimates obtained from scalp-level EEG signals. CI(X) was largest for the LAP transformation, smallest for the LM reference, and at intermediate values for the AVG and INF references. I(X) was smallest for the LAP transformation, largest for the LM reference, and at intermediate values for the AVG and INF references. Furthermore, across all references, CI(X) and I(X) reliably distinguished between resting-state conditions (larger values for eyes-open vs. eyes-closed). These findings occurred in the context of the overall expected pattern of resting state PSD. Dipole modeling showed that simulated scalp EEG-level CI(X) and I(X) reflected changes in underlying neural source dependencies, but only for higher levels of integration and with highest accuracy for the LAP transformation. Our observations suggest that the Laplacian-transformation should be preferred for the computation of scalp-level CI(X) and I(X) due to its positive impact on EEG signal quality and statistics, reduction of volume-conduction, and the higher accuracy this provides when estimating scalp-level EEG complexity and integration. PMID:28790884

Modalities of Thinking: State and Trait Effects on Cross-Frequency Functional Independent Brain Networks.

PubMed

Milz, Patricia; Pascual-Marqui, Roberto D; Lehmann, Dietrich; Faber, Pascal L

2016-05-01

Functional states of the brain are constituted by the temporally attuned activity of spatially distributed neural networks. Such networks can be identified by independent component analysis (ICA) applied to frequency-dependent source-localized EEG data. This methodology allows the identification of networks at high temporal resolution in frequency bands of established location-specific physiological functions. EEG measurements are sensitive to neural activity changes in cortical areas of modality-specific processing. We tested effects of modality-specific processing on functional brain networks. Phasic modality-specific processing was induced via tasks (state effects) and tonic processing was assessed via modality-specific person parameters (trait effects). Modality-specific person parameters and 64-channel EEG were obtained from 70 male, right-handed students. Person parameters were obtained using cognitive style questionnaires, cognitive tests, and thinking modality self-reports. EEG was recorded during four conditions: spatial visualization, object visualization, verbalization, and resting. Twelve cross-frequency networks were extracted from source-localized EEG across six frequency bands using ICA. RMANOVAs, Pearson correlations, and path modelling examined effects of tasks and person parameters on networks. Results identified distinct state- and trait-dependent functional networks. State-dependent networks were characterized by decreased, trait-dependent networks by increased alpha activity in sub-regions of modality-specific pathways. Pathways of competing modalities showed opposing alpha changes. State- and trait-dependent alpha were associated with inhibitory and automated processing, respectively. Antagonistic alpha modulations in areas of competing modalities likely prevent intruding effects of modality-irrelevant processing. Considerable research suggested alpha modulations related to modality-specific states and traits. This study identified the distinct electrophysiological cortical frequency-dependent networks within which they operate.

Use of EEG Monitoring and Management of Non-Convulsive Seizures in Critically Ill Patients: A Survey of Neurologists

PubMed Central

Abend, Nicholas S.; Dlugos, Dennis J.; Hahn, Cecil D.; Hirsch, Lawrence J.; Herman, Susan T.

2010-01-01

Background Continuous EEG monitoring (cEEG) of critically ill patients is frequently utilized to detect non-convulsive seizures (NCS) and status epilepticus (NCSE). The indications for cEEG, as well as when and how to treat NCS, remain unclear. We aimed to describe the current practice of cEEG in critically ill patients to define areas of uncertainty that could aid in designing future research. Methods We conducted an international survey of neurologists focused on cEEG utilization and NCS management. Results Three-hundred and thirty physicians completed the survey. 83% use cEEG at least once per month and 86% manage NCS at least five times per year. The use of cEEG in patients with altered mental status was common (69%), with higher use if the patient had a prior convulsion (89%) or abnormal eye movements (85%). Most respondents would continue cEEG for 24 h. If NCS or NCSE is identified, the most common anticonvulsants administered were phenytoin/fosphenytoin, lorazepam, or levetiracetam, with slightly more use of levetiracetam for NCS than NCSE. Conclusions Continuous EEG monitoring (cEEG) is commonly employed in critically ill patients to detect NCS and NCSE. However, there is substantial variability in current practice related to cEEG indications and duration and to management of NCS and NCSE. The fact that such variability exists in the management of this common clinical problem suggests that further prospective study is needed. Multiple points of uncertainty are identified that require investigation. PMID:20198513

Muscle and eye movement artifact removal prior to EEG source localization.

PubMed

Hallez, Hans; Vergult, Anneleen; Phlypo, Ronald; Van Hese, Peter; De Clercq, Wim; D'Asseler, Yves; Van de Walle, Rik; Vanrumste, Bart; Van Paesschen, Wim; Van Huffel, Sabine; Lemahieu, Ignace

2006-01-01

Muscle and eye movement artifacts are very prominent in the ictal EEG of patients suffering from epilepsy, thus making the dipole localization of ictal activity very unreliable. Recently, two techniques (BSS-CCA and pSVD) were developed to remove those artifacts. The purpose of this study is to assess whether the removal of muscle and eye movement artifacts improves the EEG dipole source localization. We used a total of 8 EEG fragments, each from another patient, first unfiltered, then filtered by the BSS-CCA and pSVD. In both the filtered and unfiltered EEG fragments we estimated multiple dipoles using RAP-MUSIC. The resulting dipoles were subjected to a K-means clustering algorithm, to extract the most prominent cluster. We found that the removal of muscle and eye artifact results to tighter and more clear dipole clusters. Furthermore, we found that localization of the filtered EEG corresponded with the localization derived from the ictal SPECT in 7 of the 8 patients. Therefore, we can conclude that the BSS-CCA and pSVD improve localization of ictal activity, thus making the localization more reliable for the presurgical evaluation of the patient.

Changes in cortical activity measured with EEG during a high-intensity cycling exercise

PubMed Central

Cortese, Filomeno; Maurer, Christian; Baltich, Jennifer; Protzner, Andrea B.; Nigg, Benno M.

2015-01-01

This study investigated the effects of a high-intensity cycling exercise on changes in spectral and temporal aspects of electroencephalography (EEG) measured from 10 experienced cyclists. Cyclists performed a maximum aerobic power test on the first testing day followed by a time-to-exhaustion trial at 85% of their maximum power output on 2 subsequent days that were separated by ∼48 h. EEG was recorded using a 64-channel system at 500 Hz. Independent component (IC) analysis parsed the EEG scalp data into maximal ICs. An equivalent current dipole model was calculated for each IC, and results were clustered across subjects. A time-frequency analysis of the identified electrocortical clusters was performed to investigate the magnitude and timing of event-related spectral perturbations. Significant changes (P < 0.05) in electrocortical activity were found in frontal, supplementary motor and parietal areas of the cortex. Overall, there was a significant increase in EEG power as fatigue developed throughout the exercise. The strongest increase was found in the frontal area of the cortex. The timing of event-related desynchronization within the supplementary motor area corresponds with the onset of force production and the transition from flexion to extension in the pedaling cycle. The results indicate an involvement of the cerebral cortex during the pedaling task that most likely involves executive control function, as well as motor planning and execution. PMID:26538604

Kernel temporal enhancement approach for LORETA source reconstruction using EEG data.

PubMed

Torres-Valencia, Cristian A; Santamaria, M Claudia Joana; Alvarez, Mauricio A

2016-08-01

Reconstruction of brain sources from magnetoencephalography and electroencephalography (M/EEG) data is a well known problem in the neuroengineering field. A inverse problem should be solved and several methods have been proposed. Low Resolution Electromagnetic Tomography (LORETA) and the different variations proposed as standardized LORETA (sLORETA) and the standardized weighted LORETA (swLORETA) have solved the inverse problem following a non-parametric approach, that is by setting dipoles in the whole brain domain in order to estimate the dipole positions from the M/EEG data and assuming some spatial priors. Errors in the reconstruction of sources are presented due the low spatial resolution of the LORETA framework and the influence of noise in the observable data. In this work a kernel temporal enhancement (kTE) is proposed in order to build a preprocessing stage of the data that allows in combination with the swLORETA method a improvement in the source reconstruction. The results are quantified in terms of three dipole error localization metrics and the strategy of swLORETA + kTE obtained the best results across different signal to noise ratio (SNR) in random dipoles simulation from synthetic EEG data.

EEG neural oscillatory dynamics reveal semantic and response conflict at difference levels of conflict awareness

PubMed Central

Jiang, Jun; Zhang, Qinglin; Van Gaal, Simon

2015-01-01

Although previous work has shown that conflict can be detected in the absence of awareness, it is unknown how different sources of conflict (i.e., semantic, response) are processed in the human brain and whether these processes are differently modulated by conflict awareness. To explore this issue, we extracted oscillatory power dynamics from electroencephalographic (EEG) data recorded while human participants performed a modified version of the Stroop task. Crucially, in this task conflict awareness was manipulated by masking a conflict-inducing color word preceding a color patch target. We isolated semantic from response conflict by introducing four color words/patches, of which two were matched to the same response. We observed that both semantic as well as response conflict were associated with mid-frontal theta-band and parietal alpha-band power modulations, irrespective of the level of conflict awareness (high vs. low), although awareness of conflict increased these conflict-related power dynamics. These results show that both semantic and response conflict can be processed in the human brain and suggest that the neural oscillatory mechanisms in EEG reflect mainly “domain general” conflict processing mechanisms, instead of conflict source specific effects. PMID:26169473

EEG neural oscillatory dynamics reveal semantic and response conflict at difference levels of conflict awareness.

PubMed

Jiang, Jun; Zhang, Qinglin; Van Gaal, Simon

2015-07-14

Although previous work has shown that conflict can be detected in the absence of awareness, it is unknown how different sources of conflict (i.e., semantic, response) are processed in the human brain and whether these processes are differently modulated by conflict awareness. To explore this issue, we extracted oscillatory power dynamics from electroencephalographic (EEG) data recorded while human participants performed a modified version of the Stroop task. Crucially, in this task conflict awareness was manipulated by masking a conflict-inducing color word preceding a color patch target. We isolated semantic from response conflict by introducing four color words/patches, of which two were matched to the same response. We observed that both semantic as well as response conflict were associated with mid-frontal theta-band and parietal alpha-band power modulations, irrespective of the level of conflict awareness (high vs. low), although awareness of conflict increased these conflict-related power dynamics. These results show that both semantic and response conflict can be processed in the human brain and suggest that the neural oscillatory mechanisms in EEG reflect mainly "domain general" conflict processing mechanisms, instead of conflict source specific effects.

The functional significance of EEG microstates--Associations with modalities of thinking.

PubMed

Milz, P; Faber, P L; Lehmann, D; Koenig, T; Kochi, K; Pascual-Marqui, R D

2016-01-15

The momentary, global functional state of the brain is reflected by its electric field configuration. Cluster analytical approaches consistently extracted four head-surface brain electric field configurations that optimally explain the variance of their changes across time in spontaneous EEG recordings. These four configurations are referred to as EEG microstate classes A, B, C, and D and have been associated with verbal/phonological, visual, subjective interoceptive-autonomic processing, and attention reorientation, respectively. The present study tested these associations via an intra-individual and inter-individual analysis approach. The intra-individual approach tested the effect of task-induced increased modality-specific processing on EEG microstate parameters. The inter-individual approach tested the effect of personal modality-specific parameters on EEG microstate parameters. We obtained multichannel EEG from 61 healthy, right-handed, male students during four eyes-closed conditions: object-visualization, spatial-visualization, verbalization (6 runs each), and resting (7 runs). After each run, we assessed participants' degrees of object-visual, spatial-visual, and verbal thinking using subjective reports. Before and after the recording, we assessed modality-specific cognitive abilities and styles using nine cognitive tests and two questionnaires. The EEG of all participants, conditions, and runs was clustered into four classes of EEG microstates (A, B, C, and D). RMANOVAs, ANOVAs and post-hoc paired t-tests compared microstate parameters between conditions. TANOVAs compared microstate class topographies between conditions. Differences were localized using eLORETA. Pearson correlations assessed interrelationships between personal modality-specific parameters and EEG microstate parameters during no-task resting. As hypothesized, verbal as opposed to visual conditions consistently affected the duration, occurrence, and coverage of microstate classes A and B. Contrary to associations suggested by previous reports, parameters were increased for class A during visualization, and class B during verbalization. In line with previous reports, microstate D parameters were increased during no-task resting compared to the three internal, goal-directed tasks. Topographic differences between conditions included particular sub-regions of components of the metabolic default mode network. Modality-specific personal parameters did not consistently correlate with microstate parameters except verbal cognitive style which correlated negatively with microstate class A duration and positively with class C occurrence. This is the first study that aimed to induce EEG microstate class parameter changes based on their hypothesized functional significance. Beyond the associations of microstate classes A and B with visual and verbal processing, respectively, our results suggest that a finely-tuned interplay between all four EEG microstate classes is necessary for the continuous formation of visual and verbal thoughts. Our results point to the possibility that the EEG microstate classes may represent the head-surface measured activity of intra-cortical sources primarily exhibiting inhibitory functions. However, additional studies are needed to verify and elaborate on this hypothesis. Copyright © 2015 Elsevier Inc. All rights reserved.

Screening EEG in Aircrew Selection: Clinical Aerospace Neurology Perspective

NASA Technical Reports Server (NTRS)

Clark, Jonathan B.; Riley, Terrence

2001-01-01

As clinical aerospace neurologists we do not favor using screening EEG in pilot selection on unselected and otherwise asymptomatic individuals. The role of EEG in aviation screening should be as an adjunct to diagnosis, and the decision to disqualify a pilot should never be based solely on the EEG. Although a policy of using a screening EEG in an unselected population might detect an individual with a potentially increased relative risk, it would needlessly exclude many applicants who would probably never have a seizure. A diagnostic test performed on an asymptomatic individual without clinical indications, in a population with a low prevalence of disease (seizure) may be of limited or possibly detrimental value. We feel that rather than do EEGs on all candidates, a better approach would be to perform an EEG for a specific indication, such as family history of seizure, single convulsion (seizure) , history of unexplained loss of consciousness or head injury. Routine screening EEGs in unselected aviation applications are not done without clinical indication in the U.S. Air Force, Navy, or NASA. The USAF discontinued routine screening EEGs for selection in 1978, the U.S. Navy discontinued it in 1981 , and NASA discontinued it in 1995. EEG as an aeromedical screening tool in the US Navy dates back to 1939. The US Navy routinely used EEGs to screen all aeromedical personnel from 1961 to 1981. The incidence of epileptiform activity on EEG in asymptomatic flight candidates ranges from 0.11 to 2.5%. In 3 studies of asymptomatic flight candidates with epileptiform activity on EEG followed for 2 to 15 years, 1 of 31 (3.2%), 1 of 30 (3.3%), and 0 of 14 (0%) developed a seizure, for a cumulative risk of an individual with an epileptiform EEG developing a seizure of 2.67% (2 in 75). Of 28,658 student naval aviation personnel screened 31 had spikes and/or slow waves on EEG, and only 1 later developed a seizure. Of the 28,627 who had a normal EEG, 4 later developed seizures, or .0139% (4/28627). After review of the value of the EEG as a screening tool, the US Navy now uses EEG only for certain clinical indications (head injury, unexplained loss of consciousness, family history of epilepsy, and abnormal neurological exam). Currently the US Navy does not use EEG for screening for any flight applicant without a neurologic indication. In the US Navy, an electroencephalographic pattern is determined to be epileptiform by a neurologist.

Integrating EEG and fMRI in epilepsy.

PubMed

Formaggio, Emanuela; Storti, Silvia Francesca; Bertoldo, Alessandra; Manganotti, Paolo; Fiaschi, Antonio; Toffolo, Gianna Maria

2011-02-14

Integrating electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) studies enables to non-invasively investigate human brain function and to find the direct correlation of these two important measures of brain activity. Presurgical evaluation of patients with epilepsy is one of the areas where EEG and fMRI integration has considerable clinical relevance for localizing the brain regions generating interictal epileptiform activity. The conventional analysis of EEG-fMRI data is based on the visual identification of the interictal epileptiform discharges (IEDs) on scalp EEG. The convolution of these EEG events, represented as stick functions, with a model of the fMRI response, i.e. the hemodynamic response function, provides the regressor for general linear model (GLM) analysis of fMRI data. However, the conventional analysis is not automatic and suffers of some subjectivity in IEDs classification. Here, we present an easy-to-use and automatic approach for combined EEG-fMRI analysis able to improve IEDs identification based on Independent Component Analysis and wavelet analysis. EEG signal due to IED is reconstructed and its wavelet power is used as a regressor in GLM. The method was validated on simulated data and then applied on real data set consisting of 2 normal subjects and 5 patients with partial epilepsy. In all continuous EEG-fMRI recording sessions a good quality EEG was obtained allowing the detection of spontaneous IEDs and the analysis of the related BOLD activation. The main clinical finding in EEG-fMRI studies of patients with partial epilepsy is that focal interictal slow-wave activity was invariably associated with increased focal BOLD responses in a spatially related brain area. Our study extends current knowledge on epileptic foci localization and confirms previous reports suggesting that BOLD activation associated with slow activity might have a role in localizing the epileptogenic region even in the absence of clear interictal spikes. Copyright © 2010 Elsevier Inc. All rights reserved.

The inverse problem in electroencephalography using the bidomain model of electrical activity.

PubMed

Lopez Rincon, Alejandro; Shimoda, Shingo

2016-12-01

Acquiring information about the distribution of electrical sources in the brain from electroencephalography (EEG) data remains a significant challenge. An accurate solution would provide an understanding of the inner mechanisms of the electrical activity in the brain and information about damaged tissue. In this paper, we present a methodology for reconstructing brain electrical activity from EEG data by using the bidomain formulation. The bidomain model considers continuous active neural tissue coupled with a nonlinear cell model. Using this technique, we aim to find the brain sources that give rise to the scalp potential recorded by EEG measurements taking into account a non-static reconstruction. We simulate electrical sources in the brain volume and compare the reconstruction to the minimum norm estimates (MNEs) and low resolution electrical tomography (LORETA) results. Then, with the EEG dataset from the EEG Motor Movement/Imagery Database of the Physiobank, we identify the reaction to visual stimuli by calculating the time between stimulus presentation and the spike in electrical activity. Finally, we compare the activation in the brain with the registered activation using the LinkRbrain platform. Our methodology shows an improved reconstruction of the electrical activity and source localization in comparison with MNE and LORETA. For the Motor Movement/Imagery Database, the reconstruction is consistent with the expected position and time delay generated by the stimuli. Thus, this methodology is a suitable option for continuously reconstructing brain potentials. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

Rheoencephalographic and electroencephalographic measures of cognitive workload: analytical procedures.

PubMed

Montgomery, L D; Montgomery, R W; Guisado, R

1995-05-01

This investigation demonstrates the feasibility of mental workload assessment by rheoencephalographic (REG) and multichannel electroencephalographic (EEG) monitoring. During the performance of this research, unique testing, analytical and display procedures were developed for REG and EEG monitoring that extend the current state of the art and provide valuable tools for the study of cerebral circulatory and neural activity during cognition. REG records are analyzed to provide indices of the right and left hemisphere hemodynamic changes that take place during each test sequence. The EEG data are modeled using regression techniques and mathematically transformed to provide energy-density distributions of the scalp electrostatic field. These procedures permit concurrent REG/EEG cognitive testing not possible with current techniques. The introduction of a system for recording and analysis of cognitive REG/EEG test sequences facilitates the study of learning and memory disorders, dementia and other encephalopathies.

Rheoencephalographic and electroencephalographic measures of cognitive workload: analytical procedures

NASA Technical Reports Server (NTRS)

Montgomery, L. D.; Montgomery, R. W.; Guisado, R.

1995-01-01

This investigation demonstrates the feasibility of mental workload assessment by rheoencephalographic (REG) and multichannel electroencephalographic (EEG) monitoring. During the performance of this research, unique testing, analytical and display procedures were developed for REG and EEG monitoring that extend the current state of the art and provide valuable tools for the study of cerebral circulatory and neural activity during cognition. REG records are analyzed to provide indices of the right and left hemisphere hemodynamic changes that take place during each test sequence. The EEG data are modeled using regression techniques and mathematically transformed to provide energy-density distributions of the scalp electrostatic field. These procedures permit concurrent REG/EEG cognitive testing not possible with current techniques. The introduction of a system for recording and analysis of cognitive REG/EEG test sequences facilitates the study of learning and memory disorders, dementia and other encephalopathies.

Analysis and visualization of single-trial event-related potentials

NASA Technical Reports Server (NTRS)

Jung, T. P.; Makeig, S.; Westerfield, M.; Townsend, J.; Courchesne, E.; Sejnowski, T. J.

2001-01-01

In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Copyright 2001 Wiley-Liss, Inc.

Hybrid Weighted Minimum Norm Method A new method based LORETA to solve EEG inverse problem.

PubMed

Song, C; Zhuang, T; Wu, Q

2005-01-01

This Paper brings forward a new method to solve EEG inverse problem. Based on following physiological characteristic of neural electrical activity source: first, the neighboring neurons are prone to active synchronously; second, the distribution of source space is sparse; third, the active intensity of the sources are high centralized, we take these prior knowledge as prerequisite condition to develop the inverse solution of EEG, and not assume other characteristic of inverse solution to realize the most commonly 3D EEG reconstruction map. The proposed algorithm takes advantage of LORETA's low resolution method which emphasizes particularly on 'localization' and FOCUSS's high resolution method which emphasizes particularly on 'separability'. The method is still under the frame of the weighted minimum norm method. The keystone is to construct a weighted matrix which takes reference from the existing smoothness operator, competition mechanism and study algorithm. The basic processing is to obtain an initial solution's estimation firstly, then construct a new estimation using the initial solution's information, repeat this process until the solutions under last two estimate processing is keeping unchanged.

Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM☆

PubMed Central

López, J.D.; Litvak, V.; Espinosa, J.J.; Friston, K.; Barnes, G.R.

2014-01-01

The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy—an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm. PMID:24041874

Improving data retention in EEG research with children using child-centered eye tracking

PubMed Central

Maguire, Mandy J.; Magnon, Grant; Fitzhugh, Anna E.

2014-01-01

Background Event Related Potentials (ERPs) elicited by visual stimuli have increased our understanding of developmental disorders and adult cognitive abilities for decades; however, these studies are very difficult with populations who cannot sustain visual attention such as infants and young children. Current methods for studying such populations include requiring a button response, which may be impossible for some participants, and experimenter monitoring, which is subject to error, highly variable, and spatially imprecise. New Method We developed a child-centered methodology to integrate EEG data acquisition and eye-tracking technologies that uses “attention-getters” in which stimulus display is contingent upon the child’s gaze. The goal was to increase the number of trials retained. Additionally, we used the eye-tracker to categorize and analyze the EEG data based on gaze to specific areas of the visual display, compared to analyzing based on stimulus presentation. Results Compared with Existing Methods The number of trials retained was substantially improved using the child-centered methodology compared to a button-press response in 7–8 year olds. In contrast, analyzing the EEG based on eye gaze to specific points within the visual display as opposed to stimulus presentation provided too few trials for reliable interpretation. Conclusions By using the linked EEG-eye-tracker we significantly increased data retention. With this method, studies can be completed with fewer participants and a wider range of populations. However, caution should be used when epoching based on participants’ eye gaze because, in this case, this technique provided substantially fewer trials. PMID:25251555

Emotion and resilience: a multilevel investigation of hemispheric electroencephalogram asymmetry and emotion regulation in maltreated and nonmaltreated children.

PubMed

Curtis, W John; Cicchetti, Dante

2007-01-01

The current study was a multilevel investigation of resilience, emotion regulation, and hemispheric electroencephalogram (EEG) asymmetry in a sample of maltreated and nonmaltreated school age children. It was predicted that the positive emotionality and increased emotion regulatory ability associated with resilient functioning would be associated with relatively greater left frontal EEG activity. The study also investigated differences in pathways to resilience between maltreated and nonmaltreated children. The findings indicated that EEG asymmetry across central cortical regions distinguished between resilient and nonresilient children, with greater left hemisphere activity characterizing those who were resilient. In addition, nonmaltreated children showed greater left hemisphere EEG activity across parietal cortical regions. There was also a significant interaction between resilience, maltreatment status, and gender for asymmetry at anterior frontal electrodes, where nonmaltreated resilient females had greater relative left frontal activity compared to more right frontal activity exhibited by resilient maltreated females. An observational measure of emotion regulation significantly contributed to the prediction of resilience in the maltreated and nonmaltreated children, but EEG asymmetry in central cortical regions independently predicted resilience only in the maltreated group. The findings are discussed in terms of their meaning for the development of resilient functioning.

Inter-hemispheric electroencephalography coherence analysis: assessing brain activity during monotonous driving.

PubMed

Jap, Budi Thomas; Lal, Sara; Fischer, Peter

2010-06-01

The current study investigated the effect of monotonous driving on inter-hemispheric electroencephalography (EEG) coherence. Twenty-four non-professional drivers were recruited to perform a fatigue instigating monotonous driving task while 30 channels of EEG were simultaneously recorded. The EEG recordings were then divided into 5 equal sections over the entire driving period for analysis. Inter-hemispheric coherence was computed from 5 homologous EEG electrode pairs (FP1-FP2, C3-C4, T7-T8, P7-P8, and O1-O2) for delta, theta, alpha and beta frequency bands. Results showed that frontal and occipital inter-hemispheric coherence values were significantly higher than central, parietal, and temporal sites for all four frequency bands (p<0.0001). In the alpha frequency band, significant difference was found between earlier and later driving sections (p=0.02). The coherence values in all EEG frequency bands were slightly increased at the end of the driving session, except for FP1-FP2 electrode pair, which showed no significant change in coherence in the beta frequency band at the end of the driving session. Copyright 2010 Elsevier B.V. All rights reserved.

OCCIPITAL SOURCES OF RESTING STATE ALPHA RHYTHMS ARE RELATED TO LOCAL GRAY MATTER DENSITY IN SUBJECTS WITH AMNESIC MILD COGNITIVE IMPAIRMENT AND ALZHEIMER’S DISEASE

PubMed Central

Claudio, Babiloni; Claudio, Del Percio; Marina, Boccardi; Roberta, Lizio; Susanna, Lopez; Filippo, Carducci; Nicola, Marzano; Andrea, Soricelli; Raffaele, Ferri; Ivano, Triggiani Antonio; Annapaola, Prestia; Serenella, Salinari; Rasser Paul, E; Erol, Basar; Francesco, Famà; Flavio, Nobili; Görsev, Yener; Durusu, Emek-Savaş Derya; Gesualdo, Loreto; Ciro, Mundi; Thompson Paul, M; Rossini Paolo, M.; Frisoni Giovanni, B

2014-01-01

Occipital sources of resting state electroencephalographic (EEG) alpha rhythms are abnormal, at the group level, in patients with amnesic mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Here we evaluated the hypothesis that amplitude of these occipital sources is related to neurodegeneration in occipital lobe as measured by magnetic resonance imaging (MRI). Resting-state eyes-closed EEG rhythms were recorded in 45 healthy elderly (Nold), 100 MCI, and 90 AD subjects. Neurodegeneration of occipital lobe was indexed by weighted averages of gray matter density (GMD), estimated from structural MRIs. EEG rhythms of interest were alpha 1 (8–10.5 Hz) and alpha 2 (10.5–13 Hz). EEG cortical sources were estimated by low resolution brain electromagnetic tomography (LORETA). Results showed a positive correlation between occipital GMD and amplitude of occipital alpha 1 sources in Nold, MCI and AD subjects as a whole group (r=0.3, p=0.000004, N=235). Furthermore, there was a positive correlation between amplitude of occipital alpha 1 sources and cognitive status as revealed by Mini Mental State Evaluation (MMSE) score across all subjects (r=0.38, p=0.000001, N=235). Finally, amplitude of occipital alpha 1 sources allowed a moderate classification of individual Nold and AD subjects (sensitivity: 87.8%; specificity: 66.7%; area under the Receiver Operating Characteristic (ROC) curve: 0.81). These results suggest that the amplitude of occipital sources of resting state alpha rhythms is related to AD neurodegeneration in occipital lobe along pathological aging. PMID:25442118

Experimental validation of the influence of white matter anisotropy on the intracranial EEG forward solution.

PubMed

Bangera, Nitin B; Schomer, Donald L; Dehghani, Nima; Ulbert, Istvan; Cash, Sydney; Papavasiliou, Steve; Eisenberg, Solomon R; Dale, Anders M; Halgren, Eric

2010-12-01

Forward solutions with different levels of complexity are employed for localization of current generators, which are responsible for the electric and magnetic fields measured from the human brain. The influence of brain anisotropy on the forward solution is poorly understood. The goal of this study is to validate an anisotropic model for the intracranial electric forward solution by comparing with the directly measured 'gold standard'. Dipolar sources are created at known locations in the brain and intracranial electroencephalogram (EEG) is recorded simultaneously. Isotropic models with increasing level of complexity are generated along with anisotropic models based on Diffusion tensor imaging (DTI). A Finite Element Method based forward solution is calculated and validated using the measured data. Major findings are (1) An anisotropic model with a linear scaling between the eigenvalues of the electrical conductivity tensor and water self-diffusion tensor in brain tissue is validated. The greatest improvement was obtained when the stimulation site is close to a region of high anisotropy. The model with a global anisotropic ratio of 10:1 between the eigenvalues (parallel: tangential to the fiber direction) has the worst performance of all the anisotropic models. (2) Inclusion of cerebrospinal fluid as well as brain anisotropy in the forward model is necessary for an accurate description of the electric field inside the skull. The results indicate that an anisotropic model based on the DTI can be constructed non-invasively and shows an improved performance when compared to the isotropic models for the calculation of the intracranial EEG forward solution.

Effects of Fipronil on the EEG of Long Evans Rats

EPA Science Inventory

We have reported that the non-stimulus driven EEG is differentially altered by deltamethrin or permethrin (Lyke and Herr, Toxicologist, 114(S-1):265, 2010). In the current study, we examined the ability to detect changes in EEG activity produced by fipronil, a phenylpyrazole pest...

Automatic and Direct Identification of Blink Components from Scalp EEG

PubMed Central

Kong, Wanzeng; Zhou, Zhanpeng; Hu, Sanqing; Zhang, Jianhai; Babiloni, Fabio; Dai, Guojun

2013-01-01

Eye blink is an important and inevitable artifact during scalp electroencephalogram (EEG) recording. The main problem in EEG signal processing is how to identify eye blink components automatically with independent component analysis (ICA). Taking into account the fact that the eye blink as an external source has a higher sum of correlation with frontal EEG channels than all other sources due to both its location and significant amplitude, in this paper, we proposed a method based on correlation index and the feature of power distribution to automatically detect eye blink components. Furthermore, we prove mathematically that the correlation between independent components and scalp EEG channels can be translating directly from the mixing matrix of ICA. This helps to simplify calculations and understand the implications of the correlation. The proposed method doesn't need to select a template or thresholds in advance, and it works without simultaneously recording an electrooculography (EOG) reference. The experimental results demonstrate that the proposed method can automatically recognize eye blink components with a high accuracy on entire datasets from 15 subjects. PMID:23959240

Long term impairment of cognitive functions and alterations of NMDAR subunits after continuous microwave exposure.

PubMed

Wang, Hui; Tan, Shengzhi; Xu, Xinping; Zhao, Li; Zhang, Jing; Yao, Binwei; Gao, Yabing; Zhou, Hongmei; Peng, Ruiyun

2017-11-01

The long term effects of continuous microwave exposure cannot be ignored for the simulation of the real environment and increasing concerns about the negative cognitive effects of microwave exposure. In this study, 220 male Wistar rats were exposed by a 2.856GHz radiation source with the average power density of 0, 2.5, 5 and 10mW/cm 2 for 6min/day, 5days/week and up to 6weeks. The MWM task, the EEG analysis, the hippocampus structure observation and the western blot were applied until the 12months after microwave exposure to detect the spatial learning and memory abilities, the cortical electrical activity, changes of hippocampal structure and the NMDAR subunits expressions. Results found that the rats in the 10mW/cm 2 group showed the decline of spatial learning and memory abilities and EEG disorders (the decrease of EEG frequencies, and increase of EEG amplitudes and delta wave powers). Moreover, changes of basic structure and ultrastructure of hippocampus also found in the 10 and 5mW/cm 2 groups. The decrease of NR 2A, 2B and p-NR2B might contribute to the impairment of cognitive functions. Our findings suggested that the continuous microwave exposure could cause the dose-dependent long term impairment of spatial learning and memory, the abnormalities of EEG and the hippocampal structure injuries. The decrease of NMDAR key subunits and phosphorylation of NR 2B might contribute to the cognitive impairment. Copyright © 2017 Elsevier Inc. All rights reserved.

Applied Neuroscience at the AFRL 711th Human Performance Wing

DTIC Science & Technology

2010-09-01

Support teaming and collaboration research performed by RHCPT 25 History of Applied Neuroscience Research First EEG studies of workload at AFRL...First to classify mental workload based on integrated EEG /ECG 26 First successful real- time workload classification Measured EEG workload in...complex tasks Closed-loop adaptive aiding based on EEG /ECG History of Applied Neuroscience Research 27 Current Applied Neuroscience Research • Mix of in

Your brain on bikes: P3, MMN/N2b, and baseline noise while pedaling a stationary bike.

PubMed

Scanlon, Joanna E M; Sieben, Alex J; Holyk, Kevin R; Mathewson, Kyle E

2017-06-01

Increasingly, there is a trend to measure brain activity in more ecologically realistic scenarios. Normally, the confines of the laboratory and sedentary tasks mitigate sources of electrical noise on EEG measurement. Moving EEG outside of the lab requires understanding of the impact of complex movements and activities on traditional EEG and ERP measures. Here, we recorded EEG with active electrodes while participants were either riding or sitting on a stationary bike in an electrical and sound-attenuated chamber in the lab. Participants performed an auditory oddball task, pressing a button when they detected rare target tones in a series of standard frequent tones. We quantified both the levels of spectral, single-trial baseline, and ERP baseline noise, as well as classic MMN/N2b and P3 ERP components measured during both biking and sitting still. We observed slight increases in posterior high frequency noise in the spectra, and increased noise in the baseline period during biking. However, morphologically and topographically similar MMN/N2b and P3 components were measured reliably while both biking and sitting. A quantification of the power to reliably measure ERPs as a function of the number of trials revealed slight increases in the number of trials needed during biking to achieve the same level of power. Taken in sum, our results confirm that classic ERPs can be measured reliably during biking activities in the lab. Future directions will employ these techniques outside the lab in ecologically valid situations. © 2017 Society for Psychophysiological Research.

Affective attitudes to face images associated with intracerebral EEG source location before face viewing.

PubMed

Pizzagalli, D; Koenig, T; Regard, M; Lehmann, D

1999-01-01

We investigated whether different, personality-related affective attitudes are associated with different brain electric field (EEG) sources before any emotional challenge (stimulus exposure). A 27-channel EEG was recorded in 15 subjects during eyes-closed resting. After recording, subjects rated 32 images of human faces for affective appeal. The subjects in the first (i.e., most negative) and fourth (i.e., most positive) quartile of general affective attitude were further analyzed. The EEG data (mean=25+/-4. 8 s/subject) were subjected to frequency-domain model dipole source analysis (FFT-Dipole-Approximation), resulting in 3-dimensional intracerebral source locations and strengths for the delta-theta, alpha, and beta EEG frequency band, and for the full range (1.5-30 Hz) band. Subjects with negative attitude (compared to those with positive attitude) showed the following source locations: more inferior for all frequency bands, more anterior for the delta-theta band, more posterior and more right for the alpha, beta and 1.5-30 Hz bands. One year later, the subjects were asked to rate the face images again. The rating scores for the same face images were highly correlated for all subjects, and original and retest affective mean attitude was highly correlated across subjects. The present results show that subjects with different affective attitudes to face images had different active, cerebral, neural populations in a task-free condition prior to viewing the images. We conclude that the brain functional state which implements affective attitude towards face images as a personality feature exists without elicitors, as a continuously present, dynamic feature of brain functioning. Copyright 1999 Elsevier Science B.V.

Identifying the effects of microsaccades in tripolar EEG signals.

PubMed

Bellisle, Rachel; Steele, Preston; Bartels, Rachel; Lei Ding; Sunderam, Sridhar; Besio, Walter

2017-07-01

Microsaccades are tiny, involuntary eye movements that occur during fixation, and they are necessary to human sight to maintain a sharp image and correct the effects of other fixational movements. Researchers have theorized and studied the effects of microsaccades on electroencephalography (EEG) signals to understand and eliminate the unwanted artifacts from EEG. The tripolar concentric ring electrode (TCRE) sensors are used to acquire TCRE EEG (tEEG). The tEEG detects extremely focal signals from directly below the TCRE sensor. We have noticed a slow wave frequency found in some tEEG recordings. Therefore, we conducted the current work to determine if there was a correlation between the slow wave in the tEEG and the microsaccades. This was done by analyzing the coherence of the frequency spectrums of both tEEG and eye movement in recordings where microsaccades are present. Our preliminary findings show that there is a correlation between the two.

Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG

PubMed Central

Bleichner, Martin G.; Debener, Stefan

2017-01-01

Electroencephalography (EEG) is an important clinical tool and frequently used to study the brain-behavior relationship in humans noninvasively. Traditionally, EEG signals are recorded by positioning electrodes on the scalp and keeping them in place with glue, rubber bands, or elastic caps. This setup provides good coverage of the head, but is impractical for EEG acquisition in natural daily-life situations. Here, we propose the transparent EEG concept. Transparent EEG aims for motion tolerant, highly portable, unobtrusive, and near invisible data acquisition with minimum disturbance of a user's daily activities. In recent years several ear-centered EEG solutions that are compatible with the transparent EEG concept have been presented. We discuss work showing that miniature electrodes placed in and around the human ear are a feasible solution, as they are sensitive enough to pick up electrical signals stemming from various brain and non-brain sources. We also describe the cEEGrid flex-printed sensor array, which enables unobtrusive multi-channel EEG acquisition from around the ear. In a number of validation studies we found that the cEEGrid enables the recording of meaningful continuous EEG, event-related potentials and neural oscillations. Here, we explain the rationale underlying the cEEGrid ear-EEG solution, present possible use cases and identify open issues that need to be solved on the way toward transparent EEG. PMID:28439233

3D source localization of interictal spikes in epilepsy patients with MRI lesions

NASA Astrophysics Data System (ADS)

Ding, Lei; Worrell, Gregory A.; Lagerlund, Terrence D.; He, Bin

2006-08-01

The present study aims to accurately localize epileptogenic regions which are responsible for epileptic activities in epilepsy patients by means of a new subspace source localization approach, i.e. first principle vectors (FINE), using scalp EEG recordings. Computer simulations were first performed to assess source localization accuracy of FINE in the clinical electrode set-up. The source localization results from FINE were compared with the results from a classic subspace source localization approach, i.e. MUSIC, and their differences were tested statistically using the paired t-test. Other factors influencing the source localization accuracy were assessed statistically by ANOVA. The interictal epileptiform spike data from three adult epilepsy patients with medically intractable partial epilepsy and well-defined symptomatic MRI lesions were then studied using both FINE and MUSIC. The comparison between the electrical sources estimated by the subspace source localization approaches and MRI lesions was made through the coregistration between the EEG recordings and MRI scans. The accuracy of estimations made by FINE and MUSIC was also evaluated and compared by R2 statistic, which was used to indicate the goodness-of-fit of the estimated sources to the scalp EEG recordings. The three-concentric-spheres head volume conductor model was built for each patient with three spheres of different radii which takes the individual head size and skull thickness into consideration. The results from computer simulations indicate that the improvement of source spatial resolvability and localization accuracy of FINE as compared with MUSIC is significant when simulated sources are closely spaced, deep, or signal-to-noise ratio is low in a clinical electrode set-up. The interictal electrical generators estimated by FINE and MUSIC are in concordance with the patients' structural abnormality, i.e. MRI lesions, in all three patients. The higher R2 values achieved by FINE than MUSIC indicate that FINE provides a more satisfactory fitting of the scalp potential measurements than MUSIC in all patients. The present results suggest that FINE provides a useful brain source imaging technique, from clinical EEG recordings, for identifying and localizing epileptogenic regions in epilepsy patients with focal partial seizures. The present study may lead to the establishment of a high-resolution source localization technique from scalp-recorded EEGs for aiding presurgical planning in epilepsy patients.

Effects of Transcranial Direct Current Stimulation, Transcranial Pulsed Current Stimulation, and Their Combination on Brain Oscillations in Patients with Chronic Visceral Pain: A Pilot Crossover Randomized Controlled Study.

PubMed

Thibaut, Aurore; Russo, Cristina; Hurtado-Puerto, Aura Maria; Morales-Quezada, Jorge Leon; Deitos, Alícia; Petrozza, John Christopher; Freedman, Steven; Fregni, Felipe

2017-01-01

Chronic visceral pain (CVP) syndromes are persistently painful disorders with a remarkable lack of effective treatment options. This study aimed at evaluating the effects of different neuromodulation techniques in patients with CVP on cortical activity, through electreocephalography (EEG) and on pain perception, through clinical tests. A pilot crossover randomized controlled study. Out-patient. Adults with CVP (>3 months). Participants received four interventions in a randomized order: (1) transcranial pulsed current stimulation (tPCS) and active transcranial direct current stimulation (tDCS) combined, (2) tPCS alone, (3) tDCS alone, and (4) sham condition. Resting state quantitative electroencephalography (qEEG) and pain assessments were performed before and after each intervention. Results were compared with a cohort of 47 healthy controls. We enrolled six patients with CVP for a total of 21 visits completed. Compared with healthy participants, patients with CVP showed altered cortical activity characterized by increased power in theta, alpha and beta bands, and a significant reduction in the alpha/beta ratio. Regarding tES, the combination of tDCS with tPCS had no effect on power in any of the bandwidths, nor brain regions. Comparing tPCS with tDCS alone, we found that tPCS induced higher increase in power within the theta and alpha bandwidths. This study confirms that patients with CVP present abnormal EEG-indexed cortical activity compared with healthy controls. Moreover, we showed that combining two types of neurostimulation techniques had no effect, whereas the two interventions, when applied individually, have different neural signatures.

[Features of seasonal reorganizations of the central mechanisms of regulation in children northerners with different level of social risk].

PubMed

Soroko, S I; Rozhkov, V P; Bekshaev, S S

2013-12-01

The paper presents a comparative analysis of frequency, spatial-temporal parameters and three-dimensional localization of EEG sources that characterize changes of cortical-subcortical interactions processes in autumn and spring periods at northern schoolchildren living in satisfactory and disadvantaged (risk group) conditions of the social (family) environment. Seasonal rearrangement of interaction between wave components of main EEG rhythms was revealed. School students present regressive changes in the EEG pattern temporal organization in spring compared to autumn, and this effect was more expressed at adolescents from group of risk. Data EEDS-tomography showed increased activity in the prefrontal, cingular and subcallosal areas of the cortex in the autumn period that could be related to the mechanisms of season depression caused by the significant reduction of the day length in the North. The increased activity of the limbic system structures which is persisted in the spring in adolescents from risk group narrows the range of regulation of adaptive reactions. Unfavorable conditions of the family environment are an additional stress factor to increased load on the regulatory mechanisms that have a negative impact on the emotional-motivation behavior of children and adolescents, thus increasing the risk of the school and of social disadaptation.

A highly detailed FEM volume conductor model based on the ICBM152 average head template for EEG source imaging and TCS targeting.

PubMed

Haufe, Stefan; Huang, Yu; Parra, Lucas C

2015-08-01

In electroencephalographic (EEG) source imaging as well as in transcranial current stimulation (TCS), it is common to model the head using either three-shell boundary element (BEM) or more accurate finite element (FEM) volume conductor models. Since building FEMs is computationally demanding and labor intensive, they are often extensively reused as templates even for subjects with mismatching anatomies. BEMs can in principle be used to efficiently build individual volume conductor models; however, the limiting factor for such individualization are the high acquisition costs of structural magnetic resonance images. Here, we build a highly detailed (0.5mm(3) resolution, 6 tissue type segmentation, 231 electrodes) FEM based on the ICBM152 template, a nonlinear average of 152 adult human heads, which we call ICBM-NY. We show that, through more realistic electrical modeling, our model is similarly accurate as individual BEMs. Moreover, through using an unbiased population average, our model is also more accurate than FEMs built from mismatching individual anatomies. Our model is made available in Matlab format.

Self-guided Positive Imagery Training: Effects beyond the Emotions-A Loreta Study.

PubMed

Velikova, Svetla; Nordtug, Bente

2017-01-01

Previously we demonstrated that a 12-week lasting self-guided positive imagery training had a positive effect on the psycho-emotional state of healthy subjects and was associated with an increase in functional connectivity in the brain. Here we repeated the previous project, but expanded the study, testing the hypothesis that training can also affect cognitive functions. Twenty subjects (half of them with subthreshold depression according CES-D) participated in the program of positive imagery training for 12 weeks. The schedule began with group training for 2 days, followed by training at home. Evaluations of cognitive functions and electroencephalographic (EEG) activity were conducted during three examinations as follows: E 0 -baseline (1 month before the training); E 1 -pre-training and E 2 -post-training. CNS Vital Signs battery was used to test the following cognitive domains: verbal and visual memory, executive functions, cognitive flexibility, social acuity, non-verbal reasoning. EEGs (19-channel) were recorded at rest with closed eyes and analyzed with Low-resolution electromagnetic tomography software. One-way repeated measures ANOVA, followed by pairwise comparison showed a significant increase after training (E 2 vs. E 1 ; E 2 vs. E 0 ) in the number of correct hits for positive emotions received during perception of emotions test (POET); after the sample was split according to the initial presence of depressive symptoms, the effect was present only in the subgroup with subthreshold depressive symptomatology. Post-training (E 2 vs. E 1 ; E 2 vs. E 0 ) the number of correct answers on non-verbal reasoning test increased; this effect was observed only in the subgroup that does have any depressive symptoms. Comparison of EEG post-training vs. pre-training demonstrated a significant reduction in current source density (CSD) after the training in the left hemisphere (insular cortex, frontal and temporal lobes in delta, theta and alpha1 bands). The observed changes were presented only in the subgroup with initial subthreshold depressive symptomatology. A negative correlation was found between POET and CSD in the left insular cortex for theta band. No significant differences were observed when data from EEG and cognitive tests obtained during pre-training were compared with baseline values. Potential use of training for the rehabilitation of various disturbances with cognitive and emotional deficits is discussed.

Self-guided Positive Imagery Training: Effects beyond the Emotions–A Loreta Study

PubMed Central

Velikova, Svetla; Nordtug, Bente

2018-01-01

Previously we demonstrated that a 12-week lasting self-guided positive imagery training had a positive effect on the psycho-emotional state of healthy subjects and was associated with an increase in functional connectivity in the brain. Here we repeated the previous project, but expanded the study, testing the hypothesis that training can also affect cognitive functions. Twenty subjects (half of them with subthreshold depression according CES-D) participated in the program of positive imagery training for 12 weeks. The schedule began with group training for 2 days, followed by training at home. Evaluations of cognitive functions and electroencephalographic (EEG) activity were conducted during three examinations as follows: E0-baseline (1 month before the training); E1-pre-training and E2-post-training. CNS Vital Signs battery was used to test the following cognitive domains: verbal and visual memory, executive functions, cognitive flexibility, social acuity, non-verbal reasoning. EEGs (19-channel) were recorded at rest with closed eyes and analyzed with Low-resolution electromagnetic tomography software. One-way repeated measures ANOVA, followed by pairwise comparison showed a significant increase after training (E2 vs. E1; E2 vs. E0) in the number of correct hits for positive emotions received during perception of emotions test (POET); after the sample was split according to the initial presence of depressive symptoms, the effect was present only in the subgroup with subthreshold depressive symptomatology. Post-training (E2 vs. E1; E2 vs. E0) the number of correct answers on non-verbal reasoning test increased; this effect was observed only in the subgroup that does have any depressive symptoms. Comparison of EEG post-training vs. pre-training demonstrated a significant reduction in current source density (CSD) after the training in the left hemisphere (insular cortex, frontal and temporal lobes in delta, theta and alpha1 bands). The observed changes were presented only in the subgroup with initial subthreshold depressive symptomatology. A negative correlation was found between POET and CSD in the left insular cortex for theta band. No significant differences were observed when data from EEG and cognitive tests obtained during pre-training were compared with baseline values. Potential use of training for the rehabilitation of various disturbances with cognitive and emotional deficits is discussed. PMID:29375344

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks

PubMed Central

Courellis, Hristos; Mullen, Tim; Poizner, Howard; Cauwenberghs, Gert; Iversen, John R.

2017-01-01

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a “reach/saccade to spatial target” cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI. PMID:28566997

Multimodal Neuroelectric Interface Development

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Wheeler, Kevin R.; Jorgensen, Charles C.; Totah, Joseph (Technical Monitor)

2001-01-01

This project aims to improve performance of NASA missions by developing multimodal neuroelectric technologies for augmented human-system interaction. Neuroelectric technologies will add completely new modes of interaction that operate in parallel with keyboards, speech, or other manual controls, thereby increasing the bandwidth of human-system interaction. We recently demonstrated the feasibility of real-time electromyographic (EMG) pattern recognition for a direct neuroelectric human-computer interface. We recorded EMG signals from an elastic sleeve with dry electrodes, while a human subject performed a range of discrete gestures. A machine-teaming algorithm was trained to recognize the EMG patterns associated with the gestures and map them to control signals. Successful applications now include piloting two Class 4 aircraft simulations (F-15 and 757) and entering data with a "virtual" numeric keyboard. Current research focuses on on-line adaptation of EMG sensing and processing and recognition of continuous gestures. We are also extending this on-line pattern recognition methodology to electroencephalographic (EEG) signals. This will allow us to bypass muscle activity and draw control signals directly from the human brain. Our system can reliably detect P-rhythm (a periodic EEG signal from motor cortex in the 10 Hz range) with a lightweight headset containing saline-soaked sponge electrodes. The data show that EEG p-rhythm can be modulated by real and imaginary motions. Current research focuses on using biofeedback to train of human subjects to modulate EEG rhythms on demand, and to examine interactions of EEG-based control with EMG-based and manual control. Viewgraphs on these neuroelectric technologies are also included.

Opportunities and methodological challenges in EEG and MEG resting state functional brain network research.

PubMed

van Diessen, E; Numan, T; van Dellen, E; van der Kooi, A W; Boersma, M; Hofman, D; van Lutterveld, R; van Dijk, B W; van Straaten, E C W; Hillebrand, A; Stam, C J

2015-08-01

Electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings during resting state are increasingly used to study functional connectivity and network topology. Moreover, the number of different analysis approaches is expanding along with the rising interest in this research area. The comparison between studies can therefore be challenging and discussion is needed to underscore methodological opportunities and pitfalls in functional connectivity and network studies. In this overview we discuss methodological considerations throughout the analysis pipeline of recording and analyzing resting state EEG and MEG data, with a focus on functional connectivity and network analysis. We summarize current common practices with their advantages and disadvantages; provide practical tips, and suggestions for future research. Finally, we discuss how methodological choices in resting state research can affect the construction of functional networks. When taking advantage of current best practices and avoid the most obvious pitfalls, functional connectivity and network studies can be improved and enable a more accurate interpretation and comparison between studies. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Coherence and phase locking in the scalp EEG and between LORETA model sources, and microstates as putative mechanisms of brain temporo-spatial functional organization.

PubMed

Lehmann, Dietrich; Faber, Pascal L; Gianotti, Lorena R R; Kochi, Kieko; Pascual-Marqui, Roberto D

2006-01-01

Brain electric mechanisms of temporary, functional binding between brain regions are studied using computation of scalp EEG coherence and phase locking, sensitive to time differences of few milliseconds. However, such results if computed from scalp data are ambiguous since electric sources are spatially oriented. Non-ambiguous results can be obtained using calculated time series of strength of intracerebral model sources. This is illustrated applying LORETA modeling to EEG during resting and meditation. During meditation, time series of LORETA model sources revealed a tendency to decreased left-right intracerebral coherence in the delta band, and to increased anterior-posterior intracerebral coherence in the theta band. An alternate conceptualization of functional binding is based on the observation that brain electric activity is discontinuous, i.e., that it occurs in chunks of up to about 100 ms duration that are detectable as quasi-stable scalp field configurations of brain electric activity, called microstates. Their functional significance is illustrated in spontaneous and event-related paradigms, where microstates associated with imagery- versus abstract-type mentation, or while reading positive versus negative emotion words showed clearly different regions of cortical activation in LORETA tomography. These data support the concept that complete brain functions of higher order such as a momentary thought might be incorporated in temporal chunks of processing in the range of tens to about 100 ms as quasi-stable brain states; during these time windows, subprocesses would be accepted as members of the ongoing chunk of processing.

Neurophysiological correlates of persistent psycho-affective alterations in athletes with a history of concussion.

PubMed

Moore, Robert Davis; Sauve, William; Ellemberg, Dave

2016-12-01

Understanding the neuropathological underpinnings of sport-related concussion are critical for diagnosis, prognosis, and remediation. Although electro-encephalographic (EEG) methods have proven invaluable for understanding psycho-affective pathologies in various clinical conditions, they have not been used to understand the psycho-affective outcomes of concussive injuries. Accordingly, we evaluated the relation of electroencephalographic (EEG) power in collegiate athletes to psycho-affective measures. We predicted that athletes with a history of concussion would exhibit alterations in frontal EEG asymmetries indicative of increased depression, anxiety and more general mood disturbance. During this cross-sectional study, resting EEG and measures of mood and affect, including the Beck Depression Inventory-II (BDI-II) and Profile of Mood States (POMS) were collected in 81 young-adult male athletes (52 concussion history; 29 controls). All athletes with a history of concussion (9+ months from injury) reported to be symptom free, and all participants were actively taking part in their sport at the time of testing. Compared to control athletes, the athletes with a history of concussion exhibited alterations in frontal-alpha and frontal-beta asymmetry (p's < .05). Correlational analyses revealed that alterations in frontal-alpha asymmetry were related to self-reported depression and anxiety, and alterations in beta-asymmetry were related to self-reported anger/aggression, but these relations were only significant for athletes with a history of concussion. The current study suggests that athletes with a history of concussion who made a complete return to play and reported to be asymptomatic on a commonly used symptom checklist may still exhibit neural activity associated with increased levels of depression, anxiety and anger/hostility. The current results reinforce the clinical necessity for long-term evaluations of athletes irrespective of apparent symptom resolution, and suggest that EEG may serve as a sensitive tool to identify and track concussion-related alterations in psycho-affective health before they manifest as clinical disorders.

Hemodynamic Response to Interictal Epileptiform Discharges Addressed by Personalized EEG-fNIRS Recordings

PubMed Central

Pellegrino, Giovanni; Machado, Alexis; von Ellenrieder, Nicolas; Watanabe, Satsuki; Hall, Jeffery A.; Lina, Jean-Marc; Kobayashi, Eliane; Grova, Christophe

2016-01-01

Objective: We aimed at studying the hemodynamic response (HR) to Interictal Epileptic Discharges (IEDs) using patient-specific and prolonged simultaneous ElectroEncephaloGraphy (EEG) and functional Near InfraRed Spectroscopy (fNIRS) recordings. Methods: The epileptic generator was localized using Magnetoencephalography source imaging. fNIRS montage was tailored for each patient, using an algorithm to optimize the sensitivity to the epileptic generator. Optodes were glued using collodion to achieve prolonged acquisition with high quality signal. fNIRS data analysis was handled with no a priori constraint on HR time course, averaging fNIRS signals to similar IEDs. Cluster-permutation analysis was performed on 3D reconstructed fNIRS data to identify significant spatio-temporal HR clusters. Standard (GLM with fixed HRF) and cluster-permutation EEG-fMRI analyses were performed for comparison purposes. Results: fNIRS detected HR to IEDs for 8/9 patients. It mainly consisted oxy-hemoglobin increases (seven patients), followed by oxy-hemoglobin decreases (six patients). HR was lateralized in six patients and lasted from 8.5 to 30 s. Standard EEG-fMRI analysis detected an HR in 4/9 patients (4/9 without enough IEDs, 1/9 unreliable result). The cluster-permutation EEG-fMRI analysis restricted to the region investigated by fNIRS showed additional strong and non-canonical BOLD responses starting earlier than the IEDs and lasting up to 30 s. Conclusions: (i) EEG-fNIRS is suitable to detect the HR to IEDs and can outperform EEG-fMRI because of prolonged recordings and greater chance to detect IEDs; (ii) cluster-permutation analysis unveils additional HR features underestimated when imposing a canonical HR function (iii) the HR is often bilateral and lasts up to 30 s. PMID:27047325

Adenosine deaminase polymorphism affects sleep EEG spectral power in a large epidemiological sample.

PubMed

Mazzotti, Diego Robles; Guindalini, Camila; de Souza, Altay Alves Lino; Sato, João Ricardo; Santos-Silva, Rogério; Bittencourt, Lia Rita Azeredo; Tufik, Sergio

2012-01-01

Slow wave oscillations in the electroencephalogram (EEG) during sleep may reflect both sleep need and intensity, which are implied in homeostatic regulation. Adenosine is strongly implicated in sleep homeostasis, and a single nucleotide polymorphism in the adenosine deaminase gene (ADA G22A) has been associated with deeper and more efficient sleep. The present study verified the association between the ADA G22A polymorphism and changes in sleep EEG spectral power (from C3-A2, C4-A1, O1-A2, and O2-A1 derivations) in the Epidemiologic Sleep Study (EPISONO) sample from São Paulo, Brazil. Eight-hundred individuals were subjected to full-night polysomnography and ADA G22A genotyping. Spectral analysis of the EEG was carried out in all individuals using fast Fourier transformation of the signals from each EEG electrode. The genotype groups were compared in the whole sample and in a subsample of 120 individuals matched according to ADA genotype for age, gender, body mass index, caffeine intake status, presence of sleep disturbance, and sleep-disturbing medication. When compared with homozygous GG genotype carriers, A allele carriers showed higher delta spectral power in Stage 1 and Stages 3+4 of sleep, and increased theta spectral power in Stages 1, 2 and REM sleep. These changes were seen both in the whole sample and in the matched subset. The higher EEG spectral power indicates that the sleep of individuals carrying the A allele may be more intense. Therefore, this polymorphism may be an important source of variation in sleep homeostasis in humans, through modulation of specific components of the sleep EEG.

The Application of Electro- and Magneto-Encephalography in Tinnitus Research – Methods and Interpretations

PubMed Central

Adjamian, Peyman

2014-01-01

In recent years, there has been a significant increase in the use of electroencephalography (EEG) and magnetoencephalography (MEG) to investigate changes in oscillatory brain activity associated with tinnitus with many conflicting results. Current view of the underlying mechanism of tinnitus is that it results from changes in brain activity in various structures of the brain as a consequence of sensory deprivation. This in turn gives rise to increased spontaneous activity and/or synchrony in the auditory centers but also involves modulation from non-auditory processes from structures of the limbic and paralimbic system. Some of the neural changes associated with tinnitus may be assessed non-invasively in human beings with MEG and EEG (M/EEG) in ways, which are superior to animal studies and other non-invasive imaging techniques. However, both MEG and EEG have their limitations and research results can be misinterpreted without appropriate consideration of these limitations. In this article, I intend to provide a brief review of these techniques, describe what the recorded signals reflect in terms of the underlying neural activity, and their strengths and limitations. I also discuss some pertinent methodological issues involved in tinnitus-related studies and conclude with suggestions to minimize possible discrepancies between results. The overall message is that while MEG and EEG are extremely useful techniques, the interpretation of results from tinnitus studies requires much caution given the individual variability in oscillatory activity and the limits of these techniques. PMID:25431567

Invisible Base Electrode Coordinates Approximation for Simultaneous SPECT and EEG Data Visualization

NASA Astrophysics Data System (ADS)

Kowalczyk, L.; Goszczynska, H.; Zalewska, E.; Bajera, A.; Krolicki, L.

2014-04-01

This work was performed as part of a larger research concerning the feasibility of improving the localization of epileptic foci, as compared to the standard SPECT examination, by applying the technique of EEG mapping. The presented study extends our previous work on the development of a method for superposition of SPECT images and EEG 3D maps when these two examinations are performed simultaneously. Due to the lack of anatomical data in SPECT images it is a much more difficult task than in the case of MRI/EEG study where electrodes are visible in morphological images. Using the appropriate dose of radioisotope we mark five base electrodes to make them visible in the SPECT image and then approximate the coordinates of the remaining electrodes using properties of the 10-20 electrode placement system and the proposed nine-ellipses model. This allows computing a sequence of 3D EEG maps spanning on all electrodes. It happens, however, that not all five base electrodes can be reliably identified in SPECT data. The aim of the current study was to develop a method for determining the coordinates of base electrode(s) missing in the SPECT image. The algorithm for coordinates approximation has been developed and was tested on data collected for three subjects with all visible electrodes. To increase the accuracy of the approximation we used head surface models. Freely available model from Oostenveld research based on data from SPM package and our own model based on data from our EEG/SPECT studies were used. For data collected in four cases with one electrode not visible we compared the invisible base electrode coordinates approximation for Oostenveld and our models. The results vary depending on the missing electrode placement, but application of the realistic head model significantly increases the accuracy of the approximation.

Quantitative EEG After Brain Stimulation and Cognitive Training in Alzheimer Disease.

PubMed

Gandelman-Marton, Revital; Aichenbaum, Sergio; Dobronevsky, Evgenya; Khaigrekht, Michael; Rabey, Jose M

2017-01-01

Medications are the currently accepted symptomatic treatment of Alzheimer disease (AD), but their impact on delaying the progression of cognitive deficits and functional impairment is limited. The authors aimed to explore long-term electrophysiological effects of repetitive transcranial magnetic stimulation interlaced with cognitive training on quantitative electroencephalography (EEG) in patients with AD. Quantitative EEG was assessed on non-repetitive transcranial magnetic stimulation interlaced with cognitive training treatment days before treatment and after each treatment phase in seven patients with mild AD. After 4.5 months (54 sessions) of treatment, a significant increase of delta activity over the temporal region was found compared with pretreatment values. Nonsignificant increases of the log EEG power were found for alpha band over the frontal and temporal regions, beta band over the frontal region, theta band over the frontal, temporal, and parieto-occipital regions, and delta band over the frontal and parieto-occipital regions. Nonsignificant decreases were found for alpha over the parieto-occipital region, and for beta over the temporal and parieto-occipital regions. A positive correlation was found between log alpha power over the frontal and temporal regions at 6 weeks and Mini-Mental State Examination (MMSE) scores at 6 weeks and 4.5 months, and between log alpha power over the parieto-occipital regions and MMSE scores at 6 weeks. A negative correlation was found between log alpha power over the frontal and temporal regions at 6 weeks and baseline Alzheimer's Disease Assessment Scale-cognitive subscale scores. Repetitive transcranial magnetic stimulation interlaced with cognitive training has long-term effects on quantitative EEG in patients with mild AD. Further research on the quantitative EEG long-term effects of transcranial magnetic stimulation interlaced with cognitive training is required to confirm the authors' data.

Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

PubMed

Cichy, Radoslaw Martin; Pantazis, Dimitrios

2017-09-01

Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.

Spatiotemporal source tuning filter bank for multiclass EEG based brain computer interfaces.

PubMed

Acharya, Soumyadipta; Mollazadeh, Moshen; Murari, Kartikeya; Thakor, Nitish

2006-01-01

Non invasive brain-computer interfaces (BCI) allow people to communicate by modulating features of their electroencephalogram (EEG). Spatiotemporal filtering has a vital role in multi-class, EEG based BCI. In this study, we used a novel combination of principle component analysis, independent component analysis and dipole source localization to design a spatiotemporal multiple source tuning (SPAMSORT) filter bank, each channel of which was tuned to the activity of an underlying dipole source. Changes in the event-related spectral perturbation (ERSP) were measured and used to train a linear support vector machine to classify between four classes of motor imagery tasks (left hand, right hand, foot and tongue) for one subject. ERSP values were significantly (p<0.01) different across tasks and better (p<0.01) than conventional spatial filtering methods (large Laplacian and common average reference). Classification resulted in an average accuracy of 82.5%. This approach could lead to promising BCI applications such as control of a prosthesis with multiple degrees of freedom.

An alternative subspace approach to EEG dipole source localization

NASA Astrophysics Data System (ADS)

Xu, Xiao-Liang; Xu, Bobby; He, Bin

2004-01-01

In the present study, we investigate a new approach to electroencephalography (EEG) three-dimensional (3D) dipole source localization by using a non-recursive subspace algorithm called FINES. In estimating source dipole locations, the present approach employs projections onto a subspace spanned by a small set of particular vectors (FINES vector set) in the estimated noise-only subspace instead of the entire estimated noise-only subspace in the case of classic MUSIC. The subspace spanned by this vector set is, in the sense of principal angle, closest to the subspace spanned by the array manifold associated with a particular brain region. By incorporating knowledge of the array manifold in identifying FINES vector sets in the estimated noise-only subspace for different brain regions, the present approach is able to estimate sources with enhanced accuracy and spatial resolution, thus enhancing the capability of resolving closely spaced sources and reducing estimation errors. The present computer simulations show, in EEG 3D dipole source localization, that compared to classic MUSIC, FINES has (1) better resolvability of two closely spaced dipolar sources and (2) better estimation accuracy of source locations. In comparison with RAP-MUSIC, FINES' performance is also better for the cases studied when the noise level is high and/or correlations among dipole sources exist.

Use of EEG to Diagnose ADHD

PubMed Central

Lenartowicz, Agatha; Loo, Sandra K.

2015-01-01

Electroencephalography (EEG) has, historically, played a focal role in the assessment of neural function in children with attention deficit hyperactivity disorder (ADHD). We review here the most recent developments in the utility of EEG in the diagnosis of ADHD, with emphasis on the most commonly used and emerging EEG metrics and their reliability in diagnostic classification. Considering the clinical heterogeneity of ADHD and the complexity of information available from the EEG signals, we suggest that considerable benefits are to be gained from multivariate analyses and a focus towards understanding of the neural generators of EEG. We conclude that while EEG cannot currently be used as a diagnostic tool, vast developments in analytical and technological tools in its domain anticipate future progress in its utility in the clinical setting. PMID:25234074

Multicompare tests of the performance of different metaheuristics in EEG dipole source localization.

PubMed

Escalona-Vargas, Diana Irazú; Lopez-Arevalo, Ivan; Gutiérrez, David

2014-01-01

We study the use of nonparametric multicompare statistical tests on the performance of simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), when used for electroencephalographic (EEG) source localization. Such task can be posed as an optimization problem for which the referred metaheuristic methods are well suited. Hence, we evaluate the localization's performance in terms of metaheuristics' operational parameters and for a fixed number of evaluations of the objective function. In this way, we are able to link the efficiency of the metaheuristics with a common measure of computational cost. Our results did not show significant differences in the metaheuristics' performance for the case of single source localization. In case of localizing two correlated sources, we found that PSO (ring and tree topologies) and DE performed the worst, then they should not be considered in large-scale EEG source localization problems. Overall, the multicompare tests allowed to demonstrate the little effect that the selection of a particular metaheuristic and the variations in their operational parameters have in this optimization problem.

Analysis of EEG Related Saccadic Eye Movement

NASA Astrophysics Data System (ADS)

Funase, Arao; Kuno, Yoshiaki; Okuma, Shigeru; Yagi, Tohru

Our final goal is to establish the model for saccadic eye movement that connects the saccade and the electroencephalogram(EEG). As the first step toward this goal, we recorded and analyzed the saccade-related EEG. In the study recorded in this paper, we tried detecting a certain EEG that is peculiar to the eye movement. In these experiments, each subject was instructed to point their eyes toward visual targets (LEDs) or the direction of the sound sources (buzzers). In the control cases, the EEG was recorded in the case of no eye movemens. As results, in the visual experiments, we found that the potential of EEG changed sharply on the occipital lobe just before eye movement. Furthermore, in the case of the auditory experiments, similar results were observed. In the case of the visual experiments and auditory experiments without eye movement, we could not observed the EEG changed sharply. Moreover, when the subject moved his/her eyes toward a right-side target, a change in EEG potential was found on the right occipital lobe. On the contrary, when the subject moved his/her eyes toward a left-side target, a sharp change in EEG potential was found on the left occipital lobe.

EEG signatures of arm isometric exertions in preparation, planning and execution.

PubMed

Nasseroleslami, Bahman; Lakany, Heba; Conway, Bernard A

2014-04-15

The electroencephalographic (EEG) activity patterns in humans during motor behaviour provide insight into normal motor control processes and for diagnostic and rehabilitation applications. While the patterns preceding brisk voluntary movements, and especially movement execution, are well described, there are few EEG studies that address the cortical activation patterns seen in isometric exertions and their planning. In this paper, we report on time and time-frequency EEG signatures in experiments in normal subjects (n=8), using multichannel EEG during motor preparation, planning and execution of directional centre-out arm isometric exertions performed at the wrist in the horizontal plane, in response to instruction-delay visual cues. Our observations suggest that isometric force exertions are accompanied by transient and sustained event-related potentials (ERP) and event-related (de-)synchronisations (ERD/ERS), comparable to those of a movement task. Furthermore, the ERPs and ERD/ERS are also observed during preparation and planning of the isometric task. Comparison of ear-lobe-referenced and surface Laplacian ERPs indicates the contribution of superficial sources in supplementary and pre-motor (FC(z)), parietal (CP(z)) and primary motor cortical areas (C₁ and FC₁) to ERPs (primarily negative peaks in frontal and positive peaks in parietal areas), but contribution of deep sources to sustained time-domain potentials (negativity in planning and positivity in execution). Transient and sustained ERD patterns in μ and β frequency bands of ear-lobe-referenced and surface Laplacian EEG indicate the contribution of both superficial and deep sources to ERD/ERS. As no physical displacement happens during the task, we can infer that the underlying mechanisms of motor-related ERPs and ERD/ERS patterns do not only depend on change in limb coordinate or muscle-length-dependent ascending sensory information and are primary generated by motor preparation, direction-dependent planning and execution of isometric motor tasks. The results contribute to our understanding of the functions of different brain regions during voluntary motor tasks and their activity signatures in EEG can shed light on the relationships between large-scale recordings such as EEG and other recordings such as single unit activity and fMRI in this context. Copyright © 2013 Elsevier Inc. All rights reserved.

Electroencephalography Amplitude Modulation Analysis for Automated Affective Tagging of Music Video Clips

PubMed Central

Clerico, Andrea; Tiwari, Abhishek; Gupta, Rishabh; Jayaraman, Srinivasan; Falk, Tiago H.

2018-01-01

The quantity of music content is rapidly increasing and automated affective tagging of music video clips can enable the development of intelligent retrieval, music recommendation, automatic playlist generators, and music browsing interfaces tuned to the users' current desires, preferences, or affective states. To achieve this goal, the field of affective computing has emerged, in particular the development of so-called affective brain-computer interfaces, which measure the user's affective state directly from measured brain waves using non-invasive tools, such as electroencephalography (EEG). Typically, conventional features extracted from the EEG signal have been used, such as frequency subband powers and/or inter-hemispheric power asymmetry indices. More recently, the coupling between EEG and peripheral physiological signals, such as the galvanic skin response (GSR), have also been proposed. Here, we show the importance of EEG amplitude modulations and propose several new features that measure the amplitude-amplitude cross-frequency coupling per EEG electrode, as well as linear and non-linear connections between multiple electrode pairs. When tested on a publicly available dataset of music video clips tagged with subjective affective ratings, support vector classifiers trained on the proposed features were shown to outperform those trained on conventional benchmark EEG features by as much as 6, 20, 8, and 7% for arousal, valence, dominance and liking, respectively. Moreover, fusion of the proposed features with EEG-GSR coupling features showed to be particularly useful for arousal (feature-level fusion) and liking (decision-level fusion) prediction. Together, these findings show the importance of the proposed features to characterize human affective states during music clip watching. PMID:29367844

Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM.

PubMed

López, J D; Litvak, V; Espinosa, J J; Friston, K; Barnes, G R

2014-01-01

The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy-an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm. © 2013. Published by Elsevier Inc. All rights reserved.

[EEG-markers of vertical postural organization in healthy persons].

PubMed

Zhavoronkova, L A; Zharikova, A V; Kushnir, E M; Mikhalkova, A A

2012-01-01

In 10 healthy persons (22.8 +/- 0.67 years) spectral-coherence parameters of EEG were analyzed in different steps of verticalizations--from gorizontal position to seat and stand one. Maximal changes of all EEG parameters were observed in state with absence of visual control. We observed an increase of power for fast spectral bands of EEG (beta- and gamma-bands) in all conditions and additional increase of these EEG parameters was observed at situation of complication of conditions of vertical pose supporting. Results of EEG coherent analysis in conditions of human verticalization showed specific increase of coherence for the majority of rhythm ranges in the right hemisphere especially in the central-frontal and in occipital-parietal areas and for interhemispheric pairs for these leads. This fact can reflect participation of cortical as well as subcortical structures in these processes. In conditions of complicate conditions of vertical pose supporting the additional increase of EEG coherence in fast bands (beta-rhythm) was observed at the frontal areas. This fact can testify about increasing of executive functions in this conditions.

Mental Task Evaluation for Hybrid NIRS-EEG Brain-Computer Interfaces

PubMed Central

Gupta, Rishabh; Falk, Tiago H.

2017-01-01

Based on recent electroencephalography (EEG) and near-infrared spectroscopy (NIRS) studies that showed that tasks such as motor imagery and mental arithmetic induce specific neural response patterns, we propose a hybrid brain-computer interface (hBCI) paradigm in which EEG and NIRS data are fused to improve binary classification performance. We recorded simultaneous NIRS-EEG data from nine participants performing seven mental tasks (word generation, mental rotation, subtraction, singing and navigation, and motor and face imagery). Classifiers were trained for each possible pair of tasks using (1) EEG features alone, (2) NIRS features alone, and (3) EEG and NIRS features combined, to identify the best task pairs and assess the usefulness of a multimodal approach. The NIRS-EEG approach led to an average increase in peak kappa of 0.03 when using features extracted from one-second windows (equivalent to an increase of 1.5% in classification accuracy for balanced classes). The increase was much stronger (0.20, corresponding to an 10% accuracy increase) when focusing on time windows of high NIRS performance. The EEG and NIRS analyses further unveiled relevant brain regions and important feature types. This work provides a basis for future NIRS-EEG hBCI studies aiming to improve classification performance toward more efficient and flexible BCIs. PMID:29181021

Effect of EEG electrode density on dipole localization accuracy using two realistically shaped skull resistivity models.

PubMed

Laarne, P H; Tenhunen-Eskelinen, M L; Hyttinen, J K; Eskola, H J

2000-01-01

The effect of number of EEG electrodes on the dipole localization was studied by comparing the results obtained using the 10-20 and 10-10 electrode systems. Two anatomically detailed models with resistivity values of 177.6 omega m and 67.0 omega m for the skull were applied. Simulated potential values generated by current dipoles were applied to different combinations of the volume conductors and electrode systems. High and low resistivity models differed slightly in favour of the lower skull resistivity model when dipole localization was based on noiseless data. The localization errors were approximately three times larger using low resistivity model for generating the potentials, but applying high resistivity model for the inverse solution. The difference between the two electrode systems was minor in favour of the 10-10 electrode system when simulated, noiseless potentials were used. In the presence of noise the dipole localization algorithm operated more accurately using the denser electrode system. In conclusion, increasing the number of recording electrodes seems to improve the localization accuracy in the presence of noise. The absolute skull resistivity value also affects the accuracy, but using an incorrect value in modelling calculations seems to be the most serious source of error.

Graph theory findings in the pathophysiology of temporal lobe epilepsy.

PubMed

Chiang, Sharon; Haneef, Zulfi

2014-07-01

Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy. Accumulating evidence has shown that TLE is a disorder of abnormal epileptogenic networks, rather than focal sources. Graph theory allows for a network-based representation of TLE brain networks, and has potential to illuminate characteristics of brain topology conducive to TLE pathophysiology, including seizure initiation and spread. We review basic concepts which we believe will prove helpful in interpreting results rapidly emerging from graph theory research in TLE. In addition, we summarize the current state of graph theory findings in TLE as they pertain its pathophysiology. Several common findings have emerged from the many modalities which have been used to study TLE using graph theory, including structural MRI, diffusion tensor imaging, surface EEG, intracranial EEG, magnetoencephalography, functional MRI, cell cultures, simulated models, and mouse models, involving increased regularity of the interictal network configuration, altered local segregation and global integration of the TLE network, and network reorganization of temporal lobe and limbic structures. As different modalities provide different views of the same phenomenon, future studies integrating data from multiple modalities are needed to clarify findings and contribute to the formation of a coherent theory on the pathophysiology of TLE. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Estimation of effective brain connectivity with dual Kalman filter and EEG source localization methods.

PubMed

Rajabioun, Mehdi; Nasrabadi, Ali Motie; Shamsollahi, Mohammad Bagher

2017-09-01

Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions.

The effect of methylphenidate on very low frequency electroencephalography oscillations in adult ADHD.

PubMed

Cooper, Ruth E; Skirrow, Caroline; Tye, Charlotte; McLoughlin, Grainne; Rijsdijk, Fruhling; Banaschweski, Tobias; Brandeis, Daniel; Kuntsi, Jonna; Asherson, Philip

2014-04-01

Altered very low-frequency electroencephalographic (VLF-EEG) activity is an endophenotype of ADHD in children and adolescents. We investigated VLF-EEG case-control differences in adult samples and the effects of methylphenidate (MPH). A longitudinal case-control study was conducted examining the effects of MPH on VLF-EEG (.02-0.2Hz) during a cued continuous performance task. 41 untreated adults with ADHD and 47 controls were assessed, and 21 cases followed up after MPH treatment, with a similar follow-up for 38 controls (mean follow-up=9.4months). Cases had enhanced frontal and parietal VLF-EEG and increased omission errors. In the whole sample, increased parietal VLF-EEG correlated with increased omission errors. After controlling for subthreshold comorbid symptoms, VLF-EEG case-control differences and treatment effects remained. Post-treatment, a time by group interaction emerged; VLF-EEG and omission errors reduced to the same level as controls, with decreased inattentive symptoms in cases. Reduced VLF-EEG following MPH treatment provides preliminary evidence that changes in VLF-EEG may relate to MPH treatment effects on ADHD symptoms; and that VLF-EEG may be an intermediate phenotype of ADHD. Further studies of the treatment effect of MPH in larger controlled studies are required to formally evaluate any causal link between MPH, VLF-EEG and ADHD symptoms. Copyright © 2014 Elsevier Inc. All rights reserved.

Analysis of bioelectric records and fabrication of phototype sleep analysis equipment

NASA Technical Reports Server (NTRS)

Kellaway, P.

1972-01-01

A computer-analysis technique was used to evaluate the changes in the waking EEGs of 5 normal subjects which occurred during the oral administration of flurazepam hydrochloride (Dalmane). While the subjects were receiving the drug, there was an increase in the amount of beta (14-38 c/sec) activity in fronto-central EEG leads in all 5 subjects. This increase in beta activity was characterized by a highly consistent increase in the number of waves that occurred during an EEG recording interval of fixed duration and by a less consistent increase in average wave amplitude. There was no detectable change in mean EEG wavelength (frequency) within the beta frequency range. The EEG patterns reverted to their baseline condition during 2-3 weeks after withdrawal of the drug. Analysis of the alpha, theta and delta components of the EEG indicated no changes during or following administration of the drug. This study clearly illustrates the usefulness of specific computer-analysis techniques in the characterization and quantification of sleep-promoting drugs upon the EEG of the normal young adults in the waking state. Two preamplifiers and 150 EEG monitoring caps with electrodes were delivered to MSC.

Combined MEG-EEG source localisation in patients with sub-acute sclerosing pan-encephalitis.

PubMed

Velmurugan, J; Sinha, Sanjib; Nagappa, Madhu; Mariyappa, N; Bindu, P S; Ravi, G S; Hazra, Nandita; Thennarasu, K; Ravi, V; Taly, A B; Satishchandra, P

2016-08-01

To study the genesis and propagation patterns of periodic complexes (PCs) associated with myoclonic jerks in sub-acute sclerosing pan-encephalitis (SSPE) using magnetoencephalography (MEG) and electroencephalography (EEG). Simultaneous recording of MEG (306 channels) and EEG (64 channels) in five patients of SSPE (M:F = 3:2; age 10.8 ± 3.2 years; symptom-duration 6.2 ± 10 months) was carried out using Elekta Neuromag(®) TRIUX™ system. Qualitative analysis of 80-160 PCs per patient was performed. Ten isomorphic classical PCs with significant field topography per patient were analysed at the 'onset' and at 'earliest significant peak' of the burst using discrete and distributed source imaging methods. MEG background was asymmetrical in 2 and slow in 3 patients. Complexes were periodic (3) or quasi-periodic (2), occurring every 4-16 s and varied in morphology among patients. Mean source localization at onset of bursts using discrete and distributed source imaging in magnetic source imaging (MSI) was in thalami and or insula (50 and 50 %, respectively) and in electric source imaging (ESI) was also in thalami and or insula (38 and 46 %, respectively). Mean source localization at the earliest rising phase of peak in MSI was in peri-central gyrus (49 and 42 %) and in ESI it was in frontal cortex (52 and 56 %). Further analysis revealed that PCs were generated in thalami and or insula and thereafter propagated to anterolateral surface of the cortices (viz. sensori-motor cortex and frontal cortex) to same side as that of the onset. This novel MEG-EEG based case series of PCs provides newer insights for understanding the plausible generators of myoclonus in SSPE and patterns of their propagation.

Spatio-temporal Reconstruction of Neural Sources Using Indirect Dominant Mode Rejection.

PubMed

Jafadideh, Alireza Talesh; Asl, Babak Mohammadzadeh

2018-04-27

Adaptive minimum variance based beamformers (MVB) have been successfully applied to magnetoencephalogram (MEG) and electroencephalogram (EEG) data to localize brain activities. However, the performance of these beamformers falls down in situations where correlated or interference sources exist. To overcome this problem, we propose indirect dominant mode rejection (iDMR) beamformer application in brain source localization. This method by modifying measurement covariance matrix makes MVB applicable in source localization in the presence of correlated and interference sources. Numerical results on both EEG and MEG data demonstrate that presented approach accurately reconstructs time courses of active sources and localizes those sources with high spatial resolution. In addition, the results of real AEF data show the good performance of iDMR in empirical situations. Hence, iDMR can be reliably used for brain source localization especially when there are correlated and interference sources.

Multimodal Spatial Calibration for Accurately Registering EEG Sensor Positions

PubMed Central

Chen, Shengyong; Xiao, Gang; Li, Xiaoli

2014-01-01

This paper proposes a fast and accurate calibration method to calibrate multiple multimodal sensors using a novel photogrammetry system for fast localization of EEG sensors. The EEG sensors are placed on human head and multimodal sensors are installed around the head to simultaneously obtain all EEG sensor positions. A multiple views' calibration process is implemented to obtain the transformations of multiple views. We first develop an efficient local repair algorithm to improve the depth map, and then a special calibration body is designed. Based on them, accurate and robust calibration results can be achieved. We evaluate the proposed method by corners of a chessboard calibration plate. Experimental results demonstrate that the proposed method can achieve good performance, which can be further applied to EEG source localization applications on human brain. PMID:24803954

How Different EEG References Influence Sensor Level Functional Connectivity Graphs

PubMed Central

Huang, Yunzhi; Zhang, Junpeng; Cui, Yuan; Yang, Gang; He, Ling; Liu, Qi; Yin, Guangfu

2017-01-01

Highlights: Hamming Distance is applied to distinguish the difference of functional connectivity networkThe orientations of sources are testified to influence the scalp Functional Connectivity Graph (FCG) from different references significantlyREST, the reference electrode standardization technique, is proved to have an overall stable and excellent performance in variable situations. The choice of an electroencephalograph (EEG) reference is a practical issue for the study of brain functional connectivity. To study how EEG reference influence functional connectivity estimation (FCE), this study compares the differences of FCE resulting from the different references such as REST (the reference electrode standardization technique), average reference (AR), linked mastoids (LM), and left mastoid references (LR). Simulations involve two parts. One is based on 300 dipolar pairs, which are located on the superficial cortex with a radial source direction. The other part is based on 20 dipolar pairs. In each pair, the dipoles have various orientation combinations. The relative error (RE) and Hamming distance (HD) between functional connectivity matrices of ideal recordings and that of recordings obtained with different references, are metrics to compare the differences of the scalp functional connectivity graph (FCG) derived from those two kinds of recordings. Lower RE and HD values imply more similarity between the two FCGs. Using the ideal recording (IR) as a standard, the results show that AR, LM and LR perform well only in specific conditions, i.e., AR performs stable when there is no upward component in sources' orientation. LR achieves desirable results when the sources' locations are away from left ear. LM achieves an indistinct difference with IR, i.e., when the distribution of source locations is symmetric along the line linking the two ears. However, REST not only achieves excellent performance for superficial and radial dipolar sources, but also achieves a stable and robust performance with variable source locations and orientations. Benefitting from the stable and robust performance of REST vs. other reference methods, REST might best recover the real FCG of EEG. Thus, REST based FCG may be a good candidate to compare the FCG of EEG based on different references from different labs. PMID:28725175

Constructing Carbon Fiber Motion-Detection Loops for Simultaneous EEG–fMRI

PubMed Central

Abbott, David F.; Masterton, Richard A. J.; Archer, John S.; Fleming, Steven W.; Warren, Aaron E. L.; Jackson, Graeme D.

2015-01-01

One of the most significant impediments to high-quality EEG recorded in an MRI scanner is subject motion. Availability of motion artifact sensors can substantially improve the quality of the recorded EEG. In the study of epilepsy, it can also dramatically increase the confidence that one has in discriminating true epileptiform activity from artifact. This is due both to the reduction in artifact and the ability to visually inspect the motion sensor signals when reading the EEG, revealing whether or not head motion is present. We have previously described the use of carbon fiber loops for detecting and correcting artifact in EEG acquired simultaneously with MRI. The loops, attached to the subject’s head, are electrically insulated from the scalp. They provide a simple and direct measure of specific artifact that is contaminating the EEG, including both subject motion and residual artifact arising from magnetic field gradients applied during MRI. Our previous implementation was used together with a custom-built EEG–fMRI system that differs substantially from current commercially available EEG–fMRI systems. The present technical note extends this work, describing in more detail how to construct the carbon fiber motion-detection loops, and how to interface them with a commercially available simultaneous EEG–fMRI system. We hope that the information provided may help those wishing to utilize a motion-detection/correction solution to improve the quality of EEG recorded within an MRI scanner. PMID:25601852

Spherical Harmonics Reveal Standing EEG Waves and Long-Range Neural Synchronization during Non-REM Sleep.

PubMed

Sivakumar, Siddharth S; Namath, Amalia G; Galán, Roberto F

2016-01-01

Previous work from our lab has demonstrated how the connectivity of brain circuits constrains the repertoire of activity patterns that those circuits can display. Specifically, we have shown that the principal components of spontaneous neural activity are uniquely determined by the underlying circuit connections, and that although the principal components do not uniquely resolve the circuit structure, they do reveal important features about it. Expanding upon this framework on a larger scale of neural dynamics, we have analyzed EEG data recorded with the standard 10-20 electrode system from 41 neurologically normal children and adolescents during stage 2, non-REM sleep. We show that the principal components of EEG spindles, or sigma waves (10-16 Hz), reveal non-propagating, standing waves in the form of spherical harmonics. We mathematically demonstrate that standing EEG waves exist when the spatial covariance and the Laplacian operator on the head's surface commute. This in turn implies that the covariance between two EEG channels decreases as the inverse of their relative distance; a relationship that we corroborate with empirical data. Using volume conduction theory, we then demonstrate that superficial current sources are more synchronized at larger distances, and determine the characteristic length of large-scale neural synchronization as 1.31 times the head radius, on average. Moreover, consistent with the hypothesis that EEG spindles are driven by thalamo-cortical rather than cortico-cortical loops, we also show that 8 additional patients with hypoplasia or complete agenesis of the corpus callosum, i.e., with deficient or no connectivity between cortical hemispheres, similarly exhibit standing EEG waves in the form of spherical harmonics. We conclude that spherical harmonics are a hallmark of spontaneous, large-scale synchronization of neural activity in the brain, which are associated with unconscious, light sleep. The analogy with spherical harmonics in quantum mechanics suggests that the variances (eigenvalues) of the principal components follow a Boltzmann distribution, or equivalently, that standing waves are in a sort of "thermodynamic" equilibrium during non-REM sleep. By extension, we speculate that consciousness emerges as the brain dynamics deviate from such equilibrium.

Spherical Harmonics Reveal Standing EEG Waves and Long-Range Neural Synchronization during Non-REM Sleep

PubMed Central

Sivakumar, Siddharth S.; Namath, Amalia G.; Galán, Roberto F.

2016-01-01

Previous work from our lab has demonstrated how the connectivity of brain circuits constrains the repertoire of activity patterns that those circuits can display. Specifically, we have shown that the principal components of spontaneous neural activity are uniquely determined by the underlying circuit connections, and that although the principal components do not uniquely resolve the circuit structure, they do reveal important features about it. Expanding upon this framework on a larger scale of neural dynamics, we have analyzed EEG data recorded with the standard 10–20 electrode system from 41 neurologically normal children and adolescents during stage 2, non-REM sleep. We show that the principal components of EEG spindles, or sigma waves (10–16 Hz), reveal non-propagating, standing waves in the form of spherical harmonics. We mathematically demonstrate that standing EEG waves exist when the spatial covariance and the Laplacian operator on the head's surface commute. This in turn implies that the covariance between two EEG channels decreases as the inverse of their relative distance; a relationship that we corroborate with empirical data. Using volume conduction theory, we then demonstrate that superficial current sources are more synchronized at larger distances, and determine the characteristic length of large-scale neural synchronization as 1.31 times the head radius, on average. Moreover, consistent with the hypothesis that EEG spindles are driven by thalamo-cortical rather than cortico-cortical loops, we also show that 8 additional patients with hypoplasia or complete agenesis of the corpus callosum, i.e., with deficient or no connectivity between cortical hemispheres, similarly exhibit standing EEG waves in the form of spherical harmonics. We conclude that spherical harmonics are a hallmark of spontaneous, large-scale synchronization of neural activity in the brain, which are associated with unconscious, light sleep. The analogy with spherical harmonics in quantum mechanics suggests that the variances (eigenvalues) of the principal components follow a Boltzmann distribution, or equivalently, that standing waves are in a sort of “thermodynamic” equilibrium during non-REM sleep. By extension, we speculate that consciousness emerges as the brain dynamics deviate from such equilibrium. PMID:27445777

Method for Improving EEG Based Emotion Recognition by Combining It with Synchronized Biometric and Eye Tracking Technologies in a Non-invasive and Low Cost Way

PubMed Central

López-Gil, Juan-Miguel; Virgili-Gomá, Jordi; Gil, Rosa; Guilera, Teresa; Batalla, Iolanda; Soler-González, Jorge; García, Roberto

2016-01-01

Technical advances, particularly the integration of wearable and embedded sensors, facilitate tracking of physiological responses in a less intrusive way. Currently, there are many devices that allow gathering biometric measurements from human beings, such as EEG Headsets or Health Bracelets. The massive data sets generated by tracking of EEG and physiology may be used, among other things, to infer knowledge about human moods and emotions. Apart from direct biometric signal measurement, eye tracking systems are nowadays capable of determining the point of gaze of the users when interacting in ICT environments, which provides an added value research on many different areas, such as psychology or marketing. We present a process in which devices for eye tracking, biometric, and EEG signal measurements are synchronously used for studying both basic and complex emotions. We selected the least intrusive devices for different signal data collection given the study requirements and cost constraints, so users would behave in the most natural way possible. On the one hand, we have been able to determine basic emotions participants were experiencing by means of valence and arousal. On the other hand, a complex emotion such as empathy has also been detected. To validate the usefulness of this approach, a study involving forty-four people has been carried out, where they were exposed to a series of affective stimuli while their EEG activity, biometric signals, and eye position were synchronously recorded to detect self-regulation. The hypothesis of the work was that people who self-regulated would show significantly different results when analyzing their EEG data. Participants were divided into two groups depending on whether Electro Dermal Activity (EDA) data indicated they self-regulated or not. The comparison of the results obtained using different machine learning algorithms for emotion recognition shows that using EEG activity alone as a predictor for self-regulation does not allow properly determining whether a person in self-regulation its emotions while watching affective stimuli. However, adequately combining different data sources in a synchronous way to detect emotions makes it possible to overcome the limitations of single detection methods. PMID:27594831

Method for Improving EEG Based Emotion Recognition by Combining It with Synchronized Biometric and Eye Tracking Technologies in a Non-invasive and Low Cost Way.

PubMed

López-Gil, Juan-Miguel; Virgili-Gomá, Jordi; Gil, Rosa; García, Roberto

2016-01-01

Technical advances, particularly the integration of wearable and embedded sensors, facilitate tracking of physiological responses in a less intrusive way. Currently, there are many devices that allow gathering biometric measurements from human beings, such as EEG Headsets or Health Bracelets. The massive data sets generated by tracking of EEG and physiology may be used, among other things, to infer knowledge about human moods and emotions. Apart from direct biometric signal measurement, eye tracking systems are nowadays capable of determining the point of gaze of the users when interacting in ICT environments, which provides an added value research on many different areas, such as psychology or marketing. We present a process in which devices for eye tracking, biometric, and EEG signal measurements are synchronously used for studying both basic and complex emotions. We selected the least intrusive devices for different signal data collection given the study requirements and cost constraints, so users would behave in the most natural way possible. On the one hand, we have been able to determine basic emotions participants were experiencing by means of valence and arousal. On the other hand, a complex emotion such as empathy has also been detected. To validate the usefulness of this approach, a study involving forty-four people has been carried out, where they were exposed to a series of affective stimuli while their EEG activity, biometric signals, and eye position were synchronously recorded to detect self-regulation. The hypothesis of the work was that people who self-regulated would show significantly different results when analyzing their EEG data. Participants were divided into two groups depending on whether Electro Dermal Activity (EDA) data indicated they self-regulated or not. The comparison of the results obtained using different machine learning algorithms for emotion recognition shows that using EEG activity alone as a predictor for self-regulation does not allow properly determining whether a person in self-regulation its emotions while watching affective stimuli. However, adequately combining different data sources in a synchronous way to detect emotions makes it possible to overcome the limitations of single detection methods.

Imaging the cortical effect of lamotrigine in patients with idiopathic generalized epilepsy: a low-resolution electromagnetic tomography (LORETA) study.

PubMed

Clemens, Béla; Piros, Pálma; Bessenyei, Mónika; Tóth, Márton; Hollódy, Katalin; Kondákor, István

2008-10-01

Anatomical localization of the cortical effect of lamotrigine (LTG) in patients with idiopathic generalized epilepsy (IGE). 19 patients with untreated IGE were investigated. EEG was recorded in the untreated condition and 3 months later when LTG treatment abolished the seizures. 19-channel EEG was recorded, and a total of 2min artifact-free, waking EEG was processed to low-resolution electromagnetic tomography (LORETA) analysis. Activity (that is, current source density, A/m(2)) was computed in four frequency bands (delta, theta, alpha, and beta), for 2394 voxels that represented the cortical gray matter and the hippocampi. Group differences between the untreated and treated conditions were computed for the four bands and all voxels by multiple t-tests for interdependent datasets. The results were presented in terms of anatomical distribution and statistical significance. p<0.01 (uncorrected) changes (decrease of activity) emerged in the theta and the alpha bands. Theta activity decreased in a large cluster of voxels including parts of the temporal, parietal, occipital cortex bilaterally, and in the transverse temporal gyri, insula, hippocampus, and uncus on the right side. Alpha activity decreased in a relatively smaller cortical area involving the right temporo-parietal junction and surrounding parts of the cortex, and part of the insula on the right side. LTG decreased theta activity in several cortical areas where abnormally increased theta activity had been found in a prior study in another cohort of untreated IGE patients [Clemens, B., Bessenyei, M., Piros, P., Tóth, M., Seress, L., Kondákor, I., 2007b. Characteristic distribution of interictal brain electrical activity in idiopathic generalized epilepsy. Epilepsia 48, 941-949]. These LTG-related changes might be related to the decrease of seizure propensity in IGE.

Experimental validation of the influence of white matter anisotropy on the intracranial EEG forward solution

PubMed Central

Schomer, Donald L.; Dehghani, Nima; Ulbert, Istvan; Cash, Sydney; Papavasiliou, Steve; Eisenberg, Solomon R.; Dale, Anders M.; Halgren, Eric

2010-01-01

Forward solutions with different levels of complexity are employed for localization of current generators, which are responsible for the electric and magnetic fields measured from the human brain. The influence of brain anisotropy on the forward solution is poorly understood. The goal of this study is to validate an anisotropic model for the intracranial electric forward solution by comparing with the directly measured ‘gold standard’. Dipolar sources are created at known locations in the brain and intracranial electroencephalogram (EEG) is recorded simultaneously. Isotropic models with increasing level of complexity are generated along with anisotropic models based on Diffusion tensor imaging (DTI). A Finite Element Method based forward solution is calculated and validated using the measured data. Major findings are (1) An anisotropic model with a linear scaling between the eigenvalues of the electrical conductivity tensor and water self-diffusion tensor in brain tissue is validated. The greatest improvement was obtained when the stimulation site is close to a region of high anisotropy. The model with a global anisotropic ratio of 10:1 between the eigenvalues (parallel: tangential to the fiber direction) has the worst performance of all the anisotropic models. (2) Inclusion of cerebrospinal fluid as well as brain anisotropy in the forward model is necessary for an accurate description of the electric field inside the skull. The results indicate that an anisotropic model based on the DTI can be constructed non-invasively and shows an improved performance when compared to the isotropic models for the calculation of the intracranial EEG forward solution. Electronic supplementary material The online version of this article (doi:10.1007/s10827-009-0205-z) contains supplementary material, which is available to authorized users. PMID:20063051

The Brain Computer Interface Future: Time for a Strategy

DTIC Science & Technology

2013-02-14

electrophysiological activity can be measured by electroencepholography ( EEG ), electrocorticography (ECoG), magnetoencephalography (MEG), or signal activity...magnetic resonance imaging (MRI) or near infrared spectroscopy. Currently EEG is most the most widely used BCI interface due to high temporal...resolution, less user risk, and lower costs.12 EEG technology has been widely available for many decades but has significantly expanded as researchers

Increase in short-term memory capacity induced by down-regulating individual theta frequency via transcranial alternating current stimulation.

PubMed

Vosskuhl, Johannes; Huster, René J; Herrmann, Christoph S

2015-01-01

Working memory (WM) and short-term memory (STM) supposedly rely on the phase-amplitude coupling (PAC) of neural oscillations in the theta and gamma frequency ranges. The ratio between the individually dominant gamma and theta frequencies is believed to determine an individual's memory capacity. The aim of this study was to establish a causal relationship between the gamma/theta ratio and WM/STM capacity by means of transcranial alternating current stimulation (tACS). To achieve this, tACS was delivered at a frequency below the individual theta frequency. Thereby the individual ratio of gamma to theta frequencies was changed, resulting in an increase of STM capacity. Healthy human participants (N = 33) were allocated to two groups, one receiving verum tACS, the other underwent a sham control protocol. The electroencephalogram (EEG) was measured before stimulation and analyzed with regard to the properties of PAC between theta and gamma frequencies to determine individual stimulation frequencies. After stimulation, EEG was recorded again in order to find after-effects of tACS in the oscillatory features of the EEG. Measures of STM and WM were obtained before, during and after stimulation. Frequency spectra and behavioral data were compared between groups and different measurement phases. The tACS- but not the sham stimulated group showed an increase in STM capacity during stimulation. WM was not affected in either groups. An increase in task-related theta amplitude after stimulation was observed only for the tACS group. These augmented theta amplitudes indicated that the manipulation of individual theta frequencies was successful and caused the increase in STM capacity.

EEG oscillations and recognition memory: theta correlates of memory retrieval and decision making.

PubMed

Jacobs, Joshua; Hwang, Grace; Curran, Tim; Kahana, Michael J

2006-08-15

Studies of memory retrieval have identified electroencephalographic (EEG) correlates of a test item's old-new status, reaction time, and memory load. In the current study, we used a multivariate analysis to disentangle the effects of these correlated variables. During retrieval, power of left-parietal theta (4-8 Hz) oscillations increased in proportion to how well a test item was remembered, and theta in central regions correlated with decision making. We also studied how these oscillatory dynamics complemented event-related potentials. These findings are the first to demonstrate that distinct patterns of theta oscillations can simultaneously relate to different aspects of behavior.

Application of Independent Component Analysis for the Data Mining of Simultaneous EEG-fMRI: Preliminary Experience on Sleep Onset

PubMed Central

Lee, Jong-Hwan; Oh, Sungsuk; Jolesz, Ferenc A.; Park, Hyunwook; Yoo, Seung-Schik

2010-01-01

The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with the ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta- and alpha-rhythms that are sleep onset related EEG signatures along with the subsequent neural circuitries from a sleep deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEG-fMRI acquisitions, especially when a reference paradigm is unavailable. PMID:19922343

Application of independent component analysis for the data mining of simultaneous Eeg-fMRI: preliminary experience on sleep onset.

PubMed

Lee, Jong-Hwan; Oh, Sungsuk; Jolesz, Ferenc A; Park, Hyunwook; Yoo, Seung-Schik

2009-01-01

The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta and alpha rhythms that are sleep onset-related EEG signatures along with the subsequent neural circuitries from a sleep-deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEG-fMRI acquisitions, especially when a reference paradigm is unavailable.

Heightened Background Cortical Synchrony in Patients With Epilepsy: EEG Phase Synchrony Analysis During Awake and Sleep Stages Using Novel Ensemble Measure.

PubMed

Nayak, Chetan S; Mariyappa, N; Majumdar, Kaushik K; Prasad, Pradeep D; Ravi, G S; Nagappa, M; Kandavel, Thennarasu; Taly, Arun B; Sinha, Sanjib

2018-05-01

Excessive cortical synchrony within neural ensembles has been implicated as an important mechanism driving epileptiform activity. The current study measures and compares background electroencephalographic (EEG) phase synchronization in patients having various types of epilepsies and healthy controls during awake and sleep stages. A total of 120 patients with epilepsy (PWE) subdivided into 3 groups (juvenile myoclonic epilepsy [JME], temporal lobe epilepsy [TLE], and extra-temporal lobe epilepsy [Ex-TLE]; n = 40 in each group) and 40 healthy controls were subjected to overnight polysomnography. EEG phase synchronization (SI) between the 8 EEG channels was assessed for delta, theta, alpha, sigma, and high beta frequency bands using ensemble measure on 10-second representative time windows and compared between patients and controls and also between awake and sleep stages. Mean ± SD of SI was compared using 2-way analysis of variance followed by pairwise comparison ( P ≤ .05). In both delta and theta bands, the SI was significantly higher in patients with JME, TLE, and Ex-TLE compared with controls, whereas in alpha, sigma, and high beta bands, SI was comparable between the groups. On comparison of SI between sleep stages, delta band: progressive increase in SI from wake ⇒ N1 ⇒ N2 ⇒ N3, whereas REM (rapid eye movement) was comparable to wake; theta band: decreased SI during N2 and increase during N3; alpha band: SI was highest in wake and lower in N1, N2, N3, and REM; and sigma and high beta bands: progressive increase in SI from wake ⇒ N1 ⇒ N2 ⇒ N3; however, sigma band showed lower SI during REM. This study found an increased background cortical synchronization in PWE compared with healthy controls in delta and theta bands during wake and sleep. This background hypersynchrony may be an important property of epileptogenic brain circuitry in PWE, which enables them to effortlessly generate a paroxysmal EEG depolarization shift.

Non-convulsive seizures and non-convulsive status epilepticus monitoring in the intensive care unit. A real need for the Gulf Cooperation Council countries.

PubMed

Mesraoua, Boulenouar; Wieser, Heinz G

2009-10-01

Continuous EEG (cEEG) monitoring in the intensive care unit (ICU) is essential for detecting non-convulsive seizures/status epilepticus (NCSs, NCSE). Currently there exist a number of continuous EEG monitoring systems adapted for use in the ICU. However, these systems have been trained using EEG data collected from healthy, neurologically intact patients with epileptic seizures, a very different patient population from ICU patients. The review consists of 2 parts, clinical and technological aspects. In the first one, we summarize the electroencephalographic aspects of NCSs/NCSE and other EEG patterns encountered in the ICU. In the second part, we explain how to develop a novel cEEG monitoring system to be used in Hamad Medical Corporation ICUs, Doha, Qatar, that is able to detect pathological EEG patterns commonly occurring in the critically ill patient. Real-time monitoring of seizure discharges, and other pathological EEG patterns will allow correct diagnosis and adequate treatment in a timely fashion.

Modulation of Electrophysiology by Transcranial Direct Current Stimulation in Psychiatric Disorders: A Systematic Review.

PubMed

Kim, Minah; Kwak, Yoo Bin; Lee, Tae Young; Kwon, Jun Soo

2018-04-27

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique increasingly used to relieve symptoms of psychiatric disorders. Electrophysiologic markers, such as electroencephalography (EEG) and event-related potentials (ERP), have high temporal resolution sensitive to detect plastic changes of the brain associated with symptomatic improvement following tDCS application. We performed systematic review to identify electrophysiological markers that reflect tDCS effects on plastic brain changes in psychiatric disorders. A total of 638 studies were identified by searching PubMed, Embase, psychINFPO. Of these, 21 full-text articles were assessed eligible and included in the review. Although the reviewed studies were heterogeneous in their choices of tDCS protocols, targeted electrophysiological markers, and disease entities, their results strongly support EEG/ERPs to sensitively reflect plastic brain changes and the associated symptomatic improvement following tDCS. EEG/ERPs may serve a potent tool in revealing the mechanisms underlying psychiatric symptoms, as well as in localizing the brain area targeted for stimulation. Future studies in each disease entities employing consistent tDCS protocols and electrophysiological markers would be necessary in order to substantiate and further elaborate the findings of studies included in the present systematic review.

Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2018-01-01

The aim of our current study is to check whether multifractal patterns of the electroencephalographic (EEG) signals of normal and epileptic patients are statistically similar or different. In this regard, the generalized Hurst exponent (GHE) method is used for robust estimation of the multifractals in each type of EEG signals, and three powerful statistical tests are performed to check existence of differences between estimated GHEs from healthy control subjects and epileptic patients. The obtained results show that multifractals exist in both types of EEG signals. Particularly, it was found that the degree of fractal is more pronounced in short variations of normal EEG signals than in short variations of EEG signals with seizure free intervals. In contrary, it is more pronounced in long variations of EEG signals with seizure free intervals than in normal EEG signals. Importantly, both parametric and nonparametric statistical tests show strong evidence that estimated GHEs of normal EEG signals are statistically and significantly different from those with seizure free intervals. Therefore, GHEs can be efficiently used to distinguish between healthy and patients suffering from epilepsy.

Mushu, a free- and open source BCI signal acquisition, written in Python.

PubMed

Venthur, Bastian; Blankertz, Benjamin

2012-01-01

The following paper describes Mushu, a signal acquisition software for retrieval and online streaming of Electroencephalography (EEG) data. It is written, but not limited, to the needs of Brain Computer Interfacing (BCI). It's main goal is to provide a unified interface to EEG data regardless of the amplifiers used. It runs under all major operating systems, like Windows, Mac OS and Linux, is written in Python and is free- and open source software licensed under the terms of the GNU General Public License.

A recursive algorithm for the three-dimensional imaging of brain electric activity: Shrinking LORETA-FOCUSS.

PubMed

Liu, Hesheng; Gao, Xiaorong; Schimpf, Paul H; Yang, Fusheng; Gao, Shangkai

2004-10-01

Estimation of intracranial electric activity from the scalp electroencephalogram (EEG) requires a solution to the EEG inverse problem, which is known as an ill-conditioned problem. In order to yield a unique solution, weighted minimum norm least square (MNLS) inverse methods are generally used. This paper proposes a recursive algorithm, termed Shrinking LORETA-FOCUSS, which combines and expands upon the central features of two well-known weighted MNLS methods: LORETA and FOCUSS. This recursive algorithm makes iterative adjustments to the solution space as well as the weighting matrix, thereby dramatically reducing the computation load, and increasing local source resolution. Simulations are conducted on a 3-shell spherical head model registered to the Talairach human brain atlas. A comparative study of four different inverse methods, standard Weighted Minimum Norm, L1-norm, LORETA-FOCUSS and Shrinking LORETA-FOCUSS are presented. The results demonstrate that Shrinking LORETA-FOCUSS is able to reconstruct a three-dimensional source distribution with smaller localization and energy errors compared to the other methods.

Value of electrical stimulation and high frequency oscillations (80–500 Hz) in identifying epileptogenic areas during intracranial EEG recordings

PubMed Central

Jacobs, Julia; Zijlmans, Maeike; Zelmann, Rina; Olivier, André; Hall, Jeffery; Gotman, Jean; Dubeau, François

2013-01-01

Summary Purpose Electrical stimulation (ES) is used during intracranial electroencephalography (EEG) investigations to delineate epileptogenic areas and seizure-onset zones (SOZs) by provoking afterdischarges (ADs) or patients’ typical seizure. High frequency oscillations (HFOs—ripples, 80–250 Hz; fast ripples, 250–500 Hz) are linked to seizure onset. This study investigates whether interictal HFOs are more frequent in areas with a low threshold to provoke ADs or seizures. Methods Intracranial EEG studies were filtered at 500 Hz and sampled at 2,000 Hz. HFOs were visually identified. Twenty patients underwent ES, with gradually increasing currents. Results were interpreted as agreeing or disagreeing with the intracranial study (clinical-EEG seizure onset defined the SOZ). Current thresholds provoking an AD or seizure were correlated with the rate of HFOs of each channel. Results ES provoked a seizure in 12 and ADs in 19 patients. Sixteen patients showed an ES response inside the SOZ, and 10 had additional areas with ADs. The response was more specific for mesiotemporal than for neocortical channels. HFO rates were negatively correlated with thresholds for ES responses; especially in neo-cortical regions; areas with low threshold and high HFO rate were colocalized even outside the SOZ. Discussion Areas showing epileptic HFOs colocalize with those reacting to ES. HFOs may represent a pathologic correlate of regions showing an ES response; both phenomena suggest a more widespread epileptogenicity. PMID:19845730

CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave.

PubMed

Oosterhof, Nikolaas N; Connolly, Andrew C; Haxby, James V

2016-01-01

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques. CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality. CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian. CoSMoMVPA is Free/Open Source Software under the permissive MIT license. Website: http://cosmomvpa.org Source code: https://github.com/CoSMoMVPA/CoSMoMVPA.

CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave

PubMed Central

Oosterhof, Nikolaas N.; Connolly, Andrew C.; Haxby, James V.

2016-01-01

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques. CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality. CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian. CoSMoMVPA is Free/Open Source Software under the permissive MIT license. Website: http://cosmomvpa.org Source code: https://github.com/CoSMoMVPA/CoSMoMVPA PMID:27499741

Unsupervised Segmentation of Head Tissues from Multi-modal MR Images for EEG Source Localization.

PubMed

Mahmood, Qaiser; Chodorowski, Artur; Mehnert, Andrew; Gellermann, Johanna; Persson, Mikael

2015-08-01

In this paper, we present and evaluate an automatic unsupervised segmentation method, hierarchical segmentation approach (HSA)-Bayesian-based adaptive mean shift (BAMS), for use in the construction of a patient-specific head conductivity model for electroencephalography (EEG) source localization. It is based on a HSA and BAMS for segmenting the tissues from multi-modal magnetic resonance (MR) head images. The evaluation of the proposed method was done both directly in terms of segmentation accuracy and indirectly in terms of source localization accuracy. The direct evaluation was performed relative to a commonly used reference method brain extraction tool (BET)-FMRIB's automated segmentation tool (FAST) and four variants of the HSA using both synthetic data and real data from ten subjects. The synthetic data includes multiple realizations of four different noise levels and several realizations of typical noise with a 20% bias field level. The Dice index and Hausdorff distance were used to measure the segmentation accuracy. The indirect evaluation was performed relative to the reference method BET-FAST using synthetic two-dimensional (2D) multimodal magnetic resonance (MR) data with 3% noise and synthetic EEG (generated for a prescribed source). The source localization accuracy was determined in terms of localization error and relative error of potential. The experimental results demonstrate the efficacy of HSA-BAMS, its robustness to noise and the bias field, and that it provides better segmentation accuracy than the reference method and variants of the HSA. They also show that it leads to a more accurate localization accuracy than the commonly used reference method and suggest that it has potential as a surrogate for expert manual segmentation for the EEG source localization problem.

Comparison between Scalp EEG and Behind-the-Ear EEG for Development of a Wearable Seizure Detection System for Patients with Focal Epilepsy

PubMed Central

Gu, Ying; Cleeren, Evy; Dan, Jonathan; Claes, Kasper; Hunyadi, Borbála

2017-01-01

A wearable electroencephalogram (EEG) device for continuous monitoring of patients suffering from epilepsy would provide valuable information for the management of the disease. Currently no EEG setup is small and unobtrusive enough to be used in daily life. Recording behind the ear could prove to be a solution to a wearable EEG setup. This article examines the feasibility of recording epileptic EEG from behind the ear. It is achieved by comparison with scalp EEG recordings. Traditional scalp EEG and behind-the-ear EEG were simultaneously acquired from 12 patients with temporal, parietal, or occipital lobe epilepsy. Behind-the-ear EEG consisted of cross-head channels and unilateral channels. The analysis on Electrooculography (EOG) artifacts resulting from eye blinking showed that EOG artifacts were absent on cross-head channels and had significantly small amplitudes on unilateral channels. Temporal waveform and frequency content during seizures from behind-the-ear EEG visually resembled that from scalp EEG. Further, coherence analysis confirmed that behind-the-ear EEG acquired meaningful epileptic discharges similarly to scalp EEG. Moreover, automatic seizure detection based on support vector machine (SVM) showed that comparable seizure detection performance can be achieved using these two recordings. With scalp EEG, detection had a median sensitivity of 100% and a false detection rate of 1.14 per hour, while, with behind-the-ear EEG, it had a median sensitivity of 94.5% and a false detection rate of 0.52 per hour. These findings demonstrate the feasibility of detecting seizures from EEG recordings behind the ear for patients with focal epilepsy. PMID:29295522

Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction

NASA Technical Reports Server (NTRS)

Smith, M. E.; Gevins, A.; Brown, H.; Karnik, A.; Du, R.

2001-01-01

Electroencephalographic (EEG) recordings were made while 16 participants performed versions of a personal-computer-based flight simulation task of low, moderate, or high difficulty. As task difficulty increased, frontal midline theta EEG activity increased and alpha band activity decreased. A participant-specific function that combined multiple EEG features to create a single load index was derived from a sample of each participant's data and then applied to new test data from that participant. Index values were computed for every 4 s of task data. Across participants, mean task load index values increased systematically with increasing task difficulty and differed significantly between the different task versions. Actual or potential applications of this research include the use of multivariate EEG-based methods to monitor task loading during naturalistic computer-based work.

EEG oscillatory power dissociates between distress- and depression-related psychopathology in subjective tinnitus.

PubMed

Meyer, Martin; Neff, Patrick; Grest, Angelina; Hemsley, Colette; Weidt, Steffi; Kleinjung, Tobias

2017-05-15

Recent research has used source estimation approaches to identify spatially distinct neural configurations in individuals with chronic, subjective tinnitus (TI). The results of these studies are often heterogeneous, a fact which may be partly explained by an inherent heterogeneity in the TI population and partly by the applied EEG data analysis procedure and EEG hardware. Hence this study was performed to re-enact a formerly published study (Joos et al., 2012) to better understand the reason for differences and overlap between studies from different labs. We re-investigated the relationship between neural oscillations and behavioral measurements of affective states in TI, namely depression and tinnitus-related distress by recruiting 45 TI who underwent resting-state EEG. Comprehensive psychopathological (depression and tinnitus-related distress scores) and psychometric data (including other tinnitus characteristics) were gathered. A principal component analysis (PCA) was performed to unveil independent factors that predict distinct aspects of tinnitus-related pathology. Furthermore, we correlated EEG power changes in the standard frequency bands with the behavioral scores for both the whole-brain level and, as a post hoc approach, for selected regions of interest (ROI) based on sLORETA. Behavioral data revealed significant relationships between measurements of depression and tinnitus-related distress. Notably, no significant results were observed for the depressive scores and modulations of the EEG signal. However, akin to the former study we evidenced a significant relationship between a power increase in the β-bands and tinnitus-related distress. In conclusion, it has emerged that depression and tinnitus-related distress, even though they are assumed not to be completely independent, manifest in distinct neural configurations. Copyright © 2017 Elsevier B.V. All rights reserved.

Dynamics of alpha control: Preparatory suppression of posterior alpha oscillations by frontal modulators revealed with combined EEG and event-related optical signal (EROS)

PubMed Central

Mathewson, Kyle E.; Beck, Diane M.; Ro, Tony; Maclin, Edward L.; Low, Kathy A.; Fabiani, Monica; Gratton, Gabriele

2015-01-01

We investigated the dynamics of brain processes facilitating conscious experience of external stimuli. Previously we proposed that alpha (8-12 Hz) oscillations, which fluctuate with both sustained and directed attention, represent a pulsed inhibition of ongoing sensory brain activity. Here we tested the prediction that inhibitory alpha oscillations in visual cortex are modulated by top-down signals from frontoparietal attention networks. We measured modulations in phase-coherent alpha oscillations from superficial frontal, parietal, and occipital cortices using the event-related optical signal (EROS), a measure of neuronal activity affording high spatiotemporal resolution, along with concurrently-recorded electroencephalogram (EEG), while subjects performed a visual target-detection task. The pre-target alpha oscillations measured with EEG and EROS from posterior areas were larger for subsequently undetected targets, supporting alpha's inhibitory role. Using EROS, we localized brain correlates of these awareness-related alpha oscillations measured at the scalp to the cuneus and precuneus. Crucially, EROS alpha suppression correlated with posterior EEG alpha power across subjects. Sorting the EROS data based on EEG alpha power quartiles to investigate alpha modulators revealed that suppression of posterior alpha was preceded by increased activity in regions of the dorsal attention network, and decreased activity in regions of the cingulo-opercular network. Cross-correlations revealed the temporal dynamics of activity within these preparatory networks prior to posterior alpha modulation. The novel combination of EEG and EROS afforded localization of the sources and correlates of alpha oscillations and their temporal relationships, supporting our proposal that top-down control from attention networks modulates both posterior alpha and awareness of visual stimuli. PMID:24702458

Mapping (and modeling) physiological movements during EEG-fMRI recordings: the added value of the video acquired simultaneously.

PubMed

Ruggieri, Andrea; Vaudano, Anna Elisabetta; Benuzzi, Francesca; Serafini, Marco; Gessaroli, Giuliana; Farinelli, Valentina; Nichelli, Paolo Frigio; Meletti, Stefano

2015-01-15

During resting-state EEG-fMRI studies in epilepsy, patients' spontaneous head-face movements occur frequently. We tested the usefulness of synchronous video recording to identify and model the fMRI changes associated with non-epileptic movements to improve sensitivity and specificity of fMRI maps related to interictal epileptiform discharges (IED). Categorization of different facial/cranial movements during EEG-fMRI was obtained for 38 patients [with benign epilepsy with centro-temporal spikes (BECTS, n=16); with idiopathic generalized epilepsy (IGE, n=17); focal symptomatic/cryptogenic epilepsy (n=5)]. We compared at single subject- and at group-level the IED-related fMRI maps obtained with and without additional regressors related to spontaneous movements. As secondary aim, we considered facial movements as events of interest to test the usefulness of video information to obtain fMRI maps of the following face movements: swallowing, mouth-tongue movements, and blinking. Video information substantially improved the identification and classification of the artifacts with respect to the EEG observation alone (mean gain of 28 events per exam). Inclusion of physiological activities as additional regressors in the GLM model demonstrated an increased Z-score and number of voxels of the global maxima and/or new BOLD clusters in around three quarters of the patients. Video-related fMRI maps for swallowing, mouth-tongue movements, and blinking were comparable to the ones obtained in previous task-based fMRI studies. Video acquisition during EEG-fMRI is a useful source of information. Modeling physiological movements in EEG-fMRI studies for epilepsy will lead to more informative IED-related fMRI maps in different epileptic conditions. Copyright © 2014 Elsevier B.V. All rights reserved.

Sleep affects cortical source modularity in temporal lobe epilepsy: A high-density EEG study.

PubMed

Del Felice, Alessandra; Storti, Silvia Francesca; Manganotti, Paolo

2015-09-01

Interictal epileptiform discharges (IEDs) constitute a perturbation of ongoing cerebral rhythms, usually more frequent during sleep. The aim of the study was to determine whether sleep influences the spread of IEDs over the scalp and whether their distribution depends on vigilance-related modifications in cortical interactions. Wake and sleep 256-channel electroencephalography (EEG) data were recorded in 12 subjects with right temporal lobe epilepsy (TLE) differentiated by whether they had mesial or neocortical TLE. Spikes were selected during wake and sleep. The averaged waking signal was subtracted from the sleep signal and projected on a bidimensional scalp map; sleep and wake spike distributions were compared by using a t-test. The superimposed signal of sleep and wake traces was obtained; the rising phase of the spike, the peak, and the deflections following the spike were identified, and their cortical generator was calculated using low-resolution brain electromagnetic tomography (LORETA) for each group. A mean of 21 IEDs in wake and 39 in sleep per subject were selected. As compared to wake, a larger IED scalp projection was detected during sleep in both mesial and neocortical TLE (p<0.05). A series of EEG deflections followed the spike, the cortical sources of which displayed alternating activations of different cortical areas in wake, substituted by isolated, stationary activations in sleep in mesial TLE and a silencing in neocortical TLE. During sleep, the IED scalp region increases, while cortical interaction decreases. The interaction of cortical modules in sleep and wake in TLE may influence the appearance of IEDs on scalp EEG; in addition, IEDs could be proxies for cerebral oscillation perturbation. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Memories of attachment hamper EEG cortical connectivity in dissociative patients.

PubMed

Farina, Benedetto; Speranza, Anna Maria; Dittoni, Serena; Gnoni, Valentina; Trentini, Cristina; Vergano, Carola Maggiora; Liotti, Giovanni; Brunetti, Riccardo; Testani, Elisa; Della Marca, Giacomo

2014-08-01

In this study, we evaluated cortical connectivity modifications by electroencephalography (EEG) lagged coherence analysis, in subjects with dissociative disorders and in controls, after retrieval of attachment memories. We asked thirteen patients with dissociative disorders and thirteen age- and sex-matched healthy controls to retrieve personal attachment-related autobiographical memories through adult attachment interviews (AAI). EEG was recorded in the closed eyes resting state before and after the AAI. EEG lagged coherence before and after AAI was compared in all subjects. In the control group, memories of attachment promoted a widespread increase in EEG connectivity, in particular in the high-frequency EEG bands. Compared to controls, dissociative patients did not show an increase in EEG connectivity after the AAI. Conclusions: These results shed light on the neurophysiology of the disintegrative effect of retrieval of traumatic attachment memories in dissociative patients.

The standardized EEG electrode array of the IFCN.

PubMed

Seeck, Margitta; Koessler, Laurent; Bast, Thomas; Leijten, Frans; Michel, Christoph; Baumgartner, Christoph; He, Bin; Beniczky, Sándor

2017-10-01

Standardized EEG electrode positions are essential for both clinical applications and research. The aim of this guideline is to update and expand the unifying nomenclature and standardized positioning for EEG scalp electrodes. Electrode positions were based on 20% and 10% of standardized measurements from anatomical landmarks on the skull. However, standard recordings do not cover the anterior and basal temporal lobes, which is the most frequent source of epileptogenic activity. Here, we propose a basic array of 25 electrodes including the inferior temporal chain, which should be used for all standard clinical recordings. The nomenclature in the basic array is consistent with the 10-10-system. High-density scalp EEG arrays (64-256 electrodes) allow source imaging with even sub-lobar precision. This supplementary exam should be requested whenever necessary, e.g. search for epileptogenic activity in negative standard EEG or for presurgical evaluation. In the near future, nomenclature for high density electrodes arrays beyond the 10-10 system needs to be defined, to allow comparison and standardized recordings across centers. Contrary to the established belief that smaller heads needs less electrodes, in young children at least as many electrodes as in adults should be applied due to smaller skull thickness and the risk of spatial aliasing. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Human Supervision of Time Critical Control Systems. Addendum

DTIC Science & Technology

2010-02-26

signals such as electroencephalogram (EEG) and electrooculography ( EOG ). Current research has demonstrated these signals ’ ability to respond to changing...relationships often present in EEG/ EOG data; they routinely achieve classification accuracy greater than 80%. However, the discrete output of these...present data there were seven EEG and EOG signals recorded, thus, ICA assumes each were a mixture of seven independent components (Stone, 2002). Some

Brain order disorder 2nd group report of f-EEG

NASA Astrophysics Data System (ADS)

Lalonde, Francois; Gogtay, Nitin; Giedd, Jay; Vydelingum, Nadarajen; Brown, David; Tran, Binh Q.; Hsu, Charles; Hsu, Ming-Kai; Cha, Jae; Jenkins, Jeffrey; Ma, Lien; Willey, Jefferson; Wu, Jerry; Oh, Kenneth; Landa, Joseph; Lin, C. T.; Jung, T. P.; Makeig, Scott; Morabito, Carlo Francesco; Moon, Qyu; Yamakawa, Takeshi; Lee, Soo-Young; Lee, Jong-Hwan; Szu, Harold H.; Kaur, Balvinder; Byrd, Kenneth; Dang, Karen; Krzywicki, Alan; Familoni, Babajide O.; Larson, Louis; Harkrider, Susan; Krapels, Keith A.; Dai, Liyi

2014-05-01

Since the Brain Order Disorder (BOD) group reported on a high density Electroencephalogram (EEG) to capture the neuronal information using EEG to wirelessly interface with a Smartphone [1,2], a larger BOD group has been assembled, including the Obama BRAIN program, CUA Brain Computer Interface Lab and the UCSD Swartz Computational Neuroscience Center. We can implement the pair-electrodes correlation functions in order to operate in a real time daily environment, which is of the computation complexity of O(N3) for N=102~3 known as functional f-EEG. The daily monitoring requires two areas of focus. Area #(1) to quantify the neuronal information flow under arbitrary daily stimuli-response sources. Approach to #1: (i) We have asserted that the sources contained in the EEG signals may be discovered by an unsupervised learning neural network called blind sources separation (BSS) of independent entropy components, based on the irreversible Boltzmann cellular thermodynamics(ΔS < 0), where the entropy is a degree of uniformity. What is the entropy? Loosely speaking, sand on the beach is more uniform at a higher entropy value than the rocks composing a mountain - the internal binding energy tells the paleontologists the existence of information. To a politician, landside voting results has only the winning information but more entropy, while a non-uniform voting distribution record has more information. For the human's effortless brain at constant temperature, we can solve the minimum of Helmholtz free energy (H = E - TS) by computing BSS, and then their pairwise-entropy source correlation function. (i) Although the entropy itself is not the information per se, but the concurrence of the entropy sources is the information flow as a functional-EEG, sketched in this 2nd BOD report. Area #(2) applying EEG bio-feedback will improve collective decision making (TBD). Approach to #2: We introduce a novel performance quality metrics, in terms of the throughput rate of faster (Δt) & more accurate (ΔA) decision making, which applies to individual, as well as team brain dynamics. Following Nobel Laureate Daniel Kahnmen's novel "Thinking fast and slow", through the brainwave biofeedback we can first identify an individual's "anchored cognitive bias sources". This is done in order to remove the biases by means of individually tailored pre-processing. Then the training effectiveness can be maximized by the collective product Δt * ΔA. For Area #1, we compute a spatiotemporally windowed EEG in vitro average using adaptive time-window sampling. The sampling rate depends on the type of neuronal responses, which is what we seek. The averaged traditional EEG measurements and are further improved by BSS decomposition into finer stimulus-response source mixing matrix [A] having finer & faster spatial grids with rapid temporal updates. Then, the functional EEG is the second order co-variance matrix defined as the electrode-pair fluctuation correlation function C(s~, s~') of independent thermodynamic source components. (1) We define a 1-D Space filling curve as a spiral curve without origin. This pattern is historically known as the Peano-Hilbert arc length a. By taking the most significant bits of the Cartesian product a≡ O(x * y * z), it represents the arc length in the numerical size with values that map the 3-D neighborhood proximity into a 1-D neighborhood arc length representation. (2) 1-D Fourier coefficients spectrum have no spurious high frequency contents, which typically arise in lexicographical (zig-zag scanning) discontinuity [Hsu & Szu, "Peano-Hilbert curve," SPIE 2014]. A simple Fourier spectrum histogram fits nicely with the Compressive Sensing CRDT Mathematics. (3) Stationary power spectral density is a reasonable approximation of EEG responses in striate layers in resonance feedback loops capable of producing a 100, 000 neuronal collective Impulse Response Function (IRF). The striate brain layer architecture represents an ensemble

Linking EEG signals, brain functions and mental operations: Advantages of the Laplacian transformation.

PubMed

Vidal, Franck; Burle, Boris; Spieser, Laure; Carbonnell, Laurence; Meckler, Cédric; Casini, Laurence; Hasbroucq, Thierry

2015-09-01

Electroencephalography (EEG) is a very popular technique for investigating brain functions and/or mental processes. To this aim, EEG activities must be interpreted in terms of brain and/or mental processes. EEG signals being a direct manifestation of neuronal activity it is often assumed that such interpretations are quite obvious or, at least, straightforward. However, they often rely on (explicit or even implicit) assumptions regarding the structures supposed to generate the EEG activities of interest. For these assumptions to be used appropriately, reliable links between EEG activities and the underlying brain structures must be established. Because of volume conduction effects and the mixture of activities they induce, these links are difficult to establish with scalp potential recordings. We present different examples showing how the Laplacian transformation, acting as an efficient source separation method, allowed to establish more reliable links between EEG activities and brain generators and, ultimately, with mental operations. The nature of those links depends on the depth of inferences that can vary from weak to strong. Along this continuum, we show that 1) while the effects of experimental manipulation can appear widely distributed with scalp potentials, Laplacian transformation allows to reveal several generators contributing (in different manners) to these modulations, 2) amplitude variations within the same set of generators can generate spurious differences in scalp potential topographies, often interpreted as reflecting different source configurations. In such a case, Laplacian transformation provides much more similar topographies, evidencing the same generator(s) set, and 3) using the LRP as an index of response activation most often produces ambiguous results, Laplacian-transformed response-locked ERPs obtained over motor areas allow resolving these ambiguities. Copyright © 2015 Elsevier B.V. All rights reserved.

Junior temperament character inventory together with quantitative EEG discriminate children with attention deficit hyperactivity disorder combined subtype from children with attention deficit hyperactivity disorder combined subtype plus oppositional defiant disorder.

PubMed

Chiarenza, Giuseppe A; Villa, Stefania; Galan, Lidice; Valdes-Sosa, Pedro; Bosch-Bayard, Jorge

2018-05-19

Oppositional defiant disorder (ODD) is frequently associated with Attention Deficit Hyperactivity Disorder (ADHD) but no clear neurophysiological evidence exists that distinguishes the two groups. Our aim was to identify biomarkers that distinguish children with Attention Deficit Hyperactivity Disorder combined subtype (ADHD_C) from children with ADHD_C + ODD, by combining the results of quantitative EEG (qEEG) and the Junior Temperament Character Inventory (JTCI). 28 ADHD_C and 22 ADHD_C + ODD children who met the DSMV criteria participated in the study. JTCI and EEG were analyzed. Stability based Biomarkers identification methodology was applied to the JTCI and the qEEG separately and combined. The qEEG was tested at the scalp and the sources levels. The classification power of the selected biomarkers was tested with a robust ROC technique. The best discriminant power was obtained when TCI and qEEG were analyzed together. Novelty seeking, self-directedness and cooperativeness were selected as biomarkers together with F4 and Cz in Delta; Fz and F4 in Theta and F7 and F8 in Beta, with a robust AUC of 0.95 for the ROC. At sources level: the regions were the right lateral and medial orbito-frontal cortex, cingular region, angular gyrus, right inferior occipital gyrus, occipital pole and the left insula in Theta, Alpha and Beta. The robust estimate of the total AUC was 0.91. These structures are part of extensive networks of novelty seeking, self-directedness and cooperativeness systems that seem dysregulated in these children. These methods represent an original approach to associate differences of personality and behavior to specific neuronal systems and subsystems. Copyright © 2018 Elsevier B.V. All rights reserved.

Nonlinear analysis of EEG in major depression with fractal dimensions.

PubMed

Akar, Saime A; Kara, Sadik; Agambayev, Sumeyra; Bilgic, Vedat

2015-01-01

Major depressive disorder (MDD) is a psychiatric mood disorder characterized by cognitive and functional impairments in attention, concentration, learning and memory. In order to investigate and understand its underlying neural activities and pathophysiology, EEG methodologies can be used. In this study, we estimated the nonlinearity features of EEG in MDD patients to assess the dynamical properties underlying the frontal and parietal brain activity. EEG data were obtained from 16 patients and 15 matched healthy controls. A wavelet-chaos methodology was used for data analysis. First, EEGs of subjects were decomposed into 5 EEG sub-bands by discrete wavelet transform. Then, both the Katz's and Higuchi's fractal dimensions (KFD and HFD) were calculated as complexity measures for full-band and sub-bands EEGs. Last, two-way analyses of variances were used to test EEG complexity differences on each fractality measures. As a result, a significantly increased complexity was found in both parietal and frontal regions of MDD patients. This significantly increased complexity was observed not only in full-band activity but also in beta and gamma sub-bands of EEG. The findings of the present study indicate the possibility of using the wavelet-chaos methodology to discriminate the EEGs of MDD patients from healthy controls.

Towards the utilization of EEG as a brain imaging tool.

PubMed

Michel, Christoph M; Murray, Micah M

2012-06-01

Recent advances in signal analysis have engendered EEG with the status of a true brain mapping and brain imaging method capable of providing spatio-temporal information regarding brain (dys)function. Because of the increasing interest in the temporal dynamics of brain networks, and because of the straightforward compatibility of the EEG with other brain imaging techniques, EEG is increasingly used in the neuroimaging community. However, the full capability of EEG is highly underestimated. Many combined EEG-fMRI studies use the EEG only as a spike-counter or an oscilloscope. Many cognitive and clinical EEG studies use the EEG still in its traditional way and analyze grapho-elements at certain electrodes and latencies. We here show that this way of using the EEG is not only dangerous because it leads to misinterpretations, but it is also largely ignoring the spatial aspects of the signals. In fact, EEG primarily measures the electric potential field at the scalp surface in the same way as MEG measures the magnetic field. By properly sampling and correctly analyzing this electric field, EEG can provide reliable information about the neuronal activity in the brain and the temporal dynamics of this activity in the millisecond range. This review explains some of these analysis methods and illustrates their potential in clinical and experimental applications. Copyright © 2011 Elsevier Inc. All rights reserved.

Mapping cortical haemodynamics during neonatal seizures using diffuse optical tomography: A case study

PubMed Central

Singh, Harsimrat; Cooper, Robert J.; Wai Lee, Chuen; Dempsey, Laura; Edwards, Andrea; Brigadoi, Sabrina; Airantzis, Dimitrios; Everdell, Nick; Michell, Andrew; Holder, David; Hebden, Jeremy C.; Austin, Topun

2014-01-01

Seizures in the newborn brain represent a major challenge to neonatal medicine. Neonatal seizures are poorly classified, under-diagnosed, difficult to treat and are associated with poor neurodevelopmental outcome. Video-EEG is the current gold-standard approach for seizure detection and monitoring. Interpreting neonatal EEG requires expertise and the impact of seizures on the developing brain remains poorly understood. In this case study we present the first ever images of the haemodynamic impact of seizures on the human infant brain, obtained using simultaneous diffuse optical tomography (DOT) and video-EEG with whole-scalp coverage. Seven discrete periods of ictal electrographic activity were observed during a 60 minute recording of an infant with hypoxic–ischaemic encephalopathy. The resulting DOT images show a remarkably consistent, high-amplitude, biphasic pattern of changes in cortical blood volume and oxygenation in response to each electrographic event. While there is spatial variation across the cortex, the dominant haemodynamic response to seizure activity consists of an initial increase in cortical blood volume prior to a large and extended decrease typically lasting several minutes. This case study demonstrates the wealth of physiologically and clinically relevant information that DOT–EEG techniques can yield. The consistency and scale of the haemodynamic responses observed here also suggest that DOT–EEG has the potential to provide improved detection of neonatal seizures. PMID:25161892

The role of blood vessels in high-resolution volume conductor head modeling of EEG.

PubMed

Fiederer, L D J; Vorwerk, J; Lucka, F; Dannhauer, M; Yang, S; Dümpelmann, M; Schulze-Bonhage, A; Aertsen, A; Speck, O; Wolters, C H; Ball, T

2016-03-01

Reconstruction of the electrical sources of human EEG activity at high spatio-temporal accuracy is an important aim in neuroscience and neurological diagnostics. Over the last decades, numerous studies have demonstrated that realistic modeling of head anatomy improves the accuracy of source reconstruction of EEG signals. For example, including a cerebro-spinal fluid compartment and the anisotropy of white matter electrical conductivity were both shown to significantly reduce modeling errors. Here, we for the first time quantify the role of detailed reconstructions of the cerebral blood vessels in volume conductor head modeling for EEG. To study the role of the highly arborized cerebral blood vessels, we created a submillimeter head model based on ultra-high-field-strength (7T) structural MRI datasets. Blood vessels (arteries and emissary/intraosseous veins) were segmented using Frangi multi-scale vesselness filtering. The final head model consisted of a geometry-adapted cubic mesh with over 17×10(6) nodes. We solved the forward model using a finite-element-method (FEM) transfer matrix approach, which allowed reducing computation times substantially and quantified the importance of the blood vessel compartment by computing forward and inverse errors resulting from ignoring the blood vessels. Our results show that ignoring emissary veins piercing the skull leads to focal localization errors of approx. 5 to 15mm. Large errors (>2cm) were observed due to the carotid arteries and the dense arterial vasculature in areas such as in the insula or in the medial temporal lobe. Thus, in such predisposed areas, errors caused by neglecting blood vessels can reach similar magnitudes as those previously reported for neglecting white matter anisotropy, the CSF or the dura - structures which are generally considered important components of realistic EEG head models. Our findings thus imply that including a realistic blood vessel compartment in EEG head models will be helpful to improve the accuracy of EEG source analyses particularly when high accuracies in brain areas with dense vasculature are required. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Human brain distinctiveness based on EEG spectral coherence connectivity.

PubMed

Rocca, D La; Campisi, P; Vegso, B; Cserti, P; Kozmann, G; Babiloni, F; Fallani, F De Vico

2014-09-01

The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of the current analyses rely on the extraction of features characterizing the activity of single brain regions, like power spectrum estimation, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherence-based connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N = 108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performance shows that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.5% is obtained in EC (96.26% in EO) when fusing power spectrum information from parieto-occipital (centro-parietal in EO) regions. Taken together, these results suggest that the functional connectivity patterns represent effective features for improving EEG-based biometric systems.

How do reference montage and electrodes setup affect the measured scalp EEG potentials?

NASA Astrophysics Data System (ADS)

Hu, Shiang; Lai, Yongxiu; Valdes-Sosa, Pedro A.; Bringas-Vega, Maria L.; Yao, Dezhong

2018-04-01

Objective. Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under debate. This study aims to investigate systematically how both reference montage and electrodes setup affect the accuracy of EEG potentials. Approach. First, the standard EEG potentials are generated by the forward calculation with a single dipole in the neural source space, for eleven channel numbers (10, 16, 21, 32, 64, 85, 96, 128, 129, 257, 335). Here, the reference is the ideal infinity implicitly determined by forward theory. Then, the standard EEG potentials are transformed to recordings with different references including five mono-polar references (Left earlobe, Fz, Pz, Oz, Cz), and three re-references (linked mastoids (LM), average reference (AR) and reference electrode standardization technique (REST)). Finally, the relative errors between the standard EEG potentials and the transformed ones are evaluated in terms of channel number, scalp regions, electrodes layout, dipole source position and orientation, as well as sensor noise and head model. Main results. Mono-polar reference recordings are usually of large distortions; thus, a re-reference after online mono-polar recording should be adopted in general to mitigate this effect. Among the three re-references, REST is generally superior to AR for all factors compared, and LM performs worst. REST is insensitive to head model perturbation. AR is subject to electrodes coverage and dipole orientation but no close relation with channel number. Significance. These results indicate that REST would be the first choice of re-reference and AR may be an alternative option for high level sensor noise case. Our findings may provide the helpful suggestions on how to obtain the EEG potentials as accurately as possible for cognitive neuroscientists and clinicians.

Online EEG artifact removal for BCI applications by adaptive spatial filtering.

PubMed

Guarnieri, Roberto; Marino, Marco; Barban, Federico; Ganzetti, Marco; Mantini, Dante

2018-06-28

The performance of brain computer interfaces (BCIs) based on electroencephalography (EEG) data strongly depends on the effective attenuation of artifacts that are mixed in the recordings. To address this problem, we have developed a novel online EEG artifact removal method for BCI applications, which combines blind source separation (BSS) and regression (REG) analysis. The BSS-REG method relies on the availability of a calibration dataset of limited duration for the initialization of a spatial filter using BSS. Online artifact removal is implemented by dynamically adjusting the spatial filter in the actual experiment, based on a linear regression technique. Our results showed that the BSS-REG method is capable of attenuating different kinds of artifacts, including ocular and muscular, while preserving true neural activity. Thanks to its low computational requirements, BSS-REG can be applied to low-density as well as high-density EEG data. We argue that BSS-REG may enable the development of novel BCI applications requiring high-density recordings, such as source-based neurofeedback and closed-loop neuromodulation. © 2018 IOP Publishing Ltd.

Modification of EEG power spectra and EEG connectivity in autobiographical memory: a sLORETA study.

PubMed

Imperatori, Claudio; Brunetti, Riccardo; Farina, Benedetto; Speranza, Anna Maria; Losurdo, Anna; Testani, Elisa; Contardi, Anna; Della Marca, Giacomo

2014-08-01

The aim of the present study was to explore the modifications of scalp EEG power spectra and EEG connectivity during the autobiographical memory test (AM-T) and during the retrieval of an autobiographical event (the high school final examination, Task 2). Seventeen healthy volunteers were enrolled (9 women and 8 men, mean age 23.4 ± 2.8 years, range 19-30). EEG was recorded at baseline and while performing the autobiographical memory (AM) tasks, by means of 19 surface electrodes and a nasopharyngeal electrode. EEG analysis was conducted by means of the standardized LOw Resolution Electric Tomography (sLORETA) software. Power spectra and lagged EEG coherence were compared between EEG acquired during the memory tasks and baseline recording. The frequency bands considered were as follows: delta (0.5-4 Hz); theta (4.5-7.5 Hz); alpha (8-12.5 Hz); beta1 (13-17.5 Hz); beta2 (18-30 Hz); gamma (30.5-60 Hz). During AM-T, we observed a significant delta power increase in left frontal and midline cortices (T = 3.554; p < 0.05) and increased EEG connectivity in delta band in prefrontal, temporal, parietal, and occipital areas, and for gamma bands in the left temporo-parietal regions (T = 4.154; p < 0.05). In Task 2, we measured an increased power in the gamma band located in the left posterior midline areas (T = 3.960; p < 0.05) and a significant increase in delta band connectivity in the prefrontal, temporal, parietal, and occipital areas, and in the gamma band involving right temporo-parietal areas (T = 4.579; p < 0.05). These results indicate that AM retrieval engages in a complex network which is mediated by both low- (delta) and high-frequency (gamma) EEG bands.

EEG - A Valuable Biomarker of Brain Injury in Preterm Infants.

PubMed

Pavlidis, Elena; Lloyd, Rhodri O; Boylan, Geraldine B

2017-01-01

This review focuses on the role of electroencephalography (EEG) in monitoring abnormalities of preterm brain function. EEG features of the most common developmental brain injuries in preterm infants, including intraventricular haemorrhage, periventricular leukomalacia, and perinatal asphyxia, are described. We outline the most common EEG biomarkers associated with these injuries, namely seizures, positive rolandic sharp waves, EEG suppression/increased interburst intervals, mechanical delta brush activity, and other deformed EEG waveforms, asymmetries, and asynchronies. The increasing survival rate of preterm infants, in particular those that are very and extremely preterm, has led to a growing demand for a specific and shared characterization of the patterns related to adverse outcome in this unique population. This review includes abundant high-quality images of the EEG patterns seen in premature infants and will provide a valuable resource for everyone working in developmental neuroscience. © 2017 S. Karger AG, Basel.

The scoring of arousal in sleep: reliability, validity, and alternatives.

PubMed

Bonnet, Michael H; Doghramji, Karl; Roehrs, Timothy; Stepanski, Edward J; Sheldon, Stephen H; Walters, Arthur S; Wise, Merrill; Chesson, Andrew L

2007-03-15

The reliability and validity of EEG arousals and other types of arousal are reviewed. Brief arousals during sleep had been observed for many years, but the evolution of sleep medicine in the 1980s directed new attention to these events. Early studies at that time in animals and humans linked brief EEG arousals and associated fragmentation of sleep to daytime sleepiness and degraded performance. Increasing interest in scoring of EEG arousals led the ASDA to publish a scoring manual in 1992. The current review summarizes numerous studies that have examined scoring reliability for these EEG arousals. Validity of EEG arousals was explored by review of studies that empirically varied arousals and found deficits similar to those found after total sleep deprivation depending upon the rate and extent of sleep fragmentation. Additional data from patients with clinical sleep disorders prior to and after effective treatment has also shown a continuing relationship between reduction in pathology-related arousals and improved sleep and daytime function. Finally, many suggestions have been made to refine arousal scoring to include additional elements (e.g., CAP), change the time frame, or focus on other physiological responses such as heart rate or blood pressure changes. Evidence to support the reliability and validity of these measures is presented. It was concluded that the scoring of EEG arousals has added much to our understanding of the sleep process but that significant work on the neurophysiology of arousal needs to be done. Additional refinement of arousal scoring will provide improved insight into sleep pathology and recovery.

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

PubMed

Sai, Chong Yeh; Mokhtar, Norrima; Arof, Hamzah; Cumming, Paul; Iwahashi, Masahiro

2018-05-01

Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pretrained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy, and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multichannel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

Prevention of Blast-Related Injuries

DTIC Science & Technology

2017-09-01

allow early screening and assessment of brain abnormality in soldiers to enable timely therapeutic intervention. The current study reports on the...use of qEEG in blast-induced brain injury using a swine model. The purposes are to determine if qEEG can detect brain activity abnormalities early...brain functional abnormalities and deficits in absence of any clinical mTBI symptoms. Methods such as EEG-wavelet entropy measures [36] and Shannon

fMRI activation patterns in an analytic reasoning task: consistency with EEG source localization

NASA Astrophysics Data System (ADS)

Li, Bian; Vasanta, Kalyana C.; O'Boyle, Michael; Baker, Mary C.; Nutter, Brian; Mitra, Sunanda

2010-03-01

Functional magnetic resonance imaging (fMRI) is used to model brain activation patterns associated with various perceptual and cognitive processes as reflected by the hemodynamic (BOLD) response. While many sensory and motor tasks are associated with relatively simple activation patterns in localized regions, higher-order cognitive tasks may produce activity in many different brain areas involving complex neural circuitry. We applied a recently proposed probabilistic independent component analysis technique (PICA) to determine the true dimensionality of the fMRI data and used EEG localization to identify the common activated patterns (mapped as Brodmann areas) associated with a complex cognitive task like analytic reasoning. Our preliminary study suggests that a hybrid GLM/PICA analysis may reveal additional regions of activation (beyond simple GLM) that are consistent with electroencephalography (EEG) source localization patterns.

Conductive polymer foam surface improves the performance of a capacitive EEG electrode.

PubMed

Baek, Hyun Jae; Lee, Hong Ji; Lim, Yong Gyu; Park, Kwang Suk

2012-12-01

In this paper, a new conductive polymer foam-surfaced electrode was proposed for use as a capacitive EEG electrode for nonintrusive EEG measurements in out-of-hospital environments. The current capacitive electrode has a rigid surface that produces an undefined contact area due to its stiffness, which renders it unable to conform to head curvature and locally isolates hairs between the electrode surface and scalp skin, making EEG measurement through hair difficult. In order to overcome this issue, a conductive polymer foam was applied to the capacitive electrode surface to provide a cushioning effect. This enabled EEG measurement through hair without any conductive contact with bare scalp skin. Experimental results showed that the new electrode provided lower electrode-skin impedance and higher voltage gains, signal-to-noise ratios, signal-to-error ratios, and correlation coefficients between EEGs measured by capacitive and conventional resistive methods compared to a conventional capacitive electrode. In addition, the new electrode could measure EEG signals, while the conventional capacitive electrode could not. We expect that the new electrode presented here can be easily installed in a hat or helmet to create a nonintrusive wearable EEG apparatus that does not make users look strange for real-world EEG applications.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion.

PubMed

Zafar, Raheel; Dass, Sarat C; Malik, Aamir Saeed

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.

Theta EEG source localization using LORETA in partial epilepsy patients with and without medication.

PubMed

Clemens, B; Bessenyei, M; Fekete, I; Puskás, S; Kondákor, I; Tóth, M; Hollódy, K

2010-06-01

To investigate and localize the sources of spontaneous, scalp-recorded theta activity in patients with partial epilepsy (PE). Nine patients with beginning, untreated PE (Group 1), 31 patients with already treated PE (Group 2), and 14 healthy persons were investigated by means of spectral analysis and LORETA, low resolution electromagnetic tomography (1 Hz very narrow band analysis, age-adjusted, Z-scored values). The frequency of main interest was 4-8 Hz. Group analysis: Group 1 displayed bilateral theta maxima in the temporal theta area (TTA), parietal theta area (PTA), and frontal theta area (FTA). In Group 2, theta activity increased all over the scalp as compared to the normative mean (Z=0) and also to Group 1. Maximum activity was found in the TTA, PTA, and FTA. However, in the PTA and FTA the centers of the abnormality shifted towards the medial cortex. Individual analysis: all the patients showed preferential activation (maximum Z-values) within one of the three theta areas. EEG activity in the theta band is increased in anatomically meaningful patterns in PE patients, which differs from the anatomical distribution of theta in healthy persons. The findings contribute to our understanding of the sources of theta rhythms and the pathophysiology of PE. Copyright 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Evaluation of driver fatigue on two channels of EEG data.

PubMed

Li, Wei; He, Qi-chang; Fan, Xiu-min; Fei, Zhi-min

2012-01-11

Electroencephalogram (EEG) data is an effective indicator to evaluate driver fatigue. The 16 channels of EEG data are collected and transformed into three bands (θ, α, and β) in the current paper. First, 12 types of energy parameters are computed based on the EEG data. Then, Grey Relational Analysis (GRA) is introduced to identify the optimal indicator of driver fatigue, after which, the number of significant electrodes is reduced using Kernel Principle Component Analysis (KPCA). Finally, the evaluation model for driver fatigue is established with the regression equation based on the EEG data from two significant electrodes (Fp1 and O1). The experimental results verify that the model is effective in evaluating driver fatigue. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Complexity analysis of brain activity in attention-deficit/hyperactivity disorder: A multiscale entropy analysis.

PubMed

Chenxi, Li; Chen, Yanni; Li, Youjun; Wang, Jue; Liu, Tian

2016-06-01

The multiscale entropy (MSE) is a novel method for quantifying the intrinsic dynamical complexity of physiological systems over several scales. To evaluate this method as a promising way to explore the neural mechanisms in ADHD, we calculated the MSE in EEG activity during the designed task. EEG data were collected from 13 outpatient boys with a confirmed diagnosis of ADHD and 13 age- and gender-matched normal control children during their doing multi-source interference task (MSIT). We estimated the MSE by calculating the sample entropy values of delta, theta, alpha and beta frequency bands over twenty time scales using coarse-grained procedure. The results showed increased complexity of EEG data in delta and theta frequency bands and decreased complexity in alpha frequency bands in ADHD children. The findings of this study revealed aberrant neural connectivity of kids with ADHD during interference task. The results showed that MSE method may be a new index to identify and understand the neural mechanism of ADHD. Copyright © 2016 Elsevier Inc. All rights reserved.

A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements

NASA Astrophysics Data System (ADS)

Kilicarslan, Atilla; Grossman, Robert G.; Contreras-Vidal, Jose Luis

2016-04-01

Objective. Non-invasive measurement of human neural activity based on the scalp electroencephalogram (EEG) allows for the development of biomedical devices that interface with the nervous system for scientific, diagnostic, therapeutic, or restorative purposes. However, EEG recordings are often considered as prone to physiological and non-physiological artifacts of different types and frequency characteristics. Among them, ocular artifacts and signal drifts represent major sources of EEG contamination, particularly in real-time closed-loop brain-machine interface (BMI) applications, which require effective handling of these artifacts across sessions and in natural settings. Approach. We extend the usage of a robust adaptive noise cancelling (ANC) scheme ({H}∞ filtering) for removal of eye blinks, eye motions, amplitude drifts and recording biases simultaneously. We also characterize the volume conduction, by estimating the signal propagation levels across all EEG scalp recording areas due to ocular artifact generators. We find that the amplitude and spatial distribution of ocular artifacts vary greatly depending on the electrode location. Therefore, fixed filtering parameters for all recording areas would naturally hinder the true overall performance of an ANC scheme for artifact removal. We treat each electrode as a separate sub-system to be filtered, and without the loss of generality, they are assumed to be uncorrelated and uncoupled. Main results. Our results show over 95-99.9% correlation between the raw and processed signals at non-ocular artifact regions, and depending on the contamination profile, 40-70% correlation when ocular artifacts are dominant. We also compare our results with the offline independent component analysis and artifact subspace reconstruction methods, and show that some local quantities are handled better by our sample-adaptive real-time framework. Decoding performance is also compared with multi-day experimental data from 2 subjects, totaling 19 sessions, with and without {H}∞ filtering of the raw data. Significance. The proposed method allows real-time adaptive artifact removal for EEG-based closed-loop BMI applications and mobile EEG studies in general, thereby increasing the range of tasks that can be studied in action and context while reducing the need for discarding data due to artifacts. Significant increase in decoding performances also justify the effectiveness of the method to be used in real-time closed-loop BMI applications.

Cyclic alternating pattern and interictal epileptiform discharges during morning sleep after sleep deprivation in temporal lobe epilepsy.

PubMed

Giorgi, Filippo Sean; Maestri, Michelangelo; Guida, Melania; Carnicelli, Luca; Caciagli, Lorenzo; Ferri, Raffaele; Bonuccelli, Ubaldo; Bonanni, Enrica

2017-08-01

Sleep deprivation (SD) increases the occurrence of interictal epileptiform discharges (IED) compared to basal EEG in temporal lobe epilepsy (TLE). In adults, EEG after SD is usually performed in the morning after SD. We aimed to evaluate whether morning sleep after SD bears additional IED-inducing effects compared with nocturnal physiological sleep, and whether changes in sleep stability (described by the cyclic alternating pattern-CAP) play a significant role. Adult patients with TLE underwent in-lab night polysomnography (n-PSG) and, within 7days from n-PSG, they underwent also a morning EEG after night SD (SD-EEG). We included only TLE patients in which both recordings showed IED. SD-EEG consisted of waking up patients at 2:00 AM and performing video EEG at 8:00 AM. For both recordings, we obtained the following markers for the first sleep cycle: IED/h (Spike Index, SI), sleep macrostructure, microstructure (NREM CAP rate; A1, A2 and A3 Indices), and SI association with CAP variables. The macrostructure of the first sleep cycle was similar in n-PSG and morning SD-EEG, whereas CAP rate and SI were significantly higher in SD-EEG. SI increase was selectively associated with CAP phases. SD increases the instability of morning recovery sleep compared with n-PSG, and particularly enhances CAP A1 phases, which are associated with the majority of IED. Thus, higher instability of morning recovery sleep may account at least in part for the increased IED yield in SD-EEG in TLE patients. Copyright © 2017 Elsevier Inc. All rights reserved.

Novel near infrared sensors for hybrid BCI applications

NASA Astrophysics Data System (ADS)

Almajidy, Rand K.; Le, Khang S.; Hofmann, Ulrich G.

2015-07-01

This study's goal is to develop a low cost, portable, accurate and comfortable NIRS module that can be used simultaneously with EEG in a dual modality system for brain computer interface (BCI). The sensing modules consist of electroencephalography (EEG) electrodes (at the positions Fp1, Fpz and Fp2 in the international 10-20 system) with eight custom made functional near infrared spectroscopy (fNIRS) channels, positioned on the prefrontal cortex area with two extra channels to measure and eliminate extra-cranial oxygenation. The NIRS sensors were designed to guarantee good sensor-skin contact, without causing subject discomfort, using springs to press them to the skin instead of pressing them by cap fixture. Two open source software packages were modified to carry out dual modality hybrid BCI experiments. The experimental paradigm consisted of a mental task (arithmetic task or text reading) and a resting period. Both oxygenated hemoglobin concentration changes (HbO), and EEG signals showed an increase during the mental task, but the onset, period and amount of that increase depends on each modality's characteristics. The subject's degree of attention played an important role especially during online sessions. The sensors can be easily used to acquire brain signals from different cerebral cortex parts. The system serves as a simple technological test bed and will be used for stroke patient rehabilitation purposes.

EEG-based usability assessment of 3D shutter glasses

NASA Astrophysics Data System (ADS)

Wenzel, Markus A.; Schultze-Kraft, Rafael; Meinecke, Frank C.; Cardinaux, Fabien; Kemp, Thomas; Müller, Klaus-Robert; Curio, Gabriel; Blankertz, Benjamin

2016-02-01

Objective. Neurotechnology can contribute to the usability assessment of products by providing objective measures of neural workload and can uncover usability impediments that are not consciously perceived by test persons. In this study, the neural processing effort imposed on the viewer of 3D television by shutter glasses was quantified as a function of shutter frequency. In particular, we sought to determine the critical shutter frequency at which the ‘neural flicker’ vanishes, such that visual fatigue due to this additional neural effort can be prevented by increasing the frequency of the system. Approach. Twenty-three participants viewed an image through 3D shutter glasses, while multichannel electroencephalogram (EEG) was recorded. In total ten shutter frequencies were employed, selected individually for each participant to cover the range below, at and above the threshold of flicker perception. The source of the neural flicker correlate was extracted using independent component analysis and the flicker impact on the visual cortex was quantified by decoding the state of the shutter from the EEG. Main Result. Effects of the shutter glasses were traced in the EEG up to around 67 Hz—about 20 Hz over the flicker perception threshold—and vanished at the subsequent frequency level of 77 Hz. Significance. The impact of the shutter glasses on the visual cortex can be detected by neurotechnology even when a flicker is not reported by the participants. Potential impact. Increasing the shutter frequency from the usual 50 Hz or 60 Hz to 77 Hz reduces the risk of visual fatigue and thus improves shutter-glass-based 3D usability.

EEG-based usability assessment of 3D shutter glasses.

PubMed

Wenzel, Markus A; Schultze-Kraft, Rafael; Meinecke, Frank C; Fabien Cardinaux; Kemp, Thomas; Klaus-Robert Müller; Gabriel Curio; Benjamin Blankertz

2016-02-01

Neurotechnology can contribute to the usability assessment of products by providing objective measures of neural workload and can uncover usability impediments that are not consciously perceived by test persons. In this study, the neural processing effort imposed on the viewer of 3D television by shutter glasses was quantified as a function of shutter frequency. In particular, we sought to determine the critical shutter frequency at which the 'neural flicker' vanishes, such that visual fatigue due to this additional neural effort can be prevented by increasing the frequency of the system. Twenty-three participants viewed an image through 3D shutter glasses, while multichannel electroencephalogram (EEG) was recorded. In total ten shutter frequencies were employed, selected individually for each participant to cover the range below, at and above the threshold of flicker perception. The source of the neural flicker correlate was extracted using independent component analysis and the flicker impact on the visual cortex was quantified by decoding the state of the shutter from the EEG. Effects of the shutter glasses were traced in the EEG up to around 67 Hz-about 20 Hz over the flicker perception threshold-and vanished at the subsequent frequency level of 77 Hz. The impact of the shutter glasses on the visual cortex can be detected by neurotechnology even when a flicker is not reported by the participants. Potential impact. Increasing the shutter frequency from the usual 50 Hz or 60 Hz to 77 Hz reduces the risk of visual fatigue and thus improves shutter-glass-based 3D usability.

A three domain covariance framework for EEG/MEG data.

PubMed

Roś, Beata P; Bijma, Fetsje; de Gunst, Mathisca C M; de Munck, Jan C

2015-10-01

In this paper we introduce a covariance framework for the analysis of single subject EEG and MEG data that takes into account observed temporal stationarity on small time scales and trial-to-trial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three components that correspond to space, time and epochs/trials, and consider maximum likelihood estimation of the unknown parameter values. An iterative algorithm that finds approximations of the maximum likelihood estimates is proposed. Our covariance model is applicable in a variety of cases where spontaneous EEG or MEG acts as source of noise and realistic noise covariance estimates are needed, such as in evoked activity studies, or where the properties of spontaneous EEG or MEG are themselves the topic of interest, like in combined EEG-fMRI experiments in which the correlation between EEG and fMRI signals is investigated. We use a simulation study to assess the performance of the estimator and investigate the influence of different assumptions about the covariance factors on the estimated covariance matrix and on its components. We apply our method to real EEG and MEG data sets. Copyright © 2015 Elsevier Inc. All rights reserved.

Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics.

PubMed

Chen, Xun; Liu, Aiping; Chen, Qiang; Liu, Yu; Zou, Liang; McKeown, Martin J

2017-09-01

Electroencephalography (EEG) recordings are frequently contaminated by both ocular and muscle artifacts. These are normally dealt with separately, by employing blind source separation (BSS) techniques relying on either second-order or higher-order statistics (SOS & HOS respectively). When HOS-based methods are used, it is usually in the setting of assuming artifacts are statistically independent to the EEG. When SOS-based methods are used, it is assumed that artifacts have autocorrelation characteristics distinct from the EEG. In reality, ocular and muscle artifacts do not completely follow the assumptions of strict temporal independence to the EEG nor completely unique autocorrelation characteristics, suggesting that exploiting HOS or SOS alone may be insufficient to remove these artifacts. Here we employ a novel BSS technique, independent vector analysis (IVA), to jointly employ HOS and SOS simultaneously to remove ocular and muscle artifacts. Numerical simulations and application to real EEG recordings were used to explore the utility of the IVA approach. IVA was superior in isolating both ocular and muscle artifacts, especially for raw EEG data with low signal-to-noise ratio, and also integrated usually separate SOS and HOS steps into a single unified step. Copyright © 2017 Elsevier Ltd. All rights reserved.

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

NASA Astrophysics Data System (ADS)

Mishra, Puneet; Singla, Sunil Kumar

2013-01-01

In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.

Evidence of Neurotoxicity of Ecstasy: Sustained Effects on Electroencephalographic Activity in Polydrug Users

PubMed Central

Adamaszek, Michael; Khaw, Alexander V.; Buck, Ulrike; Andresen, Burghard; Thomasius, Rainer

2010-01-01

Objective According to previous EEG reports of indicative disturbances in Alpha and Beta activities, a systematic search for distinct EEG abnormalities in a broader population of Ecstasy users may especially corroborate the presumed specific neurotoxicity of Ecstasy in humans. Methods 105 poly-drug consumers with former Ecstasy use and 41 persons with comparable drug history without Ecstasy use, and 11 drug naives were investigated for EEG features. Conventional EEG derivations of 19 electrodes according to the 10-20-system were conducted. Besides standard EEG bands, quantitative EEG analyses of 1-Hz-subdivided power ranges of Alpha, Theta and Beta bands have been considered. Results Ecstasy users with medium and high cumulative Ecstasy doses revealed an increase in Theta and lower Alpha activities, significant increases in Beta activities, and a reduction of background activity. Ecstasy users with low cumulative Ecstasy doses showed a significant Alpha activity at 11 Hz. Interestingly, the spectral power of low frequencies in medium and high Ecstasy users was already significantly increased in the early phase of EEG recording. Statistical analyses suggested the main effect of Ecstasy to EEG results. Conclusions Our data from a major sample of Ecstasy users support previous data revealing alterations of EEG frequency spectrum due rather to neurotoxic effects of Ecstasy on serotonergic systems in more detail. Accordingly, our data may be in line with the observation of attentional and memory impairments in Ecstasy users with moderate to high misuse. Despite the methodological problem of polydrug use also in our approach, our EEG results may be indicative of the neuropathophysiological background of the reported memory and attentional deficits in Ecstasy abusers. Overall, our findings may suggest the usefulness of EEG in diagnostic approaches in assessing neurotoxic sequela of this common drug abuse. PMID:21124854

Rewarming affects EEG background in term newborns with hypoxic-ischemic encephalopathy undergoing therapeutic hypothermia.

PubMed

Birca, Ala; Lortie, Anne; Birca, Veronica; Decarie, Jean-Claude; Veilleux, Annie; Gallagher, Anne; Dehaes, Mathieu; Lodygensky, Gregory A; Carmant, Lionel

2016-04-01

To investigate how rewarming impacts the evolution of EEG background in neonates with hypoxic-ischemic encephalopathy (HIE) undergoing therapeutic hypothermia (TH). We recruited a retrospective cohort of 15 consecutive newborns with moderate (9) and severe (6) HIE monitored with a continuous EEG during TH and at least 12h after its end. EEG background was analyzed using conventional visual and quantitative EEG analysis methods including EEG discontinuity, absolute and relative spectral magnitudes. One patient with seizures on rewarming was excluded from analyses. Visual and quantitative analyses demonstrated significant changes in EEG background from pre- to post-rewarming, characterized by an increased EEG discontinuity, more pronounced in newborns with severe compared to moderate HIE. Neonates with moderate HIE also had an increase in the relative magnitude of slower delta and a decrease in higher frequency theta and alpha waves with rewarming. Rewarming affects EEG background in HIE newborns undergoing TH, which may represent a transient adaptive response or reflect an evolving brain injury. EEG background impairment induced by rewarming may represent a biomarker of evolving encephalopathy in HIE newborns undergoing TH and underscores the importance of continuously monitoring the brain health in critically ill neonates. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models

PubMed Central

Paz-Linares, Deirel; Vega-Hernández, Mayrim; Rojas-López, Pedro A.; Valdés-Hernández, Pedro A.; Martínez-Montes, Eduardo; Valdés-Sosa, Pedro A.

2017-01-01

The estimation of EEG generating sources constitutes an Inverse Problem (IP) in Neuroscience. This is an ill-posed problem due to the non-uniqueness of the solution and regularization or prior information is needed to undertake Electrophysiology Source Imaging. Structured Sparsity priors can be attained through combinations of (L1 norm-based) and (L2 norm-based) constraints such as the Elastic Net (ENET) and Elitist Lasso (ELASSO) models. The former model is used to find solutions with a small number of smooth nonzero patches, while the latter imposes different degrees of sparsity simultaneously along different dimensions of the spatio-temporal matrix solutions. Both models have been addressed within the penalized regression approach, where the regularization parameters are selected heuristically, leading usually to non-optimal and computationally expensive solutions. The existing Bayesian formulation of ENET allows hyperparameter learning, but using the computationally intensive Monte Carlo/Expectation Maximization methods, which makes impractical its application to the EEG IP. While the ELASSO have not been considered before into the Bayesian context. In this work, we attempt to solve the EEG IP using a Bayesian framework for ENET and ELASSO models. We propose a Structured Sparse Bayesian Learning algorithm based on combining the Empirical Bayes and the iterative coordinate descent procedures to estimate both the parameters and hyperparameters. Using realistic simulations and avoiding the inverse crime we illustrate that our methods are able to recover complicated source setups more accurately and with a more robust estimation of the hyperparameters and behavior under different sparsity scenarios than classical LORETA, ENET and LASSO Fusion solutions. We also solve the EEG IP using data from a visual attention experiment, finding more interpretable neurophysiological patterns with our methods. The Matlab codes used in this work, including Simulations, Methods, Quality Measures and Visualization Routines are freely available in a public website. PMID:29200994

Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models.

PubMed

Paz-Linares, Deirel; Vega-Hernández, Mayrim; Rojas-López, Pedro A; Valdés-Hernández, Pedro A; Martínez-Montes, Eduardo; Valdés-Sosa, Pedro A

2017-01-01

The estimation of EEG generating sources constitutes an Inverse Problem (IP) in Neuroscience. This is an ill-posed problem due to the non-uniqueness of the solution and regularization or prior information is needed to undertake Electrophysiology Source Imaging. Structured Sparsity priors can be attained through combinations of (L1 norm-based) and (L2 norm-based) constraints such as the Elastic Net (ENET) and Elitist Lasso (ELASSO) models. The former model is used to find solutions with a small number of smooth nonzero patches, while the latter imposes different degrees of sparsity simultaneously along different dimensions of the spatio-temporal matrix solutions. Both models have been addressed within the penalized regression approach, where the regularization parameters are selected heuristically, leading usually to non-optimal and computationally expensive solutions. The existing Bayesian formulation of ENET allows hyperparameter learning, but using the computationally intensive Monte Carlo/Expectation Maximization methods, which makes impractical its application to the EEG IP. While the ELASSO have not been considered before into the Bayesian context. In this work, we attempt to solve the EEG IP using a Bayesian framework for ENET and ELASSO models. We propose a Structured Sparse Bayesian Learning algorithm based on combining the Empirical Bayes and the iterative coordinate descent procedures to estimate both the parameters and hyperparameters. Using realistic simulations and avoiding the inverse crime we illustrate that our methods are able to recover complicated source setups more accurately and with a more robust estimation of the hyperparameters and behavior under different sparsity scenarios than classical LORETA, ENET and LASSO Fusion solutions. We also solve the EEG IP using data from a visual attention experiment, finding more interpretable neurophysiological patterns with our methods. The Matlab codes used in this work, including Simulations, Methods, Quality Measures and Visualization Routines are freely available in a public website.

Feasibility of imaging epileptic seizure onset with EIT and depth electrodes.

PubMed

Witkowska-Wrobel, Anna; Aristovich, Kirill; Faulkner, Mayo; Avery, James; Holder, David

2018-06-01

Imaging ictal and interictal activity with Electrical Impedance Tomography (EIT) using intracranial electrode mats has been demonstrated in animal models of epilepsy. In human epilepsy subjects undergoing presurgical evaluation, depth electrodes are often preferred. The purpose of this work was to evaluate the feasibility of using EIT to localise epileptogenic areas with intracranial electrodes in humans. The accuracy of localisation of the ictal onset zone was evaluated in computer simulations using 9M element FEM models derived from three subjects. 5 mm radius perturbations imitating a single seizure onset event were placed in several locations forming two groups: under depth electrode coverage and in the contralateral hemisphere. Simulations were made for impedance changes of 1% expected for neuronal depolarisation over milliseconds and 10% for cell swelling over seconds. Reconstructions were compared with EEG source modelling for a radially orientated dipole with respect to the closest EEG recording contact. The best accuracy of EIT was obtained using all depth and 32 scalp electrodes, greater than the equivalent accuracy with EEG inverse source modelling. The localisation error was 5.2 ± 1.8, 4.3 ± 0 and 46.2 ± 25.8 mm for perturbations within the volume enclosed by depth electrodes and 29.6 ± 38.7, 26.1 ± 36.2, 54.0 ± 26.2 mm for those without (EIT 1%, 10% change, EEG source modelling, n = 15 in 3 subjects, p < 0.01). As EIT was insensitive to source dipole orientation, all 15 perturbations within the volume enclosed by depth electrodes were localised, whereas the standard clinical method of visual inspection of EEG voltages, only localised 8 out of 15 cases. This suggests that adding EIT to SEEG measurements could be beneficial in localising the onset of seizures. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Linear distributed source modeling of local field potentials recorded with intra-cortical electrode arrays.

PubMed

Hindriks, Rikkert; Schmiedt, Joscha; Arsiwalla, Xerxes D; Peter, Alina; Verschure, Paul F M J; Fries, Pascal; Schmid, Michael C; Deco, Gustavo

2017-01-01

Planar intra-cortical electrode (Utah) arrays provide a unique window into the spatial organization of cortical activity. Reconstruction of the current source density (CSD) underlying such recordings, however, requires "inverting" Poisson's equation. For inter-laminar recordings, this is commonly done by the CSD method, which consists in taking the second-order spatial derivative of the recorded local field potentials (LFPs). Although the CSD method has been tremendously successful in mapping the current generators underlying inter-laminar LFPs, its application to planar recordings is more challenging. While for inter-laminar recordings the CSD method seems reasonably robust against violations of its assumptions, is it unclear as to what extent this holds for planar recordings. One of the objectives of this study is to characterize the conditions under which the CSD method can be successfully applied to Utah array data. Using forward modeling, we find that for spatially coherent CSDs, the CSD method yields inaccurate reconstructions due to volume-conducted contamination from currents in deeper cortical layers. An alternative approach is to "invert" a constructed forward model. The advantage of this approach is that any a priori knowledge about the geometrical and electrical properties of the tissue can be taken into account. Although several inverse methods have been proposed for LFP data, the applicability of existing electroencephalographic (EEG) and magnetoencephalographic (MEG) inverse methods to LFP data is largely unexplored. Another objective of our study therefore, is to assess the applicability of the most commonly used EEG/MEG inverse methods to Utah array data. Our main conclusion is that these inverse methods provide more accurate CSD reconstructions than the CSD method. We illustrate the inverse methods using event-related potentials recorded from primary visual cortex of a macaque monkey during a motion discrimination task.

Linear distributed source modeling of local field potentials recorded with intra-cortical electrode arrays

PubMed Central

Schmiedt, Joscha; Arsiwalla, Xerxes D.; Peter, Alina; Verschure, Paul F. M. J.; Fries, Pascal; Schmid, Michael C.; Deco, Gustavo

2017-01-01

Planar intra-cortical electrode (Utah) arrays provide a unique window into the spatial organization of cortical activity. Reconstruction of the current source density (CSD) underlying such recordings, however, requires “inverting” Poisson’s equation. For inter-laminar recordings, this is commonly done by the CSD method, which consists in taking the second-order spatial derivative of the recorded local field potentials (LFPs). Although the CSD method has been tremendously successful in mapping the current generators underlying inter-laminar LFPs, its application to planar recordings is more challenging. While for inter-laminar recordings the CSD method seems reasonably robust against violations of its assumptions, is it unclear as to what extent this holds for planar recordings. One of the objectives of this study is to characterize the conditions under which the CSD method can be successfully applied to Utah array data. Using forward modeling, we find that for spatially coherent CSDs, the CSD method yields inaccurate reconstructions due to volume-conducted contamination from currents in deeper cortical layers. An alternative approach is to “invert” a constructed forward model. The advantage of this approach is that any a priori knowledge about the geometrical and electrical properties of the tissue can be taken into account. Although several inverse methods have been proposed for LFP data, the applicability of existing electroencephalographic (EEG) and magnetoencephalographic (MEG) inverse methods to LFP data is largely unexplored. Another objective of our study therefore, is to assess the applicability of the most commonly used EEG/MEG inverse methods to Utah array data. Our main conclusion is that these inverse methods provide more accurate CSD reconstructions than the CSD method. We illustrate the inverse methods using event-related potentials recorded from primary visual cortex of a macaque monkey during a motion discrimination task. PMID:29253006

Localization of synchronous cortical neural sources.

PubMed

Zerouali, Younes; Herry, Christophe L; Jemel, Boutheina; Lina, Jean-Marc

2013-03-01

Neural synchronization is a key mechanism to a wide variety of brain functions, such as cognition, perception, or memory. High temporal resolution achieved by EEG recordings allows the study of the dynamical properties of synchronous patterns of activity at a very fine temporal scale but with very low spatial resolution. Spatial resolution can be improved by retrieving the neural sources of EEG signal, thus solving the so-called inverse problem. Although many methods have been proposed to solve the inverse problem and localize brain activity, few of them target the synchronous brain regions. In this paper, we propose a novel algorithm aimed at localizing specifically synchronous brain regions and reconstructing the time course of their activity. Using multivariate wavelet ridge analysis, we extract signals capturing the synchronous events buried in the EEG and then solve the inverse problem on these signals. Using simulated data, we compare results of source reconstruction accuracy achieved by our method to a standard source reconstruction approach. We show that the proposed method performs better across a wide range of noise levels and source configurations. In addition, we applied our method on real dataset and identified successfully cortical areas involved in the functional network underlying visual face perception. We conclude that the proposed approach allows an accurate localization of synchronous brain regions and a robust estimation of their activity.

Influence of Intracranial Electrode Density and Spatial Configuration on Interictal Spike Localization: A Case Study.

PubMed

Lie, Octavian V; Papanastassiou, Alexander M; Cavazos, José E; Szabó, Ákos C

2015-10-01

Poor seizure outcomes after epilepsy surgery often reflect an incorrect localization of the epileptic sources by standard intracranial EEG interpretation because of limited electrode coverage of the epileptogenic zone. This study investigates whether, in such conditions, source modeling is able to provide more accurate source localization than the standard clinical method that can be used prospectively to improve surgical resection planning. Suboptimal epileptogenic zone sampling is simulated by subsets of the electrode configuration used to record intracranial EEG in a patient rendered seizure free after surgery. sLORETA and the clinical method solutions are applied to interictal spikes sampled with these electrode subsets and are compared for colocalization with the resection volume and displacement due to electrode downsampling. sLORETA provides often congruent and at times more accurate source localization when compared with the standard clinical method. However, with electrode downsampling, individual sLORETA solution locations can vary considerably and shift consistently toward the remaining electrodes. sLORETA application can improve source localization based on the clinical method but does not reliably compensate for suboptimal electrode placement. Incorporating sLORETA solutions based on intracranial EEG in surgical planning should proceed cautiously in cases where electrode repositioning is planned on clinical grounds.

Family nurture intervention in preterm infants increases early development of cortical activity and independence of regional power trajectories.

PubMed

Welch, Martha G; Stark, Raymond I; Grieve, Philip G; Ludwig, Robert J; Isler, Joseph R; Barone, Joseph L; Myers, Michael M

2017-12-01

Premature delivery and maternal separation during hospitalisation increase infant neurodevelopmental risk. Previously, a randomised controlled trial of Family Nurture Intervention (FNI) in the neonatal intensive care unit demonstrated improvement across multiple mother and infant domains including increased electroencephalographic (EEG) power in the frontal polar region at term age. New aims were to quantify developmental changes in EEG power in all brain regions and frequencies and correlate developmental changes in EEG power among regions. EEG (128 electrodes) was obtained at 34-44 weeks postmenstrual age from preterm infants born 26-34 weeks. Forty-four infants were treated with Standard Care and 53 with FNI. EEG power was computed in 10 frequency bands (1-48 Hz) in 10 brain regions and in active and quiet sleep. Percent change/week in EEG power was increased in FNI in 132/200 tests (p < 0.05), 117 tests passed a 5% False Discovery Rate threshold. In addition, FNI demonstrated greater regional independence in those developmental rates of change. This study strengthens the conclusion that FNI promotes cerebral cortical development of preterm infants. The findings indicate that developmental changes in EEG may provide biomarkers for risk in preterm infants as well as proximal markers of effects of FNI. ©2017 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

[Individual Types Reactivity of EEG Oscillations in Effective Heart Rhythm Biofeedback Parameters in Adolescents and Young People in the North].

PubMed

Krivonogova, E V; Poskotinova, L V; Demin, D B

2015-01-01

A single session of heart rate variability (HRV) biofeedback in apparently healthy young people and adolescents aged 14-17 years in order to increase vagal effects on heart rhythm and also electroencephalograms were carried out. Different variants of EEG spectral power during the successful HRV biofeedback session were identified. In the case of I variant of EEG activity the increase of power spectrum of alpha-, betal-, theta-components takes place in all parts of the brain. In the case of II variant of EEG activity the reduction of power spectrum of alpha-, betal-, theta-activity in all parts of the brain was observed. I and II variants of EEG activity cause more intensive regime of cortical-subcortical interactions. During the III variant of EEG activity the successful biofeedback is accompanied by increase of alpha activity in the central, front and anteriofrontal brain parts and so indicates the formation of thalamocortical relations of neural network in order to optimize the vegetal regulation of heart function. There was an increase in alpha- and beta1-activity in the parietal, central, frontal and temporal brain parts during the IV variant of EEG activity and so that it provides the relief of neural networks communication for information processing. As a result of V variance of EEG activity there was the increase of power spectrum of theta activity in the central and frontal parts of both cerebral hemispheres, so it was associated with the cortical-hippocampal interactions to achieve a successful biofeedback.

EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks.

PubMed

Berka, Chris; Levendowski, Daniel J; Lumicao, Michelle N; Yau, Alan; Davis, Gene; Zivkovic, Vladimir T; Olmstead, Richard E; Tremoulet, Patrice D; Craven, Patrick L

2007-05-01

The ability to continuously and unobtrusively monitor levels of task engagement and mental workload in an operational environment could be useful in identifying more accurate and efficient methods for humans to interact with technology. This information could also be used to optimize the design of safer, more efficient work environments that increase motivation and productivity. The present study explored the feasibility of monitoring electroencephalo-graphic (EEG) indices of engagement and workload acquired unobtrusively and quantified during performance of cognitive tests. EEG was acquired from 80 healthy participants with a wireless sensor headset (F3-F4,C3-C4,Cz-POz,F3-Cz,Fz-C3,Fz-POz) during tasks including: multi-level forward/backward-digit-span, grid-recall, trails, mental-addition, 20-min 3-Choice Vigilance, and image-learning and memory tests. EEG metrics for engagement and workload were calculated for each 1 -s of EEG. Across participants, engagement but not workload decreased over the 20-min vigilance test. Engagement and workload were significantly increased during the encoding period of verbal and image-learning and memory tests when compared with the recognition/ recall period. Workload but not engagement increased linearly as level of difficulty increased in forward and backward-digit-span, grid-recall, and mental-addition tests. EEG measures correlated with both subjective and objective performance metrics. These data in combination with previous studies suggest that EEG engagement reflects information-gathering, visual processing, and allocation of attention. EEG workload increases with increasing working memory load and during problem solving, integration of information, analytical reasoning, and may be more reflective of executive functions. Inspection of EEG on a second-by-second timescale revealed associations between workload and engagement levels when aligned with specific task events providing preliminary evidence that second-by-second classifications reflect parameters of task performance.

Brain electric correlates of strong belief in paranormal phenomena: intracerebral EEG source and regional Omega complexity analyses.

PubMed

Pizzagalli, D; Lehmann, D; Gianotti, L; Koenig, T; Tanaka, H; Wackermann, J; Brugger, P

2000-12-22

The neurocognitive processes underlying the formation and maintenance of paranormal beliefs are important for understanding schizotypal ideation. Behavioral studies indicated that both schizotypal and paranormal ideation are based on an overreliance on the right hemisphere, whose coarse rather than focussed semantic processing may favor the emergence of 'loose' and 'uncommon' associations. To elucidate the electrophysiological basis of these behavioral observations, 35-channel resting EEG was recorded in pre-screened female strong believers and disbelievers during resting baseline. EEG data were subjected to FFT-Dipole-Approximation analysis, a reference-free frequency-domain dipole source modeling, and Regional (hemispheric) Omega Complexity analysis, a linear approach estimating the complexity of the trajectories of momentary EEG map series in state space. Compared to disbelievers, believers showed: more right-located sources of the beta2 band (18.5-21 Hz, excitatory activity); reduced interhemispheric differences in Omega complexity values; higher scores on the Magical Ideation scale; more general negative affect; and more hypnagogic-like reveries after a 4-min eyes-closed resting period. Thus, subjects differing in their declared paranormal belief displayed different active, cerebral neural populations during resting, task-free conditions. As hypothesized, believers showed relatively higher right hemispheric activation and reduced hemispheric asymmetry of functional complexity. These markers may constitute the neurophysiological basis for paranormal and schizotypal ideation.

Routine vs extended outpatient EEG for the detection of interictal epileptiform discharges

PubMed Central

Britton, Jeffrey W.; Rajasekaran, Vijayalakshmi; Fabris, Rachel R.; Cherian, Perumpillichira J.; Kelly-Williams, Kristen M.; So, Elson L.; Nickels, Katherine C.; Wong-Kisiel, Lily C.; Lagerlund, Terrence D.; Cascino, Gregory D.; Worrell, Gregory A.; Wirrell, Elaine C.

2016-01-01

Objective: To compare the yield of epileptiform abnormalities on 30-minute recordings with those greater than 45 minutes. Methods: We performed a prospective observational cross-sectional study of all outpatient routine EEGs comparing the rate of interictal epileptiform discharges (IEDs) and clinical events during the initial 30 minutes (routine) with those occurring in the remaining 30–60 minutes (extended). A relative increase of 10% was considered clinically significant. Results: EEGs from 1,803 patients were included; overall EEG duration was 59.4 minutes (SD ±6.5). Of 426 patients with IEDs at any time during the EEG, 81 (19.1%, 95% confidence interval 15.6–23) occurred only after the initial 30 minutes. The rate of late IEDs was not associated with age, indication, IED type, or sleep deprivation. Longer recording times also increased event capture rate by approximately 30%. Conclusions: The yield of IED and event detection is increased in extended outpatient EEGs compared to 30-minute studies. PMID:26984946

Routine vs extended outpatient EEG for the detection of interictal epileptiform discharges.

PubMed

Burkholder, David B; Britton, Jeffrey W; Rajasekaran, Vijayalakshmi; Fabris, Rachel R; Cherian, Perumpillichira J; Kelly-Williams, Kristen M; So, Elson L; Nickels, Katherine C; Wong-Kisiel, Lily C; Lagerlund, Terrence D; Cascino, Gregory D; Worrell, Gregory A; Wirrell, Elaine C

2016-04-19

To compare the yield of epileptiform abnormalities on 30-minute recordings with those greater than 45 minutes. We performed a prospective observational cross-sectional study of all outpatient routine EEGs comparing the rate of interictal epileptiform discharges (IEDs) and clinical events during the initial 30 minutes (routine) with those occurring in the remaining 30-60 minutes (extended). A relative increase of 10% was considered clinically significant. EEGs from 1,803 patients were included; overall EEG duration was 59.4 minutes (SD ±6.5). Of 426 patients with IEDs at any time during the EEG, 81 (19.1%, 95% confidence interval 15.6-23) occurred only after the initial 30 minutes. The rate of late IEDs was not associated with age, indication, IED type, or sleep deprivation. Longer recording times also increased event capture rate by approximately 30%. The yield of IED and event detection is increased in extended outpatient EEGs compared to 30-minute studies. © 2016 American Academy of Neurology.

Transcranial Electrical Neuromodulation Based on the Reciprocity Principle

PubMed Central

Fernández-Corazza, Mariano; Turovets, Sergei; Luu, Phan; Anderson, Erik; Tucker, Don

2016-01-01

A key challenge in multi-electrode transcranial electrical stimulation (TES) or transcranial direct current stimulation (tDCS) is to find a current injection pattern that delivers the necessary current density at a target and minimizes it in the rest of the head, which is mathematically modeled as an optimization problem. Such an optimization with the Least Squares (LS) or Linearly Constrained Minimum Variance (LCMV) algorithms is generally computationally expensive and requires multiple independent current sources. Based on the reciprocity principle in electroencephalography (EEG) and TES, it could be possible to find the optimal TES patterns quickly whenever the solution of the forward EEG problem is available for a brain region of interest. Here, we investigate the reciprocity principle as a guideline for finding optimal current injection patterns in TES that comply with safety constraints. We define four different trial cortical targets in a detailed seven-tissue finite element head model, and analyze the performance of the reciprocity family of TES methods in terms of electrode density, targeting error, focality, intensity, and directionality using the LS and LCMV solutions as the reference standards. It is found that the reciprocity algorithms show good performance comparable to the LCMV and LS solutions. Comparing the 128 and 256 electrode cases, we found that use of greater electrode density improves focality, directionality, and intensity parameters. The results show that reciprocity principle can be used to quickly determine optimal current injection patterns in TES and help to simplify TES protocols that are consistent with hardware and software availability and with safety constraints. PMID:27303311

Transcranial Electrical Neuromodulation Based on the Reciprocity Principle.

PubMed

Fernández-Corazza, Mariano; Turovets, Sergei; Luu, Phan; Anderson, Erik; Tucker, Don

2016-01-01

A key challenge in multi-electrode transcranial electrical stimulation (TES) or transcranial direct current stimulation (tDCS) is to find a current injection pattern that delivers the necessary current density at a target and minimizes it in the rest of the head, which is mathematically modeled as an optimization problem. Such an optimization with the Least Squares (LS) or Linearly Constrained Minimum Variance (LCMV) algorithms is generally computationally expensive and requires multiple independent current sources. Based on the reciprocity principle in electroencephalography (EEG) and TES, it could be possible to find the optimal TES patterns quickly whenever the solution of the forward EEG problem is available for a brain region of interest. Here, we investigate the reciprocity principle as a guideline for finding optimal current injection patterns in TES that comply with safety constraints. We define four different trial cortical targets in a detailed seven-tissue finite element head model, and analyze the performance of the reciprocity family of TES methods in terms of electrode density, targeting error, focality, intensity, and directionality using the LS and LCMV solutions as the reference standards. It is found that the reciprocity algorithms show good performance comparable to the LCMV and LS solutions. Comparing the 128 and 256 electrode cases, we found that use of greater electrode density improves focality, directionality, and intensity parameters. The results show that reciprocity principle can be used to quickly determine optimal current injection patterns in TES and help to simplify TES protocols that are consistent with hardware and software availability and with safety constraints.

Electroencephalography and analgesics.

PubMed

Malver, Lasse Paludan; Brokjaer, Anne; Staahl, Camilla; Graversen, Carina; Andresen, Trine; Drewes, Asbjørn Mohr

2014-01-01

To assess centrally mediated analgesic mechanisms in clinical trials with pain patients, objective standardized methods such as electroencephalography (EEG) has many advantages. The aim of this review is to provide the reader with an overview of present findings in analgesics assessed with spontaneous EEG and evoked brain potentials (EPs) in humans. Furthermore, EEG methodologies will be discussed with respect to translation from animals to humans and future perspectives in predicting analgesic efficacy. We searched PubMed with MeSH terms 'analgesics', 'electroencephalography' and 'evoked potentials' for relevant articles. Combined with a search in their reference lists 15 articles on spontaneous EEG and 55 papers on EPs were identified. Overall, opioids produced increased activity in the delta band in the spontaneous EEG, but increases in higher frequency bands were also seen. The EP amplitudes decreased in the majority of studies. Anticonvulsants used as analgesics showed inconsistent results. The N-methyl-D-aspartate receptor antagonist ketamine showed an increase in the theta band in spontaneous EEG and decreases in EP amplitudes. Tricyclic antidepressants increased the activity in the delta, theta and beta bands in the spontaneous EEG while EPs were inconsistently affected. Weak analgesics were mainly investigated with EPs and a decrease in amplitudes was generally observed. This review reveals that both spontaneous EEG and EPs are widely used as biomarkers for analgesic drug effects. Methodological differences are common and a more uniform approach will further enhance the value of such biomarkers for drug development and prediction of treatment response in individual patients. © 2013 The British Pharmacological Society.

Electroencephalography and analgesics

PubMed Central

Malver, Lasse Paludan; Brokjær, Anne; Staahl, Camilla; Graversen, Carina; Andresen, Trine; Drewes, Asbjørn Mohr

2014-01-01

To assess centrally mediated analgesic mechanisms in clinical trials with pain patients, objective standardized methods such as electroencephalography (EEG) has many advantages. The aim of this review is to provide the reader with an overview of present findings in analgesics assessed with spontaneous EEG and evoked brain potentials (EPs) in humans. Furthermore, EEG methodologies will be discussed with respect to translation from animals to humans and future perspectives in predicting analgesic efficacy. We searched PubMed with MeSH terms ‘analgesics’, ‘electroencephalography’ and ‘evoked potentials’ for relevant articles. Combined with a search in their reference lists 15 articles on spontaneous EEG and 55 papers on EPs were identified. Overall, opioids produced increased activity in the delta band in the spontaneous EEG, but increases in higher frequency bands were also seen. The EP amplitudes decreased in the majority of studies. Anticonvulsants used as analgesics showed inconsistent results. The N-methyl-D-aspartate receptor antagonist ketamine showed an increase in the theta band in spontaneous EEG and decreases in EP amplitudes. Tricyclic antidepressants increased the activity in the delta, theta and beta bands in the spontaneous EEG while EPs were inconsistently affected. Weak analgesics were mainly investigated with EPs and a decrease in amplitudes was generally observed. This review reveals that both spontaneous EEG and EPs are widely used as biomarkers for analgesic drug effects. Methodological differences are common and a more uniform approach will further enhance the value of such biomarkers for drug development and prediction of treatment response in individual patients. PMID:23593934

The smartphone brain scanner: a portable real-time neuroimaging system.

PubMed

Stopczynski, Arkadiusz; Stahlhut, Carsten; Larsen, Jakob Eg; Petersen, Michael Kai; Hansen, Lars Kai

2014-01-01

Combining low-cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. Here we present the technical details and validation of a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system--Smartphone Brain Scanner--combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully portable system for real-time 3D EEG imaging. We discuss the benefits and challenges, including technical limitations as well as details of real-time reconstruction of 3D images of brain activity. We present examples of brain activity captured in a simple experiment involving imagined finger tapping, which shows that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using an off-the-shelf consumer neuroheadset is lower than the signal obtained using high-density standard EEG equipment, we propose mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings.

FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data.

PubMed

Oostenveld, Robert; Fries, Pascal; Maris, Eric; Schoffelen, Jan-Mathijs

2011-01-01

This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.

Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module.

PubMed

Rifai Chai; Naik, Ganesh R; Tran, Yvonne; Sai Ho Ling; Craig, Ashley; Nguyen, Hung T

2015-08-01

An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p <; 0.05).

Prolonged activation EEG differentiates dementia with and without delirium in frail elderly patients.

PubMed

Thomas, C; Hestermann, U; Walther, S; Pfueller, U; Hack, M; Oster, P; Mundt, C; Weisbrod, M

2008-02-01

Delirium in the elderly results in increased morbidity, mortality and functional decline. Delirium is underdiagnosed, particularly in dementia. To increase diagnostic accuracy, we investigated whether maintenance of activation assessed by EEG discriminates delirium in association with dementia (D+D) from dementia without delirium (DP) and cognitively unimpaired elderly subjects (CU). Routine and quantitative EEG (rEEG/qEEG) with additional prolonged activation (3 min eyes open period) were evaluated in hospitalised elderly patients with acute geriatric disease. Patients were assigned post hoc to three comparable groups (D+D/DP/CU) by expert consensus based on DSM-IV criteria. Dementia diagnosis was confirmed using cognitive and functional tests and caregiver rating (IQCODE, Informed Questionnaire of Cognitive Decline in the Elderly). While rEEG at rest showed low accuracy for a diagnosis of delirium, qEEG in DP and CU revealed a specific activation pattern of high significance found to be absent in the D+D group. Stepwise logistic regression confirmed that differentiation of D+D from DP was best resolved using activated upper alpha and delta power density which, compared with rEEG, enabled an 11% increase in diagnostic correctness to 83%, resulting in 67% sensitivity and 91% specificity. Among frail CU and D+D subjects, almost 90% were correctly classified. Dementia associated with delirium can be discriminated reliably from dementia alone in a meaningful clinical setting. Thus EEG evaluation in chronic encephalopathy should be optimised by a simple activation task and spectral analysis, particularly in the elderly with dementia.

A novel method for device-related electroencephalography artifact suppression to explore cochlear implant-related cortical changes in single-sided deafness.

PubMed

Kim, Kyungsoo; Punte, Andrea Kleine; Mertens, Griet; Van de Heyning, Paul; Park, Kyung-Joon; Choi, Hongsoo; Choi, Ji-Woong; Song, Jae-Jin

2015-11-30

Quantitative electroencephalography (qEEG) is effective when used to analyze ongoing cortical oscillations in cochlear implant (CI) users. However, localization of cortical activity in such users via qEEG is confounded by the presence of artifacts produced by the device itself. Typically, independent component analysis (ICA) is used to remove CI artifacts in auditory evoked EEG signals collected upon brief stimulation and it is effective for auditory evoked potentials (AEPs). However, AEPs do not reflect the daily environments of patients, and thus, continuous EEG data that are closer to such environments are desirable. In this case, device-related artifacts in EEG data are difficult to remove selectively via ICA due to over-completion of EEG data removal in the absence of preprocessing. EEGs were recorded for a long time under conditions of continuous auditory stimulation. To obviate the over-completion problem, we limited the frequency of CI artifacts to a significant characteristic peak and apply ICA artifact removal. Topographic brain mapping results analyzed via band-limited (BL)-ICA exhibited a better energy distribution, matched to the CI location, than data obtained using conventional ICA. Also, source localization data verified that BL-ICA effectively removed CI artifacts. The proposed method selectively removes CI artifacts from continuous EEG recordings, while ICA removal method shows residual peak and removes important brain activity signals. CI artifacts in EEG data obtained during continuous passive listening can be effectively removed with the aid of BL-ICA, opening up new EEG research possibilities in subjects with CIs. Copyright © 2015 Elsevier B.V. All rights reserved.

Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks.

PubMed

Dinov, Martin; Leech, Robert

2017-01-01

Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analog to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs. the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which are likely to be further developed and exploited in future studies. In conclusion, probabilistic clustering and a probabilistic neural network-driven approach to microstate analysis is likely to better model and reveal details and the variability hidden in current deterministic and binarized microstate assignment and analyses.

Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks

PubMed Central

Dinov, Martin; Leech, Robert

2017-01-01

Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analog to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs. the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which are likely to be further developed and exploited in future studies. In conclusion, probabilistic clustering and a probabilistic neural network-driven approach to microstate analysis is likely to better model and reveal details and the variability hidden in current deterministic and binarized microstate assignment and analyses. PMID:29163110

Comparison of different Kalman filter approaches in deriving time varying connectivity from EEG data.

PubMed

Ghumare, Eshwar; Schrooten, Maarten; Vandenberghe, Rik; Dupont, Patrick

2015-08-01

Kalman filter approaches are widely applied to derive time varying effective connectivity from electroencephalographic (EEG) data. For multi-trial data, a classical Kalman filter (CKF) designed for the estimation of single trial data, can be implemented by trial-averaging the data or by averaging single trial estimates. A general linear Kalman filter (GLKF) provides an extension for multi-trial data. In this work, we studied the performance of the different Kalman filtering approaches for different values of signal-to-noise ratio (SNR), number of trials and number of EEG channels. We used a simulated model from which we calculated scalp recordings. From these recordings, we estimated cortical sources. Multivariate autoregressive model parameters and partial directed coherence was calculated for these estimated sources and compared with the ground-truth. The results showed an overall superior performance of GLKF except for low levels of SNR and number of trials.

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA.

PubMed

Labounek, René; Bridwell, David A; Mareček, Radek; Lamoš, Martin; Mikl, Michal; Slavíček, Tomáš; Bednařík, Petr; Baštinec, Jaromír; Hluštík, Petr; Brázdil, Milan; Jan, Jiří

2018-01-01

Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.

A computer-based information system for epilepsy and electroencephalography.

PubMed

Finnerup, N B; Fuglsang-Frederiksen, A; Røssel, P; Jennum, P

1999-08-01

This paper describes a standardised computer-based information system for electroencephalography (EEG) focusing on epilepsy. The system was developed using a prototyping approach. It is based on international recommendations for EEG examination, interpretation and terminology, international guidelines for epidemiological studies on epilepsy and classification of epileptic seizures and syndromes and international classification of diseases. It is divided into: (1) clinical information and epilepsy relevant data; and (2) EEG data, which is hierarchically structured including description and interpretation of EEG. Data is coded but is supplemented with unrestricted text. The resulting patient database can be integrated with other clinical databases and with the patient record system and may facilitate clinical and epidemiological research and development of standards and guidelines for EEG description and interpretation. The system is currently used for teleconsultation between Gentofte and Lisbon.

Grand average ERP-image plotting and statistics: A method for comparing variability in event-related single-trial EEG activities across subjects and conditions

PubMed Central

Delorme, Arnaud; Miyakoshi, Makoto; Jung, Tzyy-Ping; Makeig, Scott

2014-01-01

With the advent of modern computing methods, modeling trial-to-trial variability in biophysical recordings including electroencephalography (EEG) has become of increasingly interest. Yet no widely used method exists for comparing variability in ordered collections of single-trial data epochs across conditions and subjects. We have developed a method based on an ERP-image visualization tool in which potential, spectral power, or some other measure at each time point in a set of event-related single-trial data epochs are represented as color coded horizontal lines that are then stacked to form a 2-D colored image. Moving-window smoothing across trial epochs can make otherwise hidden event-related features in the data more perceptible. Stacking trials in different orders, for example ordered by subject reaction time, by context-related information such as inter-stimulus interval, or some other characteristic of the data (e.g., latency-window mean power or phase of some EEG source) can reveal aspects of the multifold complexities of trial-to-trial EEG data variability. This study demonstrates new methods for computing and visualizing grand ERP-image plots across subjects and for performing robust statistical testing on the resulting images. These methods have been implemented and made freely available in the EEGLAB signal-processing environment that we maintain and distribute. PMID:25447029

Correlated components of ongoing EEG point to emotionally laden attention - a possible marker of engagement?

PubMed

Dmochowski, Jacek P; Sajda, Paul; Dias, Joao; Parra, Lucas C

2012-01-01

Recent evidence from functional magnetic resonance imaging suggests that cortical hemodynamic responses coincide in different subjects experiencing a common naturalistic stimulus. Here we utilize neural responses in the electroencephalogram (EEG) evoked by multiple presentations of short film clips to index brain states marked by high levels of correlation within and across subjects. We formulate a novel signal decomposition method which extracts maximally correlated signal components from multiple EEG records. The resulting components capture correlations down to a one-second time resolution, thus revealing that peak correlations of neural activity across viewings can occur in remarkable correspondence with arousing moments of the film. Moreover, a significant reduction in neural correlation occurs upon a second viewing of the film or when the narrative is disrupted by presenting its scenes scrambled in time. We also probe oscillatory brain activity during periods of heightened correlation, and observe during such times a significant increase in the theta band for a frontal component and reductions in the alpha and beta frequency bands for parietal and occipital components. Low-resolution EEG tomography of these components suggests that the correlated neural activity is consistent with sources in the cingulate and orbitofrontal cortices. Put together, these results suggest that the observed synchrony reflects attention- and emotion-modulated cortical processing which may be decoded with high temporal resolution by extracting maximally correlated components of neural activity.

Correlated Components of Ongoing EEG Point to Emotionally Laden Attention – A Possible Marker of Engagement?

PubMed Central

Dmochowski, Jacek P.; Sajda, Paul; Dias, Joao; Parra, Lucas C.

2012-01-01

Recent evidence from functional magnetic resonance imaging suggests that cortical hemodynamic responses coincide in different subjects experiencing a common naturalistic stimulus. Here we utilize neural responses in the electroencephalogram (EEG) evoked by multiple presentations of short film clips to index brain states marked by high levels of correlation within and across subjects. We formulate a novel signal decomposition method which extracts maximally correlated signal components from multiple EEG records. The resulting components capture correlations down to a one-second time resolution, thus revealing that peak correlations of neural activity across viewings can occur in remarkable correspondence with arousing moments of the film. Moreover, a significant reduction in neural correlation occurs upon a second viewing of the film or when the narrative is disrupted by presenting its scenes scrambled in time. We also probe oscillatory brain activity during periods of heightened correlation, and observe during such times a significant increase in the theta band for a frontal component and reductions in the alpha and beta frequency bands for parietal and occipital components. Low-resolution EEG tomography of these components suggests that the correlated neural activity is consistent with sources in the cingulate and orbitofrontal cortices. Put together, these results suggest that the observed synchrony reflects attention- and emotion-modulated cortical processing which may be decoded with high temporal resolution by extracting maximally correlated components of neural activity. PMID:22623915

Two Different Populations within the Healthy Elderly: Lack of Conflict Detection in Those at Risk of Cognitive Decline

PubMed Central

Sánchez-Moguel, Sergio M.; Alatorre-Cruz, Graciela C.; Silva-Pereyra, Juan; González-Salinas, Sofía; Sanchez-Lopez, Javier; Otero-Ojeda, Gloria A.; Fernández, Thalía

2018-01-01

During healthy aging, inhibitory processing is affected at the sensorial, perceptual, and cognitive levels. The assessment of event-related potentials (ERPs) during the Stroop task has been used to study age-related decline in the efficiency of inhibitory processes. Studies using ERPs have found that the P300 amplitude increases and the N500 amplitude is attenuated in healthy elderly adults compared to those in young adults. On the other hand, it has been reported that theta excess in resting EEG with eyes closed is a good predictor of cognitive decline during aging 7 years later, while a normal EEG increases the probability of not developing cognitive decline. The behavioral and ERP responses during a Counting-Stroop task were compared between 22 healthy elderly subjects with normal EEG (Normal-EEG group) and 22 healthy elderly subjects with an excess of EEG theta activity (Theta-EEG group). Behaviorally, the Normal-EEG group showed a higher behavioral interference effect than the Theta-EEG group. ERP patterns were different between the groups, and two facts are highlighted: (a) the P300 amplitude was higher in the Theta-EEG group, with both groups showing a P300 effect in almost all electrodes, and (b) the Theta-EEG group did not show an N500 effect. These results suggest that the diminishment in inhibitory control observed in the Theta-EEG group may be compensated by different processes in earlier stages, which would allow them to perform the task with similar efficiency to that of participants with a normal EEG. This study is the first to show that healthy elderly subjects with an excess of theta EEG activity not only are at risk of developing cognitive decline but already have a cognitive impairment. PMID:29375352

Two Different Populations within the Healthy Elderly: Lack of Conflict Detection in Those at Risk of Cognitive Decline.

PubMed

Sánchez-Moguel, Sergio M; Alatorre-Cruz, Graciela C; Silva-Pereyra, Juan; González-Salinas, Sofía; Sanchez-Lopez, Javier; Otero-Ojeda, Gloria A; Fernández, Thalía

2017-01-01

During healthy aging, inhibitory processing is affected at the sensorial, perceptual, and cognitive levels. The assessment of event-related potentials (ERPs) during the Stroop task has been used to study age-related decline in the efficiency of inhibitory processes. Studies using ERPs have found that the P300 amplitude increases and the N500 amplitude is attenuated in healthy elderly adults compared to those in young adults. On the other hand, it has been reported that theta excess in resting EEG with eyes closed is a good predictor of cognitive decline during aging 7 years later, while a normal EEG increases the probability of not developing cognitive decline. The behavioral and ERP responses during a Counting-Stroop task were compared between 22 healthy elderly subjects with normal EEG (Normal-EEG group) and 22 healthy elderly subjects with an excess of EEG theta activity (Theta-EEG group). Behaviorally, the Normal-EEG group showed a higher behavioral interference effect than the Theta-EEG group. ERP patterns were different between the groups, and two facts are highlighted: (a) the P300 amplitude was higher in the Theta-EEG group, with both groups showing a P300 effect in almost all electrodes, and (b) the Theta-EEG group did not show an N500 effect. These results suggest that the diminishment in inhibitory control observed in the Theta-EEG group may be compensated by different processes in earlier stages, which would allow them to perform the task with similar efficiency to that of participants with a normal EEG. This study is the first to show that healthy elderly subjects with an excess of theta EEG activity not only are at risk of developing cognitive decline but already have a cognitive impairment.

Changes in the electroencephalogram during anaesthesia and their physiological basis.

PubMed

Hagihira, S

2015-07-01

The use of EEG monitors to assess the level of hypnosis during anaesthesia has become widespread. Anaesthetists, however, do not usually observe the raw EEG data: they generally pay attention only to the Bispectral Index (BIS™) and other indices calculated by EEG monitors. This abstracted information only partially characterizes EEG features. To properly appreciate the availability and reliability of EEG-derived indices, it is necessary to understand how raw EEG changes during anaesthesia. With hemi-frontal lead EEGs obtained under volatile anaesthesia or propofol anaesthesia, the dominant EEG frequency decreases and the amplitude increases with increasing concentrations of anaesthetic. Looking more closely, the EEG changes are more complicated. At surgical concentrations of anaesthesia, spindle waves (alpha range) become dominant. At deeper levels, this activity decreases, and theta and delta waves predominate. At even deeper levels, EEG waveform changes into a burst and suppression pattern, and finally becomes flat. EEG waveforms vary in the presence of noxious stimuli (surgical skin incision), which is not always reflected in BIS™, or other processed EEG indices. Spindle waves are adequately sensitive, however, to noxious stimuli: under surgical anaesthesia they disappear when noxious stimuli are applied, and reappear when adequate analgesia is obtained. To prevent awareness during anaesthesia, I speculate that the most effective strategy is to administer anaesthetic agents in such a way as to maintain anaesthesia at a level where spindle waves predominate. © The Author 2015. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Truncated RAP-MUSIC (TRAP-MUSIC) for MEG and EEG source localization.

PubMed

Mäkelä, Niko; Stenroos, Matti; Sarvas, Jukka; Ilmoniemi, Risto J

2018-02-15

Electrically active brain regions can be located applying MUltiple SIgnal Classification (MUSIC) on magneto- or electroencephalographic (MEG; EEG) data. We introduce a new MUSIC method, called truncated recursively-applied-and-projected MUSIC (TRAP-MUSIC). It corrects a hidden deficiency of the conventional RAP-MUSIC algorithm, which prevents estimation of the true number of brain-signal sources accurately. The correction is done by applying a sequential dimension reduction to the signal-subspace projection. We show that TRAP-MUSIC significantly improves the performance of MUSIC-type localization; in particular, it successfully and robustly locates active brain regions and estimates their number. We compare TRAP-MUSIC and RAP-MUSIC in simulations with varying key parameters, e.g., signal-to-noise ratio, correlation between source time-courses, and initial estimate for the dimension of the signal space. In addition, we validate TRAP-MUSIC with measured MEG data. We suggest that with the proposed TRAP-MUSIC method, MUSIC-type localization could become more reliable and suitable for various online and offline MEG and EEG applications. Copyright © 2017 Elsevier Inc. All rights reserved.

Electroencephalography in premature and full-term infants. Developmental features and glossary.

PubMed

André, M; Lamblin, M-D; d'Allest, A M; Curzi-Dascalova, L; Moussalli-Salefranque, F; S Nguyen The, Tich; Vecchierini-Blineau, M-F; Wallois, F; Walls-Esquivel, E; Plouin, P

2010-05-01

Following the pioneering work of C. Dreyfus-Brisac and N. Monod, research into neonatal electroencephalography (EEG) has developed tremendously in France. French neurophysiologists who had been trained in Paris (France) collaborated on a joint project on the introduction, development, and currently available neonatal EEG recording techniques. They assessed the analytical criteria for the different maturational stages and standardized neonatal EEG terminology on the basis of the large amount of data available in the French and the English literature. The results of their work were presented in 1999. Since the first edition, technology has moved towards the widespread use of digitized recordings. Although the data obtained with analog recordings can be applied to digitized EEG tracings, the present edition, including new published data, is illustrated with digitized recordings. Herein, the reader can find a comprehensive description of EEG features and neonatal behavioural states at different gestational ages, and also a definition of the main aspects and patterns of both pathological and normal EEGs, presented in glossary form. In both sections, numerous illustrations have been provided. This precise neonatal EEG terminology should improve homogeneity in the analysis of neonatal EEG recordings, and facilitate the setting up of multicentric studies on certain aspects of normal EEG recordings and various pathological patterns. Copyright 2010 Elsevier Masson SAS. All rights reserved.

Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF).

PubMed

Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R

2018-01-01

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3-45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25-63% improvement), and VEPs variability (16-44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.

Multiple sparse volumetric priors for distributed EEG source reconstruction.

PubMed

Strobbe, Gregor; van Mierlo, Pieter; De Vos, Maarten; Mijović, Bogdan; Hallez, Hans; Van Huffel, Sabine; López, José David; Vandenberghe, Stefaan

2014-10-15

We revisit the multiple sparse priors (MSP) algorithm implemented in the statistical parametric mapping software (SPM) for distributed EEG source reconstruction (Friston et al., 2008). In the present implementation, multiple cortical patches are introduced as source priors based on a dipole source space restricted to a cortical surface mesh. In this note, we present a technique to construct volumetric cortical regions to introduce as source priors by restricting the dipole source space to a segmented gray matter layer and using a region growing approach. This extension allows to reconstruct brain structures besides the cortical surface and facilitates the use of more realistic volumetric head models including more layers, such as cerebrospinal fluid (CSF), compared to the standard 3-layered scalp-skull-brain head models. We illustrated the technique with ERP data and anatomical MR images in 12 subjects. Based on the segmented gray matter for each of the subjects, cortical regions were created and introduced as source priors for MSP-inversion assuming two types of head models. The standard 3-layered scalp-skull-brain head models and extended 4-layered head models including CSF. We compared these models with the current implementation by assessing the free energy corresponding with each of the reconstructions using Bayesian model selection for group studies. Strong evidence was found in favor of the volumetric MSP approach compared to the MSP approach based on cortical patches for both types of head models. Overall, the strongest evidence was found in favor of the volumetric MSP reconstructions based on the extended head models including CSF. These results were verified by comparing the reconstructed activity. The use of volumetric cortical regions as source priors is a useful complement to the present implementation as it allows to introduce more complex head models and volumetric source priors in future studies. Copyright © 2014 Elsevier Inc. All rights reserved.

Microstates in resting-state EEG: current status and future directions.

PubMed

Khanna, Arjun; Pascual-Leone, Alvaro; Michel, Christoph M; Farzan, Faranak

2015-02-01

Electroencephalography (EEG) is a powerful method of studying the electrophysiology of the brain with high temporal resolution. Several analytical approaches to extract information from the EEG signal have been proposed. One method, termed microstate analysis, considers the multichannel EEG recording as a series of quasi-stable "microstates" that are each characterized by a unique topography of electric potentials over the entire channel array. Because this technique simultaneously considers signals recorded from all areas of the cortex, it is capable of assessing the function of large-scale brain networks whose disruption is associated with several neuropsychiatric disorders. In this review, we first introduce the method of EEG microstate analysis. We then review studies that have discovered significant changes in the resting-state microstate series in a variety of neuropsychiatric disorders and behavioral states. We discuss the potential utility of this method in detecting neurophysiological impairments in disease and monitoring neurophysiological changes in response to an intervention. Finally, we discuss how the resting-state microstate series may reflect rapid switching among neural networks while the brain is at rest, which could represent activity of resting-state networks described by other neuroimaging modalities. We conclude by commenting on the current and future status of microstate analysis, and suggest that EEG microstates represent a promising neurophysiological tool for understanding and assessing brain network dynamics on a millisecond timescale in health and disease. Copyright © 2014 Elsevier Ltd. All rights reserved.

Microstates in Resting-State EEG: Current Status and Future Directions

PubMed Central

Khanna, Arjun; Pascual-Leone, Alvaro; Michel, Christoph M.; Farzan, Faranak

2015-01-01

Electroencephalography (EEG) is a powerful method of studying the electrophysiology of the brain with high temporal resolution. Several analytical approaches to extract information from the EEG signal have been proposed. One method, termed microstate analysis, considers the multichannel EEG recording as a series of quasi-stable “microstates” that are each characterized by a unique topography of electric potentials over the entire channel array. Because this technique simultaneously considers signals recorded from all areas of the cortex, it is capable of assessing the function of large-scale brain networks whose disruption is associated with several neuropsychiatric disorders. In this review, we first introduce the method of EEG microstate analysis. We then review studies that have discovered significant changes in the resting-state microstate series in a variety of neuropsychiatric disorders and behavioral states. We discuss the potential utility of this method in detecting neurophysiological impairments in disease and monitoring neurophysiological changes in response to an intervention. Finally, we discuss how the resting-state microstate series may reflect rapid switching among neural networks while the brain is at rest, which could represent activity of resting-state networks described by other neuroimaging modalities. We conclude by commenting on the current and future status of microstate analysis, and suggest that EEG microstates represent a promising neurophysiological tool for understanding and assessing brain network dynamics on a millisecond timescale in health and disease. PMID:25526823

The quantified EEG characteristics of responders and non-responders to long-term treatment with atomoxetine in children with attention deficit hyperactivity disorders.

PubMed

Chiarenza, Giuseppe Augusto; Chabot, Robert; Isenhart, Robert; Montaldi, Luciano; Chiarenza, Marco Paolo; Torto, Maria Grazia Lo; Prichep, Leslie S

2016-06-01

The aim of our study is to examine quantitative Electroencephalogram (QEEG) differences between ADHD patients that are responders and non-responders to long-term treatment with Atomoxetine at baseline and after 6 and 12months of treatment. Patients with attention deficit hyperactivity disorder (ADHD) received atomoxetine titrated, over 7days, from 0.5 to 1.2mg/kg/day. QEEG and Swanson, Nolan, and Pelham-IV Questionnaire (SNAP-IV) scores were recorded before treatment and after therapy. Twenty minutes of eyes closed resting EEG was recorded from 19 electrodes referenced to linked earlobes. Full frequency and narrow band spectra of two minutes of artifact-free EEG were computed as well as source localization using Variable Resolution Electrical Tomography (VARETA). Abnormalities were identified using Z-spectra relative to normative values. Patients were classified as responders, non-responders and partial responders based upon the SNAP-IV findings. At baseline, the responders showed increased absolute power in alpha and delta in frontal and temporal regions, whereas, non-responders showed increased absolute power in all frequency bands that was widely distributed. With treatment responders' absolute power values moved toward normal values, whereas, non-responders remained at baseline values. Patients with increased power in the alpha band with no evidence of alterations in the beta or theta range, might be responders to treatment with atomoxetine. Increased power in the beta band coupled with increased alpha seems to be related to non-responders and one should consider atomoxetine withdrawal, especially if there is persistence of increased alpha and beta accompanied by an increase of theta. Copyright © 2016 Elsevier B.V. All rights reserved.

Wedge MUSIC: a novel approach to examine experimental differences of brain source connectivity patterns from EEG/MEG data.

PubMed

Ewald, Arne; Avarvand, Forooz Shahbazi; Nolte, Guido

2014-11-01

We introduce a novel method to estimate bivariate synchronization, i.e. interacting brain sources at a specific frequency or band, from MEG or EEG data robust to artifacts of volume conduction. The data driven calculation is solely based on the imaginary part of the cross-spectrum as opposed to the imaginary part of coherency. In principle, the method quantifies how strong a synchronization between a distinct pair of brain sources is present in the data. As an input of the method all pairs of pre-defined locations inside the brain can be used which is computationally exhaustive. In contrast to that, reference sources can be used that have been identified by any source reconstruction technique in a prior analysis step. We introduce different variants of the method and evaluate the performance in simulations. As a particular advantage of the proposed methodology, we demonstrate that the novel approach is capable of investigating differences in brain source interactions between experimental conditions or with respect to a certain baseline. For measured data, we first show the application on resting state MEG data where we find locally synchronized sources in the motor-cortex based on the sensorimotor idle rhythms. Finally, we show an example on EEG motor imagery data where we contrast hand and foot movements. Here, we also find local interactions in the expected brain areas. Copyright © 2014. Published by Elsevier Inc.

Safety and EEG data quality of concurrent high-density EEG and high-speed fMRI at 3 Tesla.

PubMed

Foged, Mette Thrane; Lindberg, Ulrich; Vakamudi, Kishore; Larsson, Henrik B W; Pinborg, Lars H; Kjær, Troels W; Fabricius, Martin; Svarer, Claus; Ozenne, Brice; Thomsen, Carsten; Beniczky, Sándor; Paulson, Olaf B; Posse, Stefan

2017-01-01

Concurrent EEG and fMRI is increasingly used to characterize the spatial-temporal dynamics of brain activity. However, most studies to date have been limited to conventional echo-planar imaging (EPI). There is considerable interest in integrating recently developed high-speed fMRI methods with high-density EEG to increase temporal resolution and sensitivity for task-based and resting state fMRI, and for detecting interictal spikes in epilepsy. In the present study using concurrent high-density EEG and recently developed high-speed fMRI methods, we investigate safety of radiofrequency (RF) related heating, the effect of EEG on cortical signal-to-noise ratio (SNR) in fMRI, and assess EEG data quality. The study compared EPI, multi-echo EPI, multi-band EPI and multi-slab echo-volumar imaging pulse sequences, using clinical 3 Tesla MR scanners from two different vendors that were equipped with 64- and 256-channel MR-compatible EEG systems, respectively, and receive only array head coils. Data were collected in 11 healthy controls (3 males, age range 18-70 years) and 13 patients with epilepsy (8 males, age range 21-67 years). Three of the healthy controls were scanned with the 256-channel EEG system, the other subjects were scanned with the 64-channel EEG system. Scalp surface temperature, SNR in occipital cortex and head movement were measured with and without the EEG cap. The degree of artifacts and the ability to identify background activity was assessed by visual analysis by a trained expert in the 64 channel EEG data (7 healthy controls, 13 patients). RF induced heating at the surface of the EEG electrodes during a 30-minute scan period with stable temperature prior to scanning did not exceed 1.0° C with either EEG system and any of the pulse sequences used in this study. There was no significant decrease in cortical SNR due to the presence of the EEG cap (p > 0.05). No significant differences in the visually analyzed EEG data quality were found between EEG recorded during high-speed fMRI and during conventional EPI (p = 0.78). Residual ballistocardiographic artifacts resulted in 58% of EEG data being rated as poor quality. This study demonstrates that high-density EEG can be safely implemented in conjunction with high-speed fMRI and that high-speed fMRI does not adversely affect EEG data quality. However, the deterioration of the EEG quality due to residual ballistocardiographic artifacts remains a significant constraint for routine clinical applications of concurrent EEG-fMRI.

Disentangling Neural Sources of the Motor Interference Effect in High Functioning Autism: An EEG-Study

ERIC Educational Resources Information Center

Deschrijver, Eliane; Wiersema, Jan R.; Brass, Marcel

2017-01-01

The role of imitation in autism spectrum disorder (ASD) is controversial. Researchers have argued that deficient control of self- and other-related motor representations (self-other distinction) might explain imitation difficulties. In a recent EEG study, we showed that control of imitation relies on high-level as well as on low-level cognitive…

ERP denoising in multichannel EEG data using contrasts between signal and noise subspaces.

PubMed

Ivannikov, Andriy; Kalyakin, Igor; Hämäläinen, Jarmo; Leppänen, Paavo H T; Ristaniemi, Tapani; Lyytinen, Heikki; Kärkkäinen, Tommi

2009-06-15

In this paper, a new method intended for ERP denoising in multichannel EEG data is discussed. The denoising is done by separating ERP/noise subspaces in multidimensional EEG data by a linear transformation and the following dimension reduction by ignoring noise components during inverse transformation. The separation matrix is found based on the assumption that ERP sources are deterministic for all repetitions of the same type of stimulus within the experiment, while the other noise sources do not obey the determinancy property. A detailed derivation of the technique is given together with the analysis of the results of its application to a real high-density EEG data set. The interpretation of the results and the performance of the proposed method under conditions, when the basic assumptions are violated - e.g. the problem is underdetermined - are also discussed. Moreover, we study how the factors of the number of channels and trials used by the method influence the effectiveness of ERP/noise subspaces separation. In addition, we explore also the impact of different data resampling strategies on the performance of the considered algorithm. The results can help in determining the optimal parameters of the equipment/methods used to elicit and reliably estimate ERPs.

EEG-neurofeedback training of beta band (12-22Hz) affects alpha and beta frequencies - A controlled study of a healthy population.

PubMed

Jurewicz, Katarzyna; Paluch, Katarzyna; Kublik, Ewa; Rogala, Jacek; Mikicin, Mirosław; Wróbel, Andrzej

2018-01-08

The frequency-function relation of various EEG bands has inspired EEG-neurofeedback procedures intending to improve cognitive abilities in numerous clinical groups. In this study, we administered EEG-neurofeedback (EEG-NFB) to a healthy population to determine the efficacy of this procedure. We evaluated feedback manipulation in the beta band (12-22Hz), known to be involved in visual attention processing. Two groups of healthy adults were trained to either up- or down-regulate beta band activity, thus providing mutual control. Up-regulation training induced increases in beta and alpha band (8-12Hz) amplitudes during the first three sessions. Group-independent increases in the activity of both bands were observed in the later phase of training. EEG changes were not matched by measured behavioural indices of attention. Parallel changes in the two bands challenge the idea of frequency-specific EEG-NFB protocols and suggest their interdependence. Our study exposes the possibility (i) that the alpha band is more prone to manipulation, and (ii) that changes in the bands' amplitudes are independent from specified training. We therefore encourage a more comprehensive approach to EEG-neurofeedback training embracing physiological and/or operational relations among various EEG bands. Copyright © 2017 Elsevier Ltd. All rights reserved.

EEG source reconstruction reveals frontal-parietal dynamics of spatial conflict processing.

PubMed

Cohen, Michael X; Ridderinkhof, K Richard

2013-01-01

Cognitive control requires the suppression of distracting information in order to focus on task-relevant information. We applied EEG source reconstruction via time-frequency linear constrained minimum variance beamforming to help elucidate the neural mechanisms involved in spatial conflict processing. Human subjects performed a Simon task, in which conflict was induced by incongruence between spatial location and response hand. We found an early (∼200 ms post-stimulus) conflict modulation in stimulus-contralateral parietal gamma (30-50 Hz), followed by a later alpha-band (8-12 Hz) conflict modulation, suggesting an early detection of spatial conflict and inhibition of spatial location processing. Inter-regional connectivity analyses assessed via cross-frequency coupling of theta (4-8 Hz), alpha, and gamma power revealed conflict-induced shifts in cortical network interactions: Congruent trials (relative to incongruent trials) had stronger coupling between frontal theta and stimulus-contrahemifield parietal alpha/gamma power, whereas incongruent trials had increased theta coupling between medial frontal and lateral frontal regions. These findings shed new light into the large-scale network dynamics of spatial conflict processing, and how those networks are shaped by oscillatory interactions.

Effects of Cable Sway, Electrode Surface Area, and Electrode Mass on Electroencephalography Signal Quality during Motion.

PubMed

Symeonidou, Evangelia-Regkina; Nordin, Andrew D; Hairston, W David; Ferris, Daniel P

2018-04-03

More neuroscience researchers are using scalp electroencephalography (EEG) to measure electrocortical dynamics during human locomotion and other types of movement. Motion artifacts corrupt the EEG and mask underlying neural signals of interest. The cause of motion artifacts in EEG is often attributed to electrode motion relative to the skin, but few studies have examined EEG signals under head motion. In the current study, we tested how motion artifacts are affected by the overall mass and surface area of commercially available electrodes, as well as how cable sway contributes to motion artifacts. To provide a ground-truth signal, we used a gelatin head phantom with embedded antennas broadcasting electrical signals, and recorded EEG with a commercially available electrode system. A robotic platform moved the phantom head through sinusoidal displacements at different frequencies (0-2 Hz). Results showed that a larger electrode surface area can have a small but significant effect on improving EEG signal quality during motion and that cable sway is a major contributor to motion artifacts. These results have implications in the development of future hardware for mobile brain imaging with EEG.

Synchronizing MIDI and wireless EEG measurements during natural piano performance.

PubMed

Zamm, Anna; Palmer, Caroline; Bauer, Anna-Katharina R; Bleichner, Martin G; Demos, Alexander P; Debener, Stefan

2017-07-08

Although music performance has been widely studied in the behavioural sciences, less work has addressed the underlying neural mechanisms, perhaps due to technical difficulties in acquiring high-quality neural data during tasks requiring natural motion. The advent of wireless electroencephalography (EEG) presents a solution to this problem by allowing for neural measurement with minimal motion artefacts. In the current study, we provide the first validation of a mobile wireless EEG system for capturing the neural dynamics associated with piano performance. First, we propose a novel method for synchronously recording music performance and wireless mobile EEG. Second, we provide results of several timing tests that characterize the timing accuracy of our system. Finally, we report EEG time domain and frequency domain results from N=40 pianists demonstrating that wireless EEG data capture the unique temporal signatures of musicians' performances with fine-grained precision and accuracy. Taken together, we demonstrate that mobile wireless EEG can be used to measure the neural dynamics of piano performance with minimal motion constraints. This opens many new possibilities for investigating the brain mechanisms underlying music performance. Copyright © 2017 Elsevier B.V. All rights reserved.

Neuromodulating Attention and Mind-Wandering Processes with a Single Session Real Time EEG.

PubMed

Gonçalves, Óscar F; Carvalho, Sandra; Mendes, Augusto J; Leite, Jorge; Boggio, Paulo S

2018-06-01

Our minds are continuously alternating between external attention (EA) and mind wandering (MW). An appropriate balance between EA and MW is important for promoting efficient perceptual processing, executive functioning, decision-making, auto-biographical memory, and creativity. There is evidence that EA processes are associated with increased activity in high-frequency EEG bands (e.g., SMR), contrasting with the dominance of low-frequency bands during MW (e.g., Theta). The aim of the present study was to test the effects of two distinct single session real-time EEG (rtEEG) protocols (SMR up-training/Theta down-training-SMR⇑Theta⇓; Theta up-training/SMR down-training-Theta⇑SMR⇓) on EA and MW processes. Thirty healthy volunteers were randomly assigned to one of two rtEEG training protocols (SMR⇑Theta⇓; Theta⇑SMR⇓). Before and after the rtEEG training, participants completed the attention network task (ANT) along with several MW measures. Both training protocols were effective in increasing SMR (SMR⇑Theta⇓) and theta (Theta⇑SMR⇓) amplitudes but not in decreasing the amplitude of down-trained bands. There were no significant effects of the rtEEG training in either EA or MW measures. However, there was a significant positive correlation between post-training SMR increases and the use of deliberate MW (rather than spontaneous) strategies. Additionally, for the Theta⇑SMR⇓ protocol, increase in post-training Theta amplitude was significantly associated with a decreased efficiency in the orientation network.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

PubMed Central

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain–computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method. PMID:28558002

A review of electroencephalographic changes in diabetes mellitus in relation to major depressive disorder.

PubMed

Baskaran, Anusha; Milev, Roumen; McIntyre, Roger S

2013-01-01

A bidirectional relationship exists between diabetes mellitus (DM) and major depressive disorder (MDD), with depression commonly reported in both type 1 DM (T1DM) and type 2 DM (T2DM), and depressive symptoms associated with a higher incidence of diabetes. However, how the two conditions are pathologically connected is not completely understood. Similar neurophysiological abnormalities have been reported in both DM and MDD, including elevated electroencephalographic (EEG) activity in low-frequency slow waves and increased latency and/or reduced amplitude of event-related potentials. It is possible that this association reflects some common underlying pathology, and it has been proposed that diabetes may place patients at risk for depression through a biological mechanism linking the metabolic changes of DM to changes in the central nervous system. In this review we will discuss EEG abnormalities in DM, as well as the biological mechanisms underlying various EEG parameters, in order to evaluate whether or not a common EEG biosignature exists between DM and MDD. Identifying such commonalities could significantly inform the current understanding of the mechanisms that subserve the development of the two conditions. Moreover, this new insight may provide the basis for informing new drug discovery capable of mitigating and possibly even preventing both conditions.

Beware: Recruitment of Muscle Activity by the EEG-Neurofeedback Trainings of High Frequencies

PubMed Central

Paluch, Katarzyna; Jurewicz, Katarzyna; Rogala, Jacek; Krauz, Rafał; Szczypińska, Marta; Mikicin, Mirosław; Wróbel, Andrzej; Kublik, Ewa

2017-01-01

EEG-neurofeedback (NFB) became a very popular method aimed at improving cognitive and behavioral performance. However, the EMG frequency spectrum overlies the higher EEG oscillations and the NFB trainings focusing on these frequencies is hindered by the problem of EMG load in the information fed back to the subjects. In such a complex signal, it is highly probable that the most controllable component will form the basis for operant conditioning. This might cause different effects in the case of various training protocols and therefore needs to be carefully assessed before designing training protocols and algorithms. In the current experiment a group of healthy adults (n = 14) was trained by professional trainers to up-regulate their beta1 (15–22 Hz) band for eight sessions. The control group (n = 18) underwent the same training regime but without rewards for increasing beta. In half of the participants trained to up-regulate beta1 band (n = 7) a systematic increase in tonic EMG activity was identified offline, implying that muscle activity became a foundation for reinforcement in the trainings. The remaining participants did not present any specific increase of the trained beta1 band amplitude. The training was perceived effective by both trainers and the trainees in all groups. These results indicate the necessity of proper control of muscle activity as a requirement for the genuine EEG-NFB training, especially in protocols that do not aim at the participants’ relaxation. The specificity of the information fed back to the participants should be of highest interest to all therapists and researchers, as it might irreversibly alter the results of the training. PMID:28373836

Sleepwalking episodes are preceded by arousal-related activation in the cingulate motor area: EEG current density imaging.

PubMed

Januszko, Piotr; Niemcewicz, Szymon; Gajda, Tomasz; Wołyńczyk-Gmaj, Dorota; Piotrowska, Anna Justyna; Gmaj, Bartłomiej; Piotrowski, Tadeusz; Szelenberger, Waldemar

2016-01-01

To investigate local arousal fluctuations in adults who received ICSD-2 diagnosis of somnambulism. EEG neuroimaging (eLORETA) was utilized to compare current density distribution for 4s epochs immediately preceding sleepwalking episode (from -4.0 s to 0 s) to the distribution during earlier 4s epochs (from -8.0 s to -4.0 s) in 20 EEG segments from 15 patients. Comparisons between eLORETA images revealed significant (t>4.52; p<0.05) brain activations before onset of sleepwalking, with greater current density within beta 3 frequency range (24-30 Hz) in Brodmann areas 33 and 24. Sleepwalking motor events are associated with arousal-related activation of cingulate motor area. These results support the notion of blurred boundaries between wakefulness and NREM sleep in sleepwalking. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Combining EEG and eye movement recording in free viewing: Pitfalls and possibilities.

PubMed

Nikolaev, Andrey R; Meghanathan, Radha Nila; van Leeuwen, Cees

2016-08-01

Co-registration of EEG and eye movement has promise for investigating perceptual processes in free viewing conditions, provided certain methodological challenges can be addressed. Most of these arise from the self-paced character of eye movements in free viewing conditions. Successive eye movements occur within short time intervals. Their evoked activity is likely to distort the EEG signal during fixation. Due to the non-uniform distribution of fixation durations, these distortions are systematic, survive across-trials averaging, and can become a source of confounding. We illustrate this problem with effects of sequential eye movements on the evoked potentials and time-frequency components of EEG and propose a solution based on matching of eye movement characteristics between experimental conditions. The proposal leads to a discussion of which eye movement characteristics are to be matched, depending on the EEG activity of interest. We also compare segmentation of EEG into saccade-related epochs relative to saccade and fixation onsets and discuss the problem of baseline selection and its solution. Further recommendations are given for implementing EEG-eye movement co-registration in free viewing conditions. By resolving some of the methodological problems involved, we aim to facilitate the transition from the traditional stimulus-response paradigm to the study of visual perception in more naturalistic conditions. Copyright © 2016 Elsevier Inc. All rights reserved.

Temporal lobe deficits in murderers: EEG findings undetected by PET.

PubMed

Gatzke-Kopp, L M; Raine, A; Buchsbaum, M; LaCasse, L

2001-01-01

This study evaluates electroencephalography (EEG) and positron emission tomography (PET) in the same subjects. Fourteen murderers were assessed by using both PET (while they were performing the continuous performance task) and EEG during a resting state. EEG revealed significant increases in slow-wave activity in the temporal, but not frontal, lobe in murderers, in contrast to prior PET findings that showed reduced prefrontal, but not temporal, glucose metabolism. Results suggest that resting EEG shows empirical utility distinct from PET activation findings.

EEG and Coma.

PubMed

Ardeshna, Nikesh I

2016-03-01

Coma is defined as a state of extreme unresponsiveness, in which a person exhibits no voluntary movement or behavior even to painful stimuli. The utilization of EEG for patients in coma has increased dramatically over the last few years. In fact, many institutions have set protocols for continuous EEG (cEEG) monitoring for patients in coma due to potential causes such as subarachnoid hemorrhage or cardiac arrest. Consequently, EEG plays an important role in diagnosis, managenent, and in some cases even prognosis of coma patients.

Short and long-term effects of sham-controlled prefrontal EEG-neurofeedback training in healthy subjects.

PubMed

Engelbregt, H J; Keeser, D; van Eijk, L; Suiker, E M; Eichhorn, D; Karch, S; Deijen, J B; Pogarell, O

2016-04-01

In this study we evaluated long-term effects of frontal beta EEG-neurofeedback training (E-NFT) on healthy subjects. We hypothesized that E-NFT can change frontal beta activity in the long-term and that changes in frontal beta EEG activity are accompanied by altered cognitive performance. 25 healthy subjects were included and randomly assigned to active or sham E-NFT. On average the subjects underwent 15 E-NFT training sessions with a training duration of 45 min. Resting-state EEG was recorded prior to E-NFT training (t1) and in a 3-year follow-up (t3). Compared to sham E-NFT, which was used for the control group, real E-NFT increased beta activity in a predictable way. This increase was maintained over a period of three years post training. However, E-NFT did not result in significantly improved cognitive performance. Based on our results, we conclude that EEG-NFT can selectively modify EEG beta activity both in short and long-term. This is a sham controlled EEG neurofeedback study demonstrating long-term effects in resting state EEG. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Distinctive time-lagged resting-state networks revealed by simultaneous EEG-fMRI.

PubMed

Feige, Bernd; Spiegelhalder, Kai; Kiemen, Andrea; Bosch, Oliver G; Tebartz van Elst, Ludger; Hennig, Jürgen; Seifritz, Erich; Riemann, Dieter

2017-01-15

Functional activation as evidenced by blood oxygen level-dependent (BOLD) functional MRI changes or event-related EEG is known to closely follow patterns of stimulation or self-paced action. Any lags are compatible with axonal conduction velocities and neural integration times. The important analysis of resting state networks is generally based on the assumption that these principles also hold for spontaneous fluctuations in brain activity. Previous observations using simultaneous EEG and fMRI indicate that slower processes, with delays in the seconds range, determine at least part of the relationship between spontaneous EEG and fMRI. To assess this relationship systematically, we used deconvolution analysis of EEG-fMRI during the resting state, assessing the relationship between EEG frequency bands and fMRI BOLD across the whole brain while allowing for time lags of up to 10.5s. Cluster analysis, identifying similar BOLD time courses in relation to EEG band power peaks, showed a clear segregation of functional subsystems of the brain. Our analysis shows that fMRI BOLD increases commonly precede EEG power increases by seconds. Most zero-lag correlations, on the other hand, were negative. This indicates two main distinct neuromodulatory mechanisms: an "idling" mechanism of simultaneous electric and metabolic network anticorrelation and a "regulatory" mechanism in which metabolic network activity precedes increased EEG power by some seconds. This has to be taken into consideration in further studies which address the causal and functional relationship of metabolic and electric brain activity patterns. Copyright © 2016 Elsevier Inc. All rights reserved.

Electrophysiological Measures of Regional Neural Interactive Coupling (Linear and Nonlinear Dependence Relationships Among Multiple Channel Electroencephalographic (EEG) Recordings),

DTIC Science & Technology

1980-01-01

clinical intervention . SG1CUDING CCMENL’ In evaluating the EEGs of subjects it is important to not that . ~major differences in EEG waveshape across...studies in dyslexia . In A.L. Benton and D. Pearl (Fs.), Dyslexia : An Appraisal of Current Knowledge. New York: Oxford University Press, 1978. 4...Electroencephalo- graphy and Clinical Neurophysio !. Oct., 67, 23(4):306-19. 6) Duffy, F.H., Denckla, M.B., Bartels, P.H., and Sandini, G. Dyslexia

[EEG correlates of aggression and anxiety in a social interaction model].

PubMed

Kniazev, G G; Bocharov, A V; Mitrofanova, L G; Slobodskoĭ-Pliusnin, Ia Iu; Pylkova, L V

2011-01-01

Aggressiveness- and anxiety-related behavioral and oscillatory patterns were investigated in 49 18-30 year old subjects during virtual social interactions. The subjects were presented with pictures of "angry", "happy", and "neutral" faces and had to choose one out of three options: "attack", "avoid", or "make friends". Sources of cortical EEG were localized with sLORETA software. Subjects with high aggressiveness chose attack more frequently and this behavior was accompanied by a stronger induced delta and theta synchronization in the right orbitofrontal cortex. In subjects with high anxiety, delta and theta responses were stronger induced in the right temporal cortex during their more frequent avoidance behavior. Thus, both in anxious and in aggressive subjects, typical behavior was accompanied by increased induced low-frequency synchronization whose localization implies that it is associated with motivational and emotional processes.

Amplitude-integrated EEG and the newborn infant.

PubMed

Shah, Divyen K; Mathur, Amit

2014-01-01

There is emerging recognition of the need for continuous long term electrographic monitoring of the encephalopathic neonate. While full-montage EEG with video remains the gold standard for monitoring, it is limited in application due to the complexity of lead application and specialized interpretation of results. Amplitude integrated EEG (aEEG) is derived from limited channels (usually C3-P3, C4-P4) and is filtered, rectified and time-compressed to serve as a bedside electrographic trend monitor. Its simple application and interpretation has resulted in increasing use in neonatal units across the world. Validation studies with full montage EEG have shown reliable results in interpretation of EEG background and electrographic seizures, especially when used with the simultaneously displayed raw EEG trace. Several aEEG monitors are commercially available and seizure algorithms are being developed for use on these monitors. These aEEG monitors, complement conventional EEG and offer a significant advance in the feasibility of long term electrographic monitoring of the encephalopathic neonate.

FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

PubMed Central

Oostenveld, Robert; Fries, Pascal; Maris, Eric; Schoffelen, Jan-Mathijs

2011-01-01

This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages. PMID:21253357

Portable Amplifier Design for a Novel EEG Monitor in Point-of-Care Applications.

PubMed

Luan, Bo; Sun, Mingui; Jia, Wenyan

2012-01-01

The Electroencephalography (EEG) is a common diagnostic tool for neurological diseases and dysfunctions, such as epilepsy and insomnia. However, the current EEG technology cannot be utilized quickly and conveniently at the point of care due to the complex skin preparation procedures required and the inconvenient EEG data acquisition systems. This work presents a portable amplifier design that integrates a set of skin screw electrodes and a wireless data link. The battery-operated amplifier contains an instrumentation amplifier, two noninverting amplifiers, two high-pass filters, and a low-pass filter. It is able to magnify the EEG signals over 10,000 times and has a high impedance, low noise, small size and low weight. Our electrode and amplifier are ideal for point-of-care applications, especially during transportation of patients suffering from traumatic brain injury or stroke.

Acute effect of carbamazepine on corticothalamic 5-9-Hz and thalamocortical spindle (10-16-Hz) oscillations in the rat.

PubMed

Zheng, Thomas W; O'Brien, Terence J; Kulikova, Sofya P; Reid, Christopher A; Morris, Margaret J; Pinault, Didier

2014-03-01

A major side effect of carbamazepine (CBZ), a drug used to treat neurological and neuropsychiatric disorders, is drowsiness, a state characterized by increased slow-wave oscillations with the emergence of sleep spindles in the electroencephalogram (EEG). We conducted cortical EEG and thalamic cellular recordings in freely moving or lightly anesthetized rats to explore the impact of CBZ within the intact corticothalamic (CT)-thalamocortical (TC) network, more specifically on CT 5-9-Hz and TC spindle (10-16-Hz) oscillations. Two to three successive 5-9-Hz waves were followed by a spindle in the cortical EEG. A single systemic injection of CBZ (20 mg/kg) induced a significant increase in the power of EEG 5-9-Hz oscillations and spindles. Intracellular recordings of glutamatergic TC neurons revealed 5-9-Hz depolarizing wave-hyperpolarizing wave sequences prolonged by robust, rhythmic spindle-frequency hyperpolarizing waves. This hybrid sequence occurred during a slow hyperpolarizing trough, and was at least 10 times more frequent under the CBZ condition than under the control condition. The hyperpolarizing waves reversed at approximately -70 mV, and became depolarizing when recorded with KCl-filled intracellular micropipettes, indicating that they were GABAA receptor-mediated potentials. In neurons of the GABAergic thalamic reticular nucleus, the principal source of TC GABAergic inputs, CBZ augmented both the number and the duration of sequences of rhythmic spindle-frequency bursts of action potentials. This indicates that these GABAergic neurons are responsible for the generation of at least the spindle-frequency hyperpolarizing waves in TC neurons. In conclusion, CBZ potentiates GABAA receptor-mediated TC spindle oscillations. Furthermore, we propose that CT 5-9-Hz waves can trigger TC spindles. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Relative Power of Specific EEG Bands and Their Ratios during Neurofeedback Training in Children with Autism Spectrum Disorder

PubMed Central

Wang, Yao; Sokhadze, Estate M.; El-Baz, Ayman S.; Li, Xiaoli; Sears, Lonnie; Casanova, Manuel F.; Tasman, Allan

2016-01-01

Neurofeedback is a mode of treatment that is potentially useful for improving self-regulation skills in persons with autism spectrum disorder. We proposed that operant conditioning of EEG in neurofeedback mode can be accompanied by changes in the relative power of EEG bands. However, the details on the change of the relative power of EEG bands during neurofeedback training course in autism are not yet well explored. In this study, we analyzed the EEG recordings of children diagnosed with autism and enrolled in a prefrontal neurofeedback treatment course. The protocol used in this training was aimed at increasing the ability to focus attention, and the procedure represented the wide band EEG amplitude suppression training along with upregulation of the relative power of gamma activity. Quantitative EEG analysis was completed for each session of neurofeedback using wavelet transform to determine the relative power of gamma and theta/beta ratio, and further to detect the statistical changes within and between sessions. We found a linear decrease of theta/beta ratio and a liner increase of relative power of gamma activity over 18 weekly sessions of neurofeedback in 18 high functioning children with autism. The study indicates that neurofeedback is an effective method for altering EEG characteristics associated with the autism spectrum disorder. Also, it provides information about specific changes of EEG activities and details the correlation between changes of EEG and neurofeedback indexes during the course of neurofeedback. This pilot study contributes to the development of more effective approaches to EEG data analysis during prefrontal neurofeedback training in autism. PMID:26834615

Acute triadimefon-induced changes in the EEG of Long-Evans Rats

EPA Science Inventory

We have reported that the non-stimulus driven EEG is altered differently by acute treatment with deltamethrin, permethrin, fipronil, or imidacloprid (Lyke and Herr, Lyke et al., Toxicologist, 2010, 2011, 2012) in non-restrained animals. In the current study, we examined the abili...

Development of Point of Care Testing Device for Neurovascular Coupling From Simultaneous Recording of EEG and NIRS During Anodal Transcranial Direct Current Stimulation

PubMed Central

Jindal, Utkarsh; Sood, Mehak; Dutta, Anirban; Chowdhury, Shubhajit Roy

2015-01-01

This paper presents a point of care testing device for neurovascular coupling (NVC) from simultaneous recording of electroencephalogram (EEG) and near infrared spectroscopy (NIRS) during anodal transcranial direct current stimulation (tDCS). Here, anodal tDCS modulated cortical neural activity leading to hemodynamic response can be used to identify the impaired cerebral microvessels functionality. The impairments in the cerebral microvessels functionality may lead to impairments in the cerebrovascular reactivity (CVR), where severely reduced CVR predicts the chances of transient ischemic attack and ipsilateral stroke. The neural and hemodynamic responses to anodal tDCS were studied through joint imaging with EEG and NIRS, where NIRS provided optical measurement of changes in tissue oxy-(\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$HbO2)$ \\end{document} and deoxy-(\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$Hb$ \\end{document}) hemoglobin concentration and EEG captured alterations in the underlying neuronal current generators. Then, a cross-correlation method for the assessment of NVC underlying the site of anodal tDCS is presented. The feasibility studies on healthy subjects and stroke survivors showed detectable changes in the EEG and the NIRS responses to a 0.526 A/\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$\\mathrm{m}^{2}$ \\end{document} of anodal tDCS. The NIRS system was bench tested on 15 healthy subjects that showed a statistically significant (p < 0.01) difference in the signal-to-noise ratio (SNR) between the ON- and OFF-states of anodal tDCS where the mean SNR of the NIRS device was found to be 42.33 ± 1.33 dB in the ON-state and 40.67 ± 1.23 dB in the OFF-state. Moreover, the clinical study conducted on 14 stroke survivors revealed that the lesioned hemisphere with impaired circulation showed significantly (p < 0.01) less change in \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$HbO2$ \\end{document} than the nonlesioned side in response to anodal tDCS. The EEG study on healthy subjects showed a statistically significant (p < 0.05) decrease around individual alpha frequency in the alpha band (8−13 Hz) following anodal tDCS. Moreover, the joint EEG-NIRS imaging on 4 stroke survivors showed an immediate increase in the theta band (4−8 Hz) EEG activity after the start of anodal tDCS at the nonlesioned hemisphere. Furthermore, cross-correlation function revealed a significant (95% confidence interval) negative cross correlation only at the nonlesioned hemisphere during anodal tDCS, where the log-transformed mean-power of EEG within 0.5−11.25 Hz lagged \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$HbO2$ \\end{document} response in one of the stroke survivors with white matter lesions. Therefore, it was concluded that the anodal tDCS can perturb the local neural and the vascular activity (via NVC) which can be used for assessing regional NVC functionality where confirmatory clinical studies are required. PMID:27170897

Topography, power, and current source density of θ oscillations during reward processing as markers for alcohol dependence.

PubMed

Kamarajan, Chella; Rangaswamy, Madhavi; Manz, Niklas; Chorlian, David B; Pandey, Ashwini K; Roopesh, Bangalore N; Porjesz, Bernice

2012-05-01

Recent studies have linked alcoholism with a dysfunctional neural reward system. Although several electrophysiological studies have explored reward processing in healthy individuals, such studies in alcohol-dependent individuals are quite rare. The present study examines theta oscillations during reward processing in abstinent alcoholics. The electroencephalogram (EEG) was recorded in 38 abstinent alcoholics and 38 healthy controls as they performed a single outcome gambling task, which involved outcomes of either loss or gain of an amount (10 or 50¢) that was bet. Event-related theta band (3.0-7.0 Hz) power following each outcome stimulus was computed using the S-transform method. Theta power at the time window of the outcome-related negativity (ORN) and positivity (ORP) (200-500 ms) was compared across groups and outcome conditions. Additionally, behavioral data of impulsivity and task performance were analyzed. The alcoholic group showed significantly decreased theta power during reward processing compared to controls. Current source density (CSD) maps of alcoholics revealed weaker and diffuse source activity for all conditions and weaker bilateral prefrontal sources during the Loss 50 condition when compared with controls who manifested stronger and focused midline sources. Furthermore, alcoholics exhibited increased impulsivity and risk-taking on the behavioral measures. A strong association between reduced anterior theta power and impulsive task-performance was observed. It is suggested that decreased power and weaker and diffuse CSD in alcoholics may be due to dysfunctional neural reward circuitry. The relationship among alcoholism, theta oscillations, reward processing, and impulsivity could offer clues to understand brain circuitries that mediate reward processing and inhibitory control. Copyright © 2011 Wiley-Liss, Inc.

Topography, Power and Current Source Density of Theta Oscillations during Reward Processing as Markers for Alcohol Dependence

PubMed Central

Kamarajan, Chella; Rangaswamy, Madhavi; Manz, Niklas; Chorlian, David B.; Pandey, Ashwini K.; Roopesh, Bangalore N.; Porjesz, Bernice

2013-01-01

Recent studies have linked alcoholism with a dysfunctional neural reward system. Although several electrophysiological studies have explored reward processing in healthy individuals, such studies in alcohol dependent individuals are quite rare. The present study examines theta oscillations during reward processing in abstinent alcoholics. The electroencephalogram (EEG) was recorded in 38 abstinent alcoholics and 38 healthy controls as they performed a single outcome gambling task which involved outcomes of either loss or gain of an amount (10¢ or 50¢) that was bet. Event-related theta band (3.0–7.0 Hz) power following each outcome stimulus was computed using the S-transform method. Theta power at the time window of the outcome-related negativity (ORN) and positivity (ORP) (200–500 ms) was compared across groups and outcome conditions. Additionally, behavioral data of impulsivity and task performance were analyzed. The alcoholic group showed significantly decreased theta power during reward processing compared to controls. Current Source Density (CSD) maps of alcoholics revealed weaker and diffuse source activity for all conditions and weaker bilateral prefrontal sources during the Loss 50 condition as compared to controls who manifested stronger and focused midline sources. Further, alcoholics exhibited increased impulsivity and risk-taking on the behavioral measures. A strong association between reduced anterior theta power and impulsive task-performance was observed. It is suggested that decreased power and weaker and diffuse CSD in alcoholics may be due to dysfunctional neural reward circuitry. The relationship among alcoholism, theta oscillations, reward processing and impulsivity could offer clues to understand brain circuitries that mediate reward processing and inhibitory control. PMID:21520344

Defining and quantifying users' mental Imagery-based BCI skills: a first step.

PubMed

Lotte, Fabien; Jeunet, Camille

2018-05-17

While promising for many applications, Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are still scarcely used outside laboratories, due to a poor reliability. It is thus necessary to study and fix this reliability issue. Doing so requires the use of appropriate reliability metrics to quantify both the classification algorithm and the BCI user's performances. So far, Classification Accuracy (CA) is the typical metric used for both aspects. However, we argue in this paper that CA is a poor metric to study BCI users' skills. Here, we propose a definition and new metrics to quantify such BCI skills for Mental Imagery (MI) BCIs, independently of any classification algorithm. Approach: We first show in this paper that CA is notably unspecific, discrete, training data and classifier dependent, and as such may not always reflect successful self-modulation of EEG patterns by the user. We then propose a definition of MI-BCI skills that reflects how well the user can self-modulate EEG patterns, and thus how well he could control an MI-BCI. Finally, we propose new performance metrics, classDis, restDist and classStab that specifically measure how distinct and stable the EEG patterns produced by the user are, independently of any classifier. Main results: By re-analyzing EEG data sets with such new metrics, we indeed confirmed that CA may hide some increase in MI-BCI skills or hide the user inability to self-modulate a given EEG pattern. On the other hand, our new metrics could reveal such skill improvements as well as identify when a mental task performed by a user was no different than rest EEG. Significance: Our results showed that when studying MI-BCI users' skills, CA should be used with care, and complemented with metrics such as the new ones proposed. Our results also stressed the need to redefine BCI user training by considering the different BCI subskills and their measures. To promote the complementary use of our new metrics, we provide the Matlab code to compute them for free and open-source. © 2018 IOP Publishing Ltd.

Conventional and reciprocal approaches to the inverse dipole localization problem for N(20)-P (20) somatosensory evoked potentials.

PubMed

Finke, Stefan; Gulrajani, Ramesh M; Gotman, Jean; Savard, Pierre

2013-01-01

The non-invasive localization of the primary sensory hand area can be achieved by solving the inverse problem of electroencephalography (EEG) for N(20)-P(20) somatosensory evoked potentials (SEPs). This study compares two different mathematical approaches for the computation of transfer matrices used to solve the EEG inverse problem. Forward transfer matrices relating dipole sources to scalp potentials are determined via conventional and reciprocal approaches using individual, realistically shaped head models. The reciprocal approach entails calculating the electric field at the dipole position when scalp electrodes are reciprocally energized with unit current-scalp potentials are obtained from the scalar product of this electric field and the dipole moment. Median nerve stimulation is performed on three healthy subjects and single-dipole inverse solutions for the N(20)-P(20) SEPs are then obtained by simplex minimization and validated against the primary sensory hand area identified on magnetic resonance images. Solutions are presented for different time points, filtering strategies, boundary-element method discretizations, and skull conductivity values. Both approaches produce similarly small position errors for the N(20)-P(20) SEP. Position error for single-dipole inverse solutions is inherently robust to inaccuracies in forward transfer matrices but dependent on the overlapping activity of other neural sources. Significantly smaller time and storage requirements are the principal advantages of the reciprocal approach. Reduced computational requirements and similar dipole position accuracy support the use of reciprocal approaches over conventional approaches for N(20)-P(20) SEP source localization.

Low-resolution electromagnetic brain tomography (LORETA) of monozygotic twins discordant for chronic fatigue syndrome.

PubMed

Sherlin, Leslie; Budzynski, Thomas; Kogan Budzynski, Helen; Congedo, Marco; Fischer, Mary E; Buchwald, Dedra

2007-02-15

Previous work using quantified EEG has suggested that brain activity in individuals with chronic fatigue syndrome (CFS) and normal persons differs. Our objective was to investigate if specific frequency band-pass regions and spatial locations are associated with CFS using low-resolution electromagnetic brain tomography (LORETA). We conducted a co-twin control study of 17 pairs of monozygotic twins where 1 twin met criteria for CFS and the co-twin was healthy. Twins underwent an extensive battery of tests including a structured psychiatric interview and a quantified EEG. Eyes closed EEG frequency-domain analysis was computed and the entire brain volume was compared of the CFS and healthy twins using a multiple comparison procedure. Compared with their healthy co-twins, twins with CFS differed in current source density. The CFS twins had higher delta in the left uncus and parahippocampal gyrus and higher theta in the cingulate gyrus and right superior frontal gyrus. These findings suggest that neurophysiological activity in specific areas of the brain may differentiate individuals with CFS from those in good health. The study corroborates that slowing of the deeper structures of the limbic system is associated with affect. It also supports the neurobiological model that the right forebrain is associated with sympathetic activity and the left forebrain with the effective management of energy. These preliminary findings await replication.

Unavoidable Errors: A Spatio-Temporal Analysis of Time-Course and Neural Sources of Evoked Potentials Associated with Error Processing in a Speeded Task

ERIC Educational Resources Information Center

Vocat, Roland; Pourtois, Gilles; Vuilleumier, Patrik

2008-01-01

The detection of errors is known to be associated with two successive neurophysiological components in EEG, with an early time-course following motor execution: the error-related negativity (ERN/Ne) and late positivity (Pe). The exact cognitive and physiological processes contributing to these two EEG components, as well as their functional…

Quantitative EEG and its Correlation with Cardiovascular, Cognition and mood State: an Integrated Study in Simulated Microgravity

NASA Astrophysics Data System (ADS)

Zhang, Jianyuan; Hu, Bin; Chen, Wenjuan; Moore, Philip; Xu, Tingting; Dong, Qunxi; Liu, Zhenyu; Luo, Yuejia; Chen, Shanguang

2014-12-01

The focus of the study is the estimation of the effects of microgravity on the central nervous activity and its underlying influencing mechanisms. To validate the microgravity-induced physiological and psychological effects on EEG, quantitative EEG features, cardiovascular indicators, mood state, and cognitive performances data collection was achieved during a 45 day period using a -6°head-down bed rest (HDBR) integrated approach. The results demonstrated significant differences in EEG data, as an increased Theta wave, a decreased Beta wave and a reduced complexity of brain, accompanied with an increased heart rate and pulse rate, decreased positive emotion, and degraded emotion conflict monitoring performance. The canonical correlation analysis (CCA) based cardiovascular and cognitive related EEG model showed the cardiovascular effect on EEG mainly affected bilateral temporal region and the cognitive effect impacted parietal-occipital and frontal regions. The results obtained in the study support the use of an approach which combines a multi-factor influential mechanism hypothesis. The changes in the EEG data may be influenced by both cardiovascular and cognitive effects.

Emotion recognition from multichannel EEG signals using K-nearest neighbor classification.

PubMed

Li, Mi; Xu, Hongpei; Liu, Xingwang; Lu, Shengfu

2018-04-27

Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals. This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels. We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were divided into four frequency bands using discrete wavelet transform, and entropy and energy were calculated as features of K-nearest neighbor Classifier. The classification accuracies of the 10, 14, 18 and 32 EEG channels based on the Gamma frequency band were 89.54%, 92.28%, 93.72% and 95.70% in the valence dimension and 89.81%, 92.24%, 93.69% and 95.69% in the arousal dimension. As the number of channels increases, the classification accuracy of emotional states also increases, the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands. This paper provided better frequency bands and channels reference for emotion recognition based on EEG.

PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG.

PubMed

Ball, Kenneth; Bigdely-Shamlo, Nima; Mullen, Tim; Robbins, Kay

2016-01-01

Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal order of signals and enforces certain mixing stationarity constraints. The resulting procedure, which we call Pairwise Complex Independent Component Analysis (PWC-ICA), performs the ICA in a complex setting and then reinterprets the results in the original observation space. We examine the performance of our candidate approach relative to several existing ICA algorithms for the blind source separation (BSS) problem on both real and simulated EEG data. On simulated data, PWC-ICA is often capable of achieving a better solution to the BSS problem than AMICA, Extended Infomax, or FastICA. On real data, the dipole interpretations of the BSS solutions discovered by PWC-ICA are physically plausible, are competitive with existing ICA approaches, and may represent sources undiscovered by other ICA methods. In conjunction with this paper, the authors have released a MATLAB toolbox that performs PWC-ICA on real, vector-valued signals.

PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG

PubMed Central

Bigdely-Shamlo, Nima; Mullen, Tim; Robbins, Kay

2016-01-01

Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal order of signals and enforces certain mixing stationarity constraints. The resulting procedure, which we call Pairwise Complex Independent Component Analysis (PWC-ICA), performs the ICA in a complex setting and then reinterprets the results in the original observation space. We examine the performance of our candidate approach relative to several existing ICA algorithms for the blind source separation (BSS) problem on both real and simulated EEG data. On simulated data, PWC-ICA is often capable of achieving a better solution to the BSS problem than AMICA, Extended Infomax, or FastICA. On real data, the dipole interpretations of the BSS solutions discovered by PWC-ICA are physically plausible, are competitive with existing ICA approaches, and may represent sources undiscovered by other ICA methods. In conjunction with this paper, the authors have released a MATLAB toolbox that performs PWC-ICA on real, vector-valued signals. PMID:27340397

Hemifield-dependent N1 and event-related theta/delta oscillations: An unbiased comparison of surface Laplacian and common EEG reference choices

PubMed Central

Kayser, Jürgen; Tenke, Craig E.

2015-01-01

Surface Laplacian methodology has been used to reduce the impact of volume conduction and arbitrary choice of EEG recording reference for the analysis of surface potentials. However, the empirical implications of employing these different transformations to the same EEG data remain obscure. This study directly compared the statistical effects of four commonly-used (nose, linked mastoids, average) or recommended (reference electrode standardization technique [REST]) references and their spherical spline current source density (CSD) transformation for a large data set stemming from a well-understood experimental manipulation. ERPs (72 sites) recorded from 130 individuals during a visual half-field paradigm with highly-controlled emotional stimuli were characterized by mid-parietooccipital N1 (125 ms peak latency) and event-related synchronization (ERS) of theta/delta (160 ms), which were most robust over the contralateral hemisphere. All five data transformations were rescaled to the same covariance and submitted to a single temporal or time-frequency PCA (Varimax) to yield simplified estimates of N1 or theta/delta ERS. Unbiased nonparametric permutation tests revealed that these hemifield-dependent asymmetries were by far most focal and prominent for CSD data, despite all transformations showing maximum effects at mid-parietooccipital sites. Employing smaller subsamples (signal-to-noise) or window-based ERP/ERS amplitudes did not affect these comparisons. Furthermore, correlations between N1 and theta/delta ERS at these sites were strongest for CSD and weakest for nose-referenced data. Contrary to the common notion that the spatial high pass filter properties of a surface Laplacian reduce important contributions of neuronal generators to the EEG signal, the present findings demonstrate that instead volume conduction inherent in surface potentials weakens the representation of neuronal activation patterns at scalp that directly reflect regional brain activity. PMID:25562833

Early Prefrontal Brain Responses to the Hedonic Quality of Emotional Words – A Simultaneous EEG and MEG Study

PubMed Central

Keuper, Kati; Zwitserlood, Pienie; Rehbein, Maimu A.; Eden, Annuschka S.; Laeger, Inga; Junghöfer, Markus; Zwanzger, Peter; Dobel, Christian

2013-01-01

The hedonic meaning of words affects word recognition, as shown by behavioral, functional imaging, and event-related potential (ERP) studies. However, the spatiotemporal dynamics and cognitive functions behind are elusive, partly due to methodological limitations of previous studies. Here, we account for these difficulties by computing combined electro-magnetoencephalographic (EEG/MEG) source localization techniques. Participants covertly read emotionally high-arousing positive and negative nouns, while EEG and MEG were recorded simultaneously. Combined EEG/MEG current-density reconstructions for the P1 (80–120 ms), P2 (150–190 ms) and EPN component (200–300 ms) were computed using realistic individual head models, with a cortical constraint. Relative to negative words, the P1 to positive words predominantly involved language-related structures (left middle temporal and inferior frontal regions), and posterior structures related to directed attention (occipital and parietal regions). Effects shifted to the right hemisphere in the P2 component. By contrast, negative words received more activation in the P1 time-range only, recruiting prefrontal regions, including the anterior cingulate cortex (ACC). Effects in the EPN were not statistically significant. These findings show that different neuronal networks are active when positive versus negative words are processed. We account for these effects in terms of an “emotional tagging” of word forms during language acquisition. These tags then give rise to different processing strategies, including enhanced lexical processing of positive words and a very fast language-independent alert response to negative words. The valence-specific recruitment of different networks might underlie fast adaptive responses to both approach- and withdrawal-related stimuli, be they acquired or biological. PMID:23940642

Combined process automation for large-scale EEG analysis.

PubMed

Sfondouris, John L; Quebedeaux, Tabitha M; Holdgraf, Chris; Musto, Alberto E

2012-01-01

Epileptogenesis is a dynamic process producing increased seizure susceptibility. Electroencephalography (EEG) data provides information critical in understanding the evolution of epileptiform changes throughout epileptic foci. We designed an algorithm to facilitate efficient large-scale EEG analysis via linked automation of multiple data processing steps. Using EEG recordings obtained from electrical stimulation studies, the following steps of EEG analysis were automated: (1) alignment and isolation of pre- and post-stimulation intervals, (2) generation of user-defined band frequency waveforms, (3) spike-sorting, (4) quantification of spike and burst data and (5) power spectral density analysis. This algorithm allows for quicker, more efficient EEG analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.

A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification.

PubMed

LeVan, P; Urrestarazu, E; Gotman, J

2006-04-01

To devise an automated system to remove artifacts from ictal scalp EEG, using independent component analysis (ICA). A Bayesian classifier was used to determine the probability that 2s epochs of seizure segments decomposed by ICA represented EEG activity, as opposed to artifact. The classifier was trained using numerous statistical, spectral, and spatial features. The system's performance was then assessed using separate validation data. The classifier identified epochs representing EEG activity in the validation dataset with a sensitivity of 82.4% and a specificity of 83.3%. An ICA component was considered to represent EEG activity if the sum of the probabilities that its epochs represented EEG exceeded a threshold predetermined using the training data. Otherwise, the component represented artifact. Using this threshold on the validation set, the identification of EEG components was performed with a sensitivity of 87.6% and a specificity of 70.2%. Most misclassified components were a mixture of EEG and artifactual activity. The automated system successfully rejected a good proportion of artifactual components extracted by ICA, while preserving almost all EEG components. The misclassification rate was comparable to the variability observed in human classification. Current ICA methods of artifact removal require a tedious visual classification of the components. The proposed system automates this process and removes simultaneously multiple types of artifacts.

Reappraisal of abnormal EEG findings in children with ADHD: on the relationship between ADHD and epileptiform discharges.

PubMed

Kanazawa, Osamu

2014-12-01

Attention-deficit hyperactivity disorder is suggested to be closely related to epilepsy. A recent large-scale study revealed that ADHD in children is often accompanied by epilepsy. In Japan, methylphenidate (MPH) as a sustained-action tablet and atomoxetine (ATX) became commercially available as medications for children recently. Since then, the number of prescriptions of both medicines has increased rapidly. Methylphenidate, as a psychostimulant, has been a source of concern because of the perceived lowered threshold for convulsions in children. Based on this background, reappraisal of EEG findings in children with ADHD is important in order to detect indications of potential comorbid epilepsy and to investigate the developmental mechanisms of the neurophysiological manifestations in patients with ADHD. EEG findings in children newly diagnosed with ADHD and their relationship with clinical findings were investigated. The author evaluated 208 patients with ADHD newly diagnosed between 2008 and 2013. Of these, there were 145 patients for whom EEG findings were obtained along with a clinical follow-up for at least three months. Patients with IQ<70 were excluded in order to obtain a homogenous group of patients with ADHD. The male-to-female ratio was 130:15, and the age range was between 5 years, 9 months and 19 years, 9 months, with mean age of 11 years, 4 months. The results revealed that about half (48.3%) of the children with ADHD had abnormal EEG findings and that 22.1% of them had epileptiform discharges. Patients without comorbidity of autism spectrum disorder (ore homogenous group with ADHD) were especially likely to show abnormal EEG findings (51.0%) including epileptiform discharges (24.5%). Afebrile seizures, that is, epileptic seizures, occurred in a boy three days after commencement of administration with MPH as a sustained-action tablet. In four patients with a past history of epilepsy, neither relapse of EEG abnormality nor epileptic seizures were observed during the follow-up period. There was to be a significantly close relationship between ADHD and epileptiform discharges. Therefore, in patients with ADHD, it is important to obtain more precise information about seizures and presence of epilepsy from the personal and family histories, as well as to undertake a thorough EEG examination. Copyright © 2014 Elsevier Inc. All rights reserved.

Safety and EEG data quality of concurrent high-density EEG and high-speed fMRI at 3 Tesla

PubMed Central

Foged, Mette Thrane; Lindberg, Ulrich; Vakamudi, Kishore; Larsson, Henrik B. W.; Pinborg, Lars H.; Kjær, Troels W.; Fabricius, Martin; Svarer, Claus; Ozenne, Brice; Thomsen, Carsten; Beniczky, Sándor; Posse, Stefan

2017-01-01

Purpose Concurrent EEG and fMRI is increasingly used to characterize the spatial-temporal dynamics of brain activity. However, most studies to date have been limited to conventional echo-planar imaging (EPI). There is considerable interest in integrating recently developed high-speed fMRI methods with high-density EEG to increase temporal resolution and sensitivity for task-based and resting state fMRI, and for detecting interictal spikes in epilepsy. In the present study using concurrent high-density EEG and recently developed high-speed fMRI methods, we investigate safety of radiofrequency (RF) related heating, the effect of EEG on cortical signal-to-noise ratio (SNR) in fMRI, and assess EEG data quality. Materials and methods The study compared EPI, multi-echo EPI, multi-band EPI and multi-slab echo-volumar imaging pulse sequences, using clinical 3 Tesla MR scanners from two different vendors that were equipped with 64- and 256-channel MR-compatible EEG systems, respectively, and receive only array head coils. Data were collected in 11 healthy controls (3 males, age range 18–70 years) and 13 patients with epilepsy (8 males, age range 21–67 years). Three of the healthy controls were scanned with the 256-channel EEG system, the other subjects were scanned with the 64-channel EEG system. Scalp surface temperature, SNR in occipital cortex and head movement were measured with and without the EEG cap. The degree of artifacts and the ability to identify background activity was assessed by visual analysis by a trained expert in the 64 channel EEG data (7 healthy controls, 13 patients). Results RF induced heating at the surface of the EEG electrodes during a 30-minute scan period with stable temperature prior to scanning did not exceed 1.0° C with either EEG system and any of the pulse sequences used in this study. There was no significant decrease in cortical SNR due to the presence of the EEG cap (p > 0.05). No significant differences in the visually analyzed EEG data quality were found between EEG recorded during high-speed fMRI and during conventional EPI (p = 0.78). Residual ballistocardiographic artifacts resulted in 58% of EEG data being rated as poor quality. Conclusion This study demonstrates that high-density EEG can be safely implemented in conjunction with high-speed fMRI and that high-speed fMRI does not adversely affect EEG data quality. However, the deterioration of the EEG quality due to residual ballistocardiographic artifacts remains a significant constraint for routine clinical applications of concurrent EEG-fMRI. PMID:28552957

Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

PubMed Central

Huang, Chih-Sheng; Yang, Wen-Yu; Chuang, Chun-Hsiang; Wang, Yu-Kai

2018-01-01

Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research. PMID:29599950

Independent component analysis separates spikes of different origin in the EEG.

PubMed

Urrestarazu, Elena; Iriarte, Jorge; Artieda, Julio; Alegre, Manuel; Valencia, Miguel; Viteri, César

2006-02-01

Independent component analysis (ICA) is a novel system that finds independent sources in recorded signals. Its usefulness in separating epileptiform activity of different origin has not been determined. The goal of this study was to demonstrate that ICA is useful for separating different spikes using samples of EEG of patients with focal epilepsy. Digital EEG samples from four patients with focal epilepsy were included. The patients had temporal (n = 2), centrotemporal (n = 1) or frontal spikes (n = 1). Twenty-six samples with two (or more) spikes from two different patients were created. The selection of the two spikes for each mixed EEG was performed randomly, trying to have all the different combinations and rejecting the mixture of two spikes from the same patient. Two different examiners studied the EEGs using ICA with JADE paradigm in Matlab platform, trying to separate and to identify the spikes. They agreed in the correct separation of the spikes in 24 of the 26 samples, classifying the spikes as frontal, temporal or centrotemporal, left or right sided. The demonstration of the possibility of detecting different artificially mixed spikes confirms that ICA may be useful in separating spikes or other elements in real EEGs.

Data acquisition instrument for EEG based on embedded system

NASA Astrophysics Data System (ADS)

Toresano, La Ode Husein Z.; Wijaya, Sastra Kusuma; Prawito, Sudarmaji, Arief; Syakura, Abdan; Badri, Cholid

2017-02-01

An electroencephalogram (EEG) is a device for measuring and recording the electrical activity of brain. The EEG data of signal can be used as a source of analysis for human brain function. The purpose of this study was to design a portable multichannel EEG based on embedded system and ADS1299. The ADS1299 is an analog front-end to be used as an Analog to Digital Converter (ADC) to convert analog signal of electrical activity of brain, a filter of electrical signal to reduce the noise on low-frequency band and a data communication to the microcontroller. The system has been tested to capture brain signal within a range of 1-20 Hz using the NETECH EEG simulator 330. The developed system was relatively high accuracy of more than 82.5%. The EEG Instrument has been successfully implemented to acquire the brain signal activity using a PC (Personal Computer) connection for displaying the recorded data. The final result of data acquisition has been processed using OpenBCI GUI (Graphical User Interface) based through real-time process for 8-channel signal acquisition, brain-mapping and power spectral decomposition signal using the standard FFT (Fast Fourier Transform) algorithm.

Multimodal neuroimaging evidence linking memory and attention systems during visual search cued by context.

PubMed

Kasper, Ryan W; Grafton, Scott T; Eckstein, Miguel P; Giesbrecht, Barry

2015-03-01

Visual search can be facilitated by the learning of spatial configurations that predict the location of a target among distractors. Neuropsychological and functional magnetic resonance imaging (fMRI) evidence implicates the medial temporal lobe (MTL) memory system in this contextual cueing effect, and electroencephalography (EEG) studies have identified the involvement of visual cortical regions related to attention. This work investigated two questions: (1) how memory and attention systems are related in contextual cueing; and (2) how these systems are involved in both short- and long-term contextual learning. In one session, EEG and fMRI data were acquired simultaneously in a contextual cueing task. In a second session conducted 1 week later, EEG data were recorded in isolation. The fMRI results revealed MTL contextual modulations that were correlated with short- and long-term behavioral context enhancements and attention-related effects measured with EEG. An fMRI-seeded EEG source analysis revealed that the MTL contributed the most variance to the variability in the attention enhancements measured with EEG. These results support the notion that memory and attention systems interact to facilitate search when spatial context is implicitly learned. © 2015 New York Academy of Sciences.

The Smartphone Brain Scanner: A Portable Real-Time Neuroimaging System

PubMed Central

Stopczynski, Arkadiusz; Stahlhut, Carsten; Larsen, Jakob Eg; Petersen, Michael Kai; Hansen, Lars Kai

2014-01-01

Combining low-cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. Here we present the technical details and validation of a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system – Smartphone Brain Scanner – combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully portable system for real-time 3D EEG imaging. We discuss the benefits and challenges, including technical limitations as well as details of real-time reconstruction of 3D images of brain activity. We present examples of brain activity captured in a simple experiment involving imagined finger tapping, which shows that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using an off-the-shelf consumer neuroheadset is lower than the signal obtained using high-density standard EEG equipment, we propose mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings. PMID:24505263

Classification of EEG abnormalities in partial epilepsy with simultaneous EEG-fMRI recordings.

PubMed

Pedreira, C; Vaudano, A E; Thornton, R C; Chaudhary, U J; Vulliemoz, S; Laufs, H; Rodionov, R; Carmichael, D W; Lhatoo, S D; Guye, M; Quian Quiroga, R; Lemieux, L

2014-10-01

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the "irritative zone" (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram-functional Magnetic Resonance Imagining (EEG-fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG-fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG-fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy. Copyright © 2014 Elsevier Inc. All rights reserved.