Hardware enhance of brain computer interfaces

NASA Astrophysics Data System (ADS)

Wu, Jerry; Szu, Harold; Chen, Yuechen; Guo, Ran; Gu, Xixi

2015-05-01

The history of brain-computer interfaces (BCIs) starts with Hans Berger's discovery of the electrical activity of the human brain and the development of electroencephalography (EEG). Recent years, BCI researches are focused on Invasive, Partially invasive, and Non-invasive BCI. Furthermore, EEG can be also applied to telepathic communication which could provide the basis for brain-based communication using imagined speech. It is possible to use EEG signals to discriminate the vowels and consonants embedded in spoken and in imagined words and apply to military product. In this report, we begin with an example of using high density EEG with high electrode density and analysis the results by using BCIs. The BCIs in this work is enhanced by A field-programmable gate array (FPGA) board with optimized two dimension (2D) image Fast Fourier Transform (FFT) analysis.

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

Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans

PubMed Central

Kim, Hyoungkyu; Hudetz, Anthony G.; Lee, Joseph; Mashour, George A.; Lee, UnCheol; Avidan, Michael S.

2018-01-01

The integrated information theory (IIT) proposes a quantitative measure, denoted as Φ, of the amount of integrated information in a physical system, which is postulated to have an identity relationship with consciousness. IIT predicts that the value of Φ estimated from brain activities represents the level of consciousness across phylogeny and functional states. Practical limitations, such as the explosive computational demands required to estimate Φ for real systems, have hindered its application to the brain and raised questions about the utility of IIT in general. To achieve practical relevance for studying the human brain, it will be beneficial to establish the reliable estimation of Φ from multichannel electroencephalogram (EEG) and define the relationship of Φ to EEG properties conventionally used to define states of consciousness. In this study, we introduce a practical method to estimate Φ from high-density (128-channel) EEG and determine the contribution of each channel to Φ. We examine the correlation of power, frequency, functional connectivity, and modularity of EEG with regional Φ in various states of consciousness as modulated by diverse anesthetics. We find that our approximation of Φ alone is insufficient to discriminate certain states of anesthesia. However, a multi-dimensional parameter space extended by four parameters related to Φ and EEG connectivity is able to differentiate all states of consciousness. The association of Φ with EEG connectivity during clinically defined anesthetic states represents a new practical approach to the application of IIT, which may be used to characterize various physiological (sleep), pharmacological (anesthesia), and pathological (coma) states of consciousness in the human brain. PMID:29503611

Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans.

PubMed

Kim, Hyoungkyu; Hudetz, Anthony G; Lee, Joseph; Mashour, George A; Lee, UnCheol

2018-01-01

The integrated information theory (IIT) proposes a quantitative measure, denoted as Φ, of the amount of integrated information in a physical system, which is postulated to have an identity relationship with consciousness. IIT predicts that the value of Φ estimated from brain activities represents the level of consciousness across phylogeny and functional states. Practical limitations, such as the explosive computational demands required to estimate Φ for real systems, have hindered its application to the brain and raised questions about the utility of IIT in general. To achieve practical relevance for studying the human brain, it will be beneficial to establish the reliable estimation of Φ from multichannel electroencephalogram (EEG) and define the relationship of Φ to EEG properties conventionally used to define states of consciousness. In this study, we introduce a practical method to estimate Φ from high-density (128-channel) EEG and determine the contribution of each channel to Φ. We examine the correlation of power, frequency, functional connectivity, and modularity of EEG with regional Φ in various states of consciousness as modulated by diverse anesthetics. We find that our approximation of Φ alone is insufficient to discriminate certain states of anesthesia. However, a multi-dimensional parameter space extended by four parameters related to Φ and EEG connectivity is able to differentiate all states of consciousness. The association of Φ with EEG connectivity during clinically defined anesthetic states represents a new practical approach to the application of IIT, which may be used to characterize various physiological (sleep), pharmacological (anesthesia), and pathological (coma) states of consciousness in the human brain.

Automated detection and labeling of high-density EEG electrodes from structural MR images.

PubMed

Marino, Marco; Liu, Quanying; Brem, Silvia; Wenderoth, Nicole; Mantini, Dante

2016-10-01

Accurate knowledge about the positions of electrodes in electroencephalography (EEG) is very important for precise source localizations. Direct detection of electrodes from magnetic resonance (MR) images is particularly interesting, as it is possible to avoid errors of co-registration between electrode and head coordinate systems. In this study, we propose an automated MR-based method for electrode detection and labeling, particularly tailored to high-density montages. Anatomical MR images were processed to create an electrode-enhanced image in individual space. Image processing included intensity non-uniformity correction, background noise and goggles artifact removal. Next, we defined a search volume around the head where electrode positions were detected. Electrodes were identified as local maxima in the search volume and registered to the Montreal Neurological Institute standard space using an affine transformation. This allowed the matching of the detected points with the specific EEG montage template, as well as their labeling. Matching and labeling were performed by the coherent point drift method. Our method was assessed on 8 MR images collected in subjects wearing a 256-channel EEG net, using the displacement with respect to manually selected electrodes as performance metric. Average displacement achieved by our method was significantly lower compared to alternative techniques, such as the photogrammetry technique. The maximum displacement was for more than 99% of the electrodes lower than 1 cm, which is typically considered an acceptable upper limit for errors in electrode positioning. Our method showed robustness and reliability, even in suboptimal conditions, such as in the case of net rotation, imprecisely gathered wires, electrode detachment from the head, and MR image ghosting. We showed that our method provides objective, repeatable and precise estimates of EEG electrode coordinates. We hope our work will contribute to a more widespread use of high-density EEG as a brain-imaging tool.

Automated detection and labeling of high-density EEG electrodes from structural MR images

NASA Astrophysics Data System (ADS)

Marino, Marco; Liu, Quanying; Brem, Silvia; Wenderoth, Nicole; Mantini, Dante

2016-10-01

Objective. Accurate knowledge about the positions of electrodes in electroencephalography (EEG) is very important for precise source localizations. Direct detection of electrodes from magnetic resonance (MR) images is particularly interesting, as it is possible to avoid errors of co-registration between electrode and head coordinate systems. In this study, we propose an automated MR-based method for electrode detection and labeling, particularly tailored to high-density montages. Approach. Anatomical MR images were processed to create an electrode-enhanced image in individual space. Image processing included intensity non-uniformity correction, background noise and goggles artifact removal. Next, we defined a search volume around the head where electrode positions were detected. Electrodes were identified as local maxima in the search volume and registered to the Montreal Neurological Institute standard space using an affine transformation. This allowed the matching of the detected points with the specific EEG montage template, as well as their labeling. Matching and labeling were performed by the coherent point drift method. Our method was assessed on 8 MR images collected in subjects wearing a 256-channel EEG net, using the displacement with respect to manually selected electrodes as performance metric. Main results. Average displacement achieved by our method was significantly lower compared to alternative techniques, such as the photogrammetry technique. The maximum displacement was for more than 99% of the electrodes lower than 1 cm, which is typically considered an acceptable upper limit for errors in electrode positioning. Our method showed robustness and reliability, even in suboptimal conditions, such as in the case of net rotation, imprecisely gathered wires, electrode detachment from the head, and MR image ghosting. Significance. We showed that our method provides objective, repeatable and precise estimates of EEG electrode coordinates. We hope our work will contribute to a more widespread use of high-density EEG as a brain-imaging tool.

Exploration of Lower Frequency EEG Dynamics and Cortical Alpha Asymmetry in Long-term Rajyoga Meditators

PubMed Central

Sharma, Kanishka; Chandra, Sushil; Dubey, Ashok Kumar

2018-01-01

Background: Rajyoga meditation is taught by Prajapita Brahmakumaris World Spiritual University (Brahmakumaris) and has been followed by more than one million followers across the globe. However, rare studies were conducted on physiological aspects of rajyoga meditation using electroencephalography (EEG). Band power and cortical asymmetry were not studied with Rajyoga meditators. Aims: This study aims to investigate the effect of regular meditation practice on EEG brain dynamics in low-frequency bands of long-term Rajyoga meditators. Settings and Design: Subjects were matched for age in both groups. Lower frequency EEG bands were analyzed in resting and during meditation. Materials and Methods: Twenty-one male long-term meditators (LTMs) and same number of controls were selected to participate in study as par inclusion criteria. Semi high-density EEG was recorded before and during meditation in LTM group and resting in control group. The main outcome of the study was spectral power of alpha and theta bands and cortical (hemispherical) asymmetry calculated using band power. Statistical Analysis: One-way ANOVA was performed to find the significant difference between EEG spectral properties of groups. Pearson's Chi-square test was used to find difference among demographics data. Results: Results reveal high-band power in alpha and theta spectra in meditators. Cortical asymmetry calculated through EEG power was also found to be high in frontal as well as parietal channels. However, no correlation was seen between the experience of meditation (years, hours) practice and EEG indices. Conclusion: Overall findings indicate contribution of smaller frequencies (alpha and theta) while maintaining meditative experience. This suggests a positive impact of meditation on frontal and parietal areas of brain, involved in the processes of regulation of selective and sustained attention as well as provide evidence about their involvement in emotion and cognitive processing. PMID:29343928

Gender differences in association between serotonin transporter gene polymorphism and resting-state EEG activity.

PubMed

Volf, N V; Belousova, L V; Knyazev, G G; Kulikov, A V

2015-01-22

Human brain oscillations represent important features of information processing and are highly heritable. Gender has been observed to affect association between the 5-HTTLPR (serotonin-transporter-linked polymorphic region) polymorphism and various endophenotypes. This study aimed to investigate the effects of 5-HTTLPR on the spontaneous electroencephalography (EEG) activity in healthy male and female subjects. DNA samples extracted from buccal swabs and resting EEG recorded at 60 standard leads were collected from 210 (101 men and 109 women) volunteers. Spectral EEG power estimates and cortical sources of EEG activity were investigated. It was shown that effects of 5-HTTLPR polymorphism on electrical activity of the brain vary as a function of gender. Women with the S/L genotype had greater global EEG power compared to men with the same genotype. In men, current source density was markedly different among genotype groups in only alpha 2 and alpha 3 frequency ranges: S/S allele carriers had higher current source density estimates in the left inferior parietal lobule in comparison with the L/L group. In women, genotype difference in global power asymmetry was found in the central-temporal region. Contrasting L/L and S/L genotype carriers also yielded significant effects in the right hemisphere inferior parietal lobule and the right postcentral gyrus with L/L genotype carriers showing lower current source density estimates than S/L genotype carriers in all but gamma bands. So, in women, the effects of 5-HTTLPR polymorphism were associated with modulation of the EEG activity in a wide range of EEG frequencies. The significance of the results lies in the demonstration of gene by sex interaction with resting EEG that has implications for understanding sex-related differences in affective states, emotion and cognition. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Reliability of quantitative EEG (qEEG) measures and LORETA current source density at 30 days.

PubMed

Cannon, Rex L; Baldwin, Debora R; Shaw, Tiffany L; Diloreto, Dominic J; Phillips, Sherman M; Scruggs, Annie M; Riehl, Timothy C

2012-06-14

There is a growing interest for using quantitative EEG and LORETA current source density in clinical and research settings. Importantly, if these indices are to be employed in clinical settings then the reliability of these measures is of great concern. Neuroguide (Applied Neurosciences) is sophisticated software developed for the analyses of power, and connectivity measures of the EEG as well as LORETA current source density. To date there are relatively few data evaluating topographical EEG reliability contrasts for all 19 channels and no studies have evaluated reliability for LORETA calculations. We obtained 4 min eyes-closed and eyes-opened EEG recordings at 30-day intervals. The EEG was analyzed in Neuroguide and FFT power, coherence and phase was computed for traditional frequency bands (delta, theta, alpha and beta) and LORETA current source density was calculated in 1 Hz increments and summed for total power in eight regions of interest (ROI). In order to obtain a robust measure of reliability we utilized a random effects model with an absolute agreement definition. The results show very good reproducibility for total absolute power and coherence. Phase shows lower reliability coefficients. LORETA current source density shows very good reliability with an average 0.81 for ECB and 0.82 for EOB. Similarly, the eight regions of interest show good to very good agreement across time. Implications for future directions and use of qEEG and LORETA in clinical populations are discussed. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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.

Local and Widely Distributed EEG Activity in Schizophrenia With Prevalence of Negative Symptoms.

PubMed

Grin-Yatsenko, Vera A; Ponomarev, Valery A; Pronina, Marina V; Poliakov, Yury I; Plotnikova, Irina V; Kropotov, Juri D

2017-09-01

We evaluated EEG frequency abnormalities in resting state (eyes closed and eyes open) EEG in a group of chronic schizophrenia patients as compared with healthy subjects. The study included 3 methods of analysis of deviation of EEG characteristics: genuine EEG, current source density (CSD), and group independent component (gIC). All 3 methods have shown that the EEG in schizophrenia patients is characterized by enhanced low-frequency (delta and theta) and high-frequency (beta) activity in comparison with the control group. However, the spatial pattern of differences was dependent on the type of method used. Comparative analysis has shown that increased EEG power in schizophrenia patients apparently concerns both widely spatially distributed components and local components of signal. Furthermore, the observed differences in the delta and theta range can be described mainly by the local components, and those in the beta range mostly by spatially widely distributed ones. The possible nature of the widely distributed activity is discussed.

Psychogenic seizures and frontal disconnection: EEG synchronisation study.

PubMed

Knyazeva, Maria G; Jalili, Mahdi; Frackowiak, Richard S; Rossetti, Andrea O

2011-05-01

Psychogenic non-epileptic seizures (PNES) are paroxysmal events that, in contrast to epileptic seizures, are related to psychological causes without the presence of epileptiform EEG changes. Recent models suggest a multifactorial basis for PNES. A potentially paramount, but currently poorly understood factor is the interplay between psychiatric features and a specific vulnerability of the brain leading to a clinical picture that resembles epilepsy. Hypothesising that functional cerebral network abnormalities may predispose to the clinical phenotype, the authors undertook a characterisation of the functional connectivity in PNES patients. The authors analysed the whole-head surface topography of multivariate phase synchronisation (MPS) in interictal high-density EEG of 13 PNES patients as compared with 13 age- and sex-matched controls. MPS mapping reduces the wealth of dynamic data obtained from high-density EEG to easily readable synchronisation maps, which provide an unbiased overview of any changes in functional connectivity associated with distributed cortical abnormalities. The authors computed MPS maps for both Laplacian and common-average-reference EEGs. In a between-group comparison, only patchy, non-uniform changes in MPS survived conservative statistical testing. However, against the background of these unimpressive group results, the authors found widespread inverse correlations between individual PNES frequency and MPS within the prefrontal and parietal cortices. PNES appears to be associated with decreased prefrontal and parietal synchronisation, possibly reflecting dysfunction of networks within these regions.

Regional Patterns of Elevated Alpha and High-Frequency Electroencephalographic Activity during Nonrapid Eye Movement Sleep in Chronic Insomnia: A Pilot Study

PubMed Central

Riedner, Brady A.; Goldstein, Michael R.; Plante, David T.; Rumble, Meredith E.; Ferrarelli, Fabio; Tononi, Giulio; Benca, Ruth M.

2016-01-01

Study Objectives: To examine nonrapid eye movement (NREM) sleep in insomnia using high-density electroencephalography (EEG). Methods: All-night sleep recordings with 256 channel high-density EEG were analyzed for 8 insomnia subjects (5 females) and 8 sex and age-matched controls without sleep complaints. Spectral analyses were conducted using unpaired t-tests and topographical differences between groups were assessed using statistical non-parametric mapping. Five minute segments of deep NREM sleep were further analyzed using sLORETA cortical source imaging. Results: The initial topographic analysis of all-night NREM sleep EEG revealed that insomnia subjects had more high-frequency EEG activity (> 16 Hz) compared to good sleeping controls and that the difference between groups was widespread across the scalp. In addition, the analysis also showed that there was a more circumscribed difference in theta (4–8 Hz) and alpha (8–12 Hz) power bands between groups. When deep NREM sleep (N3) was examined separately, the high-frequency difference between groups diminished, whereas the higher regional alpha activity in insomnia subjects persisted. Source imaging analysis demonstrated that sensory and sensorimotor cortical areas consistently exhibited elevated levels of alpha activity during deep NREM sleep in insomnia subjects relative to good sleeping controls. Conclusions: These results suggest that even during the deepest stage of sleep, sensory and sensorimotor areas in insomnia subjects may still be relatively active compared to control subjects and to the rest of the sleeping brain. Citation: Riedner BA, Goldstein MR, Plante DT, Rumble ME, Ferrarelli F, Tononi G, Benca RM. Regional patterns of elevated alpha and high-frequency electroencephalographic activity during nonrapid eye movement sleep in chronic insomnia: a pilot study. SLEEP 2016;39(4):801–812. PMID:26943465

Topographic and sex-related differences in sleep spindles in major depressive disorder: a high-density EEG investigation.

PubMed

Plante, D T; Goldstein, M R; Landsness, E C; Peterson, M J; Riedner, B A; Ferrarelli, F; Wanger, T; Guokas, J J; Tononi, G; Benca, R M

2013-03-20

Sleep spindles are believed to mediate several sleep-related functions including maintaining disconnection from the external environment during sleep, cortical development, and sleep-dependent memory consolidation. Prior studies that have examined sleep spindles in major depressive disorder (MDD) have not demonstrated consistent differences relative to control subjects, which may be due to sex-related variation and limited spatial resolution of spindle detection. Thus, this study sought to characterize sleep spindles in MDD using high-density electroencephalography (hdEEG) to examine the topography of sleep spindles across the cortex in MDD, as well as sex-related variation in spindle topography in the disorder. All-night hdEEG recordings were collected in 30 unipolar MDD participants (19 women) and 30 age and sex-matched controls. Topography of sleep spindle density, amplitude, duration, and integrated spindle activity (ISA) were assessed to determine group differences. Spindle parameters were compared between MDD and controls, including analysis stratified by sex. As a group, MDD subjects demonstrated significant increases in frontal and parietal spindle density and ISA compared to controls. When stratified by sex, MDD women demonstrated increases in frontal and parietal spindle density, amplitude, duration, and ISA; whereas MDD men demonstrated either no differences or decreases in spindle parameters. Given the number of male subjects, this study may be underpowered to detect differences in spindle parameters in male MDD participants. This study demonstrates topographic and sex-related differences in sleep spindles in MDD. Further research is warranted to investigate the role of sleep spindles and sex in the pathophysiology of MDD. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

MRI with and without a high-density EEG cap--what makes the difference?

PubMed

Klein, Carina; Hänggi, Jürgen; Luechinger, Roger; Jäncke, Lutz

2015-02-01

Besides the benefit of combining electroencephalography (EEG) and magnetic resonance imaging (MRI), much effort has been spent to develop algorithms aimed at successfully cleaning the EEG data from MRI-related gradient and ballistocardiological artifacts. However, there are also studies showing a negative influence of the EEG on MRI data quality. Therefore, in the present study, we focused for the first time on the influence of the EEG on morphometric measurements of T1-weighted MRI data (voxel- and surfaced-based morphometry). Here, we demonstrate a strong influence of the EEG on cortical thickness, surface area, and volume as well as subcortical volumes due to local EEG-related inhomogeneities of the static magnetic (B0) and the gradient field (B1). In a second step, we analyzed the signal-to-noise ratios for both the anatomical and the functional data when recorded simultaneously with EEG and MRI and compared them to the ratios of the MRI data without simultaneous EEG measurements. These analyses revealed consistently lower signal-to-noise ratios for anatomical as well as functional MRI data during simultaneous EEG registration. In contrast, further analyses of T2*-weighted images provided reliable results independent of whether including the individuals' T1-weighted image with or without the EEG cap in the fMRI preprocessing stream. Based on our findings, we strongly recommend against using the structural images obtained during simultaneous EEG-MRI recordings for further anatomical data analysis. Copyright © 2014 Elsevier Inc. All rights reserved.

Removing ballistocardiogram (BCG) artifact from full-scalp EEG acquired inside the MR scanner with Orthogonal Matching Pursuit (OMP)

PubMed Central

Xia, Hongjing; Ruan, Dan; Cohen, Mark S.

2014-01-01

Ballistocardiogram (BCG) artifact remains a major challenge that renders electroencephalographic (EEG) signals hard to interpret in simultaneous EEG and functional MRI (fMRI) data acquisition. Here, we propose an integrated learning and inference approach that takes advantage of a commercial high-density EEG cap, to estimate the BCG contribution in noisy EEG recordings from inside the MR scanner. To estimate reliably the full-scalp BCG artifacts, a near-optimal subset (20 out of 256) of channels first was identified using a modified recording setup. In subsequent recordings inside the MR scanner, BCG-only signal from this subset of channels was used to generate continuous estimates of the full-scalp BCG artifacts via inference, from which the intended EEG signal was recovered. The reconstruction of the EEG was performed with both a direct subtraction and an optimization scheme. We evaluated the performance on both synthetic and real contaminated recordings, and compared it to the benchmark Optimal Basis Set (OBS) method. In the challenging non-event-related-potential (non-ERP) EEG studies, our reconstruction can yield more than fourteen-fold improvement in reducing the normalized RMS error of EEG signals, compared to OBS. PMID:25120421

Regional Patterns of Elevated Alpha and High-Frequency Electroencephalographic Activity during Nonrapid Eye Movement Sleep in Chronic Insomnia: A Pilot Study.

PubMed

Riedner, Brady A; Goldstein, Michael R; Plante, David T; Rumble, Meredith E; Ferrarelli, Fabio; Tononi, Giulio; Benca, Ruth M

2016-04-01

To examine nonrapid eye movement (NREM) sleep in insomnia using high-density electroencephalography (EEG). All-night sleep recordings with 256 channel high-density EEG were analyzed for 8 insomnia subjects (5 females) and 8 sex and age-matched controls without sleep complaints. Spectral analyses were conducted using unpaired t-tests and topographical differences between groups were assessed using statistical non-parametric mapping. Five minute segments of deep NREM sleep were further analyzed using sLORETA cortical source imaging. The initial topographic analysis of all-night NREM sleep EEG revealed that insomnia subjects had more high-frequency EEG activity (> 16 Hz) compared to good sleeping controls and that the difference between groups was widespread across the scalp. In addition, the analysis also showed that there was a more circumscribed difference in theta (4-8 Hz) and alpha (8-12 Hz) power bands between groups. When deep NREM sleep (N3) was examined separately, the high-frequency difference between groups diminished, whereas the higher regional alpha activity in insomnia subjects persisted. Source imaging analysis demonstrated that sensory and sensorimotor cortical areas consistently exhibited elevated levels of alpha activity during deep NREM sleep in insomnia subjects relative to good sleeping controls. These results suggest that even during the deepest stage of sleep, sensory and sensorimotor areas in insomnia subjects may still be relatively active compared to control subjects and to the rest of the sleeping brain. © 2016 Associated Professional Sleep Societies, LLC.

Brain Oscillations in Sport: Toward EEG Biomarkers of Performance.

PubMed

Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard

2016-01-01

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.

Brain Oscillations in Sport: Toward EEG Biomarkers of Performance

PubMed Central

Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard

2016-01-01

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators. PMID:26955362

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.

Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.

PubMed

Bascil, M Serdar; Tesneli, Ahmet Y; Temurtas, Feyzullah

2016-09-01

Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha-beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.

Altered slow wave activity in major depressive disorder with hypersomnia: a high density EEG pilot study

PubMed Central

Plante, David T.; Landsness, Eric C.; Peterson, Michael J.; Goldstein, Michael R.; Wanger, Tim; Guokas, Jeff J.; Tononi, Giulio; Benca, Ruth M.

2012-01-01

Hypersomnolence in major depressive disorder (MDD) plays an important role in the natural history of the disorder, but the basis of hypersomnia in MDD is poorly understood. Slow wave activity (SWA) has been associated with sleep homeostasis, as well as sleep restoration and maintenance, and may be altered in MDD. Therefore, we conducted a post-hoc study that utilized high density electroencephalography (hdEEG) to test the hypothesis that MDD subjects with hypersomnia (HYS+) would have decreased SWA relative to age and sex-matched MDD subjects without hypersomnia (HYS−) and healthy controls (n=7 for each group). After correcting for multiple comparisons using statistical non-parametric mapping, HYS+ subjects demonstrated significantly reduced parieto-occipital all-night SWA relative to HYS− subjects. Our results suggest hypersomnolence may be associated with topographic reductions in SWA in MDD. Further research using adequately powered prospective design is indicated to confirm these findings. PMID:22512951

Identifying auditory attention with ear-EEG: cEEGrid versus high-density cap-EEG comparison

NASA Astrophysics Data System (ADS)

Bleichner, Martin G.; Mirkovic, Bojana; Debener, Stefan

2016-12-01

Objective. This study presents a direct comparison of a classical EEG cap setup with a new around-the-ear electrode array (cEEGrid) to gain a better understanding of the potential of ear-centered EEG. Approach. Concurrent EEG was recorded from a classical scalp EEG cap and two cEEGrids that were placed around the left and the right ear. Twenty participants performed a spatial auditory attention task in which three sound streams were presented simultaneously. The sound streams were three seconds long and differed in the direction of origin (front, left, right) and the number of beats (3, 4, 5 respectively), as well as the timbre and pitch. The participants had to attend to either the left or the right sound stream. Main results. We found clear attention modulated ERP effects reflecting the attended sound stream for both electrode setups, which agreed in morphology and effect size. A single-trial template matching classification showed that the direction of attention could be decoded significantly above chance (50%) for at least 16 out of 20 participants for both systems. The comparably high classification results of the single trial analysis underline the quality of the signal recorded with the cEEGrids. Significance. These findings are further evidence for the feasibility of around the-ear EEG recordings and demonstrate that well described ERPs can be measured. We conclude that concealed behind-the-ear EEG recordings can be an alternative to classical cap EEG acquisition for auditory attention monitoring.

Identifying auditory attention with ear-EEG: cEEGrid versus high-density cap-EEG comparison.

PubMed

Bleichner, Martin G; Mirkovic, Bojana; Debener, Stefan

2016-12-01

This study presents a direct comparison of a classical EEG cap setup with a new around-the-ear electrode array (cEEGrid) to gain a better understanding of the potential of ear-centered EEG. Concurrent EEG was recorded from a classical scalp EEG cap and two cEEGrids that were placed around the left and the right ear. Twenty participants performed a spatial auditory attention task in which three sound streams were presented simultaneously. The sound streams were three seconds long and differed in the direction of origin (front, left, right) and the number of beats (3, 4, 5 respectively), as well as the timbre and pitch. The participants had to attend to either the left or the right sound stream. We found clear attention modulated ERP effects reflecting the attended sound stream for both electrode setups, which agreed in morphology and effect size. A single-trial template matching classification showed that the direction of attention could be decoded significantly above chance (50%) for at least 16 out of 20 participants for both systems. The comparably high classification results of the single trial analysis underline the quality of the signal recorded with the cEEGrids. These findings are further evidence for the feasibility of around the-ear EEG recordings and demonstrate that well described ERPs can be measured. We conclude that concealed behind-the-ear EEG recordings can be an alternative to classical cap EEG acquisition for auditory attention monitoring.

Regional differences in trait-like characteristics of the waking EEG in early adolescence.

PubMed

Benz, Dominik C; Tarokh, Leila; Achermann, Peter; Loughran, Sarah P

2013-10-09

The human waking EEG spectrum shows high heritability and stability and, despite maturational cortical changes, high test-retest reliability in children and teens. These phenomena have also been shown to be region specific. We examined the stability of the morphology of the wake EEG spectrum in children aged 11 to 13 years recorded over weekly intervals and assessed whether the waking EEG spectrum in children may also be trait-like. Three minutes of eyes open and three minutes of eyes closed waking EEG was recorded in 22 healthy children once a week for three consecutive weeks. Eyes open and closed EEG power density spectra were calculated for two central (C3LM and C4LM) and two occipital (O1LM and O2LM) derivations. A hierarchical cluster analysis was performed to determine whether the morphology of the waking EEG spectrum between 1 and 20 Hz is trait-like. We also examined the stability of the alpha peak using an ANOVA. The morphology of the EEG spectrum recorded from central derivations was highly stable and unique to an individual (correctly classified in 85% of participants), while the EEG recorded from occipital derivations, while stable, was much less unique across individuals (correctly classified in 42% of participants). Furthermore, our analysis revealed an increase in alpha peak height concurrent with a decline in the frequency of the alpha peak across weeks for occipital derivations. No changes in either measure were observed in the central derivations. Our results indicate that across weekly recordings, power spectra at central derivations exhibit more "trait-like" characteristics than occipital derivations. These results may be relevant for future studies searching for links between phenotypes, such as psychiatric diagnoses, and the underlying genes (i.e., endophenotypes) by suggesting that such studies should make use of more anterior rather than posterior EEG derivations.

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.

Validation of the Karolinska sleepiness scale against performance and EEG variables.

PubMed

Kaida, Kosuke; Takahashi, Masaya; Akerstedt, Torbjörn; Nakata, Akinori; Otsuka, Yasumasa; Haratani, Takashi; Fukasawa, Kenji

2006-07-01

The Karolinska sleepiness scale (KSS) is frequently used for evaluating subjective sleepiness. The main aim of the present study was to investigate the validity and reliability of the KSS with electroencephalographic, behavioral and other subjective indicators of sleepiness. Participants were 16 healthy females aged 33-43 (38.1+/-2.68) years. The experiment involved 8 measurement sessions per day for 3 consecutive days. Each session contained the psychomotor vigilance task (PVT), the Karolinska drowsiness test (KDT-EEG alpha & theta power), the alpha attenuation test (AAT-alpha power ratio open/closed eyes) and the KSS. Median reaction time, number of lapses, alpha and theta power density and the alpha attenuation coefficients (AAC) showed highly significant increase with increasing KSS. The same variables were also significantly correlated with KSS, with a mean value for lapses (r=0.56). The KSS was closely related to EEG and behavioral variables, indicating a high validity in measuring sleepiness. KSS ratings may be a useful proxy for EEG or behavioral indicators of sleepiness.

Household wireless electroencephalogram hat

NASA Astrophysics Data System (ADS)

Szu, Harold; Hsu, Charles; Moon, Gyu; Yamakawa, Takeshi; Tran, Binh

2012-06-01

We applied Compressive Sensing to design an affordable, convenient Brain Machine Interface (BMI) measuring the high spatial density, and real-time process of Electroencephalogram (EEG) brainwaves by a Smartphone. It is useful for therapeutic and mental health monitoring, learning disability biofeedback, handicap interfaces, and war gaming. Its spec is adequate for a biomedical laboratory, without the cables hanging over the head and tethered to a fixed computer terminal. Our improved the intrinsic signal to noise ratio (SNR) by using the non-uniform placement of the measuring electrodes to create the proximity of measurement to the source effect. We computing a spatiotemporal average the larger magnitude of EEG data centers in 0.3 second taking on tethered laboratory data, using fuzzy logic, and computing the inside brainwave sources, by Independent Component Analysis (ICA). Consequently, we can overlay them together by non-uniform electrode distribution enhancing the signal noise ratio and therefore the degree of sparseness by threshold. We overcame the conflicting requirements between a high spatial electrode density and precise temporal resolution (beyond Event Related Potential (ERP) P300 brainwave at 0.3 sec), and Smartphone wireless bottleneck of spatiotemporal throughput rate. Our main contribution in this paper is the quality and the speed of iterative compressed image recovery algorithm based on a Block Sparse Code (Baranuick et al, IEEE/IT 2008). As a result, we achieved real-time wireless dynamic measurement of EEG brainwaves, matching well with traditionally tethered high density EEG.

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.

Isolating gait-related movement artifacts in electroencephalography during human walking

PubMed Central

Kline, Julia E.; Huang, Helen J.; Snyder, Kristine L.; Ferris, Daniel P.

2016-01-01

Objective High-density electroencephelography (EEG) can provide insight into human brain function during real-world activities with walking. Some recent studies have used EEG to characterize brain activity during walking, but the relative contributions of movement artifact and electrocortical activity have been difficult to quantify. We aimed to characterize movement artifact recorded by EEG electrodes at a range of walking speeds and to test the efficacy of artifact removal methods. We also quantified the similarity between movement artifact recorded by EEG electrodes and a head-mounted accelerometer. Approach We used a novel experimental method to isolate and record movement artifact with EEG electrodes during walking. We blocked electrophysiological signals using a nonconductive layer (silicone swim cap) and simulated an electrically conductive scalp on top of the swim cap using a wig coated with conductive gel. We recorded motion artifact EEG data from nine young human subjects walking on a treadmill at speeds from 0.4–1.6 m/s. We then tested artifact removal methods including moving average and wavelet-based techniques. Main Results Movement artifact recorded with EEG electrodes varied considerably, across speed, subject, and electrode location. The movement artifact measured with EEG electrodes did not correlate well with head acceleration. All of the tested artifact removal methods attenuated low-frequency noise but did not completely remove movement artifact. The spectral power fluctuations in the movement artifact data resembled data from some previously published studies of EEG during walking. Significance Our results suggest that EEG data recorded during walking likely contains substantial movement artifact that: cannot be explained by head accelerations; varies across speed, subject, and channel; and cannot be removed using traditional signal processing methods. Future studies should focus on more sophisticated methods for removing of EEG movement artifact to advance the field. PMID:26083595

Isolating gait-related movement artifacts in electroencephalography during human walking.

PubMed

Kline, Julia E; Huang, Helen J; Snyder, Kristine L; Ferris, Daniel P

2015-08-01

High-density electroencephelography (EEG) can provide an insight into human brain function during real-world activities with walking. Some recent studies have used EEG to characterize brain activity during walking, but the relative contributions of movement artifact and electrocortical activity have been difficult to quantify. We aimed to characterize movement artifact recorded by EEG electrodes at a range of walking speeds and to test the efficacy of artifact removal methods. We also quantified the similarity between movement artifact recorded by EEG electrodes and a head-mounted accelerometer. We used a novel experimental method to isolate and record movement artifact with EEG electrodes during walking. We blocked electrophysiological signals using a nonconductive layer (silicone swim cap) and simulated an electrically conductive scalp on top of the swim cap using a wig coated with conductive gel. We recorded motion artifact EEG data from nine young human subjects walking on a treadmill at speeds from 0.4 to 1.6 m s(-1). We then tested artifact removal methods including moving average and wavelet-based techniques. Movement artifact recorded with EEG electrodes varied considerably, across speed, subject, and electrode location. The movement artifact measured with EEG electrodes did not correlate well with head acceleration. All of the tested artifact removal methods attenuated low-frequency noise but did not completely remove movement artifact. The spectral power fluctuations in the movement artifact data resembled data from some previously published studies of EEG during walking. Our results suggest that EEG data recorded during walking likely contains substantial movement artifact that: cannot be explained by head accelerations; varies across speed, subject, and channel; and cannot be removed using traditional signal processing methods. Future studies should focus on more sophisticated methods for removal of EEG movement artifact to advance the field.

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

Sedation for electroencephalography with dexmedetomidine or chloral hydrate: a comparative study on the qualitative and quantitative electroencephalogram pattern.

PubMed

Fernandes, Magda L; Oliveira, Welser Machado de; Santos, Maria do Carmo Vasconcellos; Gomez, Renato S

2015-01-01

Sedation for electroencephalography in uncooperative patients is a controversial issue because majority of sedatives, hypnotics, and general anesthetics interfere with the brain's electrical activity. Chloral hydrate (CH) is typically used for this sedation, and dexmedetomidine (DEX) was recently tested because preliminary data suggest that this drug does not affect the electroencephalogram (EEG). The aim of the present study was to compare the EEG pattern during DEX or CH sedation to test the hypothesis that both drugs exert similar effects on the EEG. A total of 17 patients underwent 2 EEGs on 2 separate occasions, one with DEX and the other with CH. The EEG qualitative variables included the phases of sleep and the background activity. The EEG quantitative analysis was performed during the first 2 minutes of the second stage of sleep. The EEG quantitative variables included density, duration, and amplitude of the sleep spindles and absolute spectral power. The results showed that the qualitative analysis, density, duration, and amplitude of sleep spindles did not differ between DEX and CH sedation. The power of the slow-frequency bands (δ and θ) was higher with DEX, but the power of the faster-frequency bands (α and β) was higher with CH. The total power was lower with DEX than with CH. The differences of DEX and CH in EEG power did not change the EEG qualitative interpretation, which was similar with the 2 drugs. Other studies comparing natural sleep and sleep induced by these drugs are needed to clarify the clinical relevance of the observed EEG quantitative differences.

Pulse Wave Amplitude Drops during Sleep are Reliable Surrogate Markers of Changes in Cortical Activity

PubMed Central

Delessert, Alexandre; Espa, Fabrice; Rossetti, Andrea; Lavigne, Gilles; Tafti, Mehdi; Heinzer, Raphael

2010-01-01

Background: During sleep, sudden drops in pulse wave amplitude (PWA) measured by pulse oximetry are commonly associated with simultaneous arousals and are thought to result from autonomic vasoconstriction. In the present study, we determine whether PWA drops were associated with changes in cortical activity as determined by EEG spectral analysis. Methods: A 20% decrease in PWA was chosen as a minimum for a drop. A total of 1085 PWA drops from 10 consecutive sleep recordings were analyzed. EEG spectral analysis was performed over 5 consecutive epochs of 5 seconds: 2 before, 1 during, and 2 after the PWA drop. EEG spectral analysis was performed over delta, theta, alpha, sigma, and beta frequency bands. Within each frequency band, power density was compared across the five 5-sec epochs. Presence or absence of visually scored EEG arousals were adjudicated by an investigator blinded to the PWA signal and considered associated with PWA drop if concomitant. Results: A significant increase in EEG power density in all EEG frequency bands was found during PWA drops (P < 0.001) compared to before and after drop. Even in the absence of visually scored arousals, PWA drops were associated with a significant increase in EEG power density (P < 0.001) in most frequency bands. Conclusions: Drops in PWA are associated with a significant increase in EEG power density, suggesting that these events can be used as a surrogate for changes in cortical activity during sleep. This approach may prove of value in scoring respiratory events on limited-channel (type III) portable monitors. Citation: Delessert A; Espa F; Rossetti A; Lavigne G; Tafti M; Heinzer R. Pulse wave amplitude drops during sleep are reliable surrogate markers of changes in cortical activity. SLEEP 2010;33(12):1687-1692. PMID:21120131

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.

Developmental trajectories of EEG sleep slow wave activity as a marker for motor skill development during adolescence: a pilot study.

PubMed

Lustenberger, Caroline; Mouthon, Anne-Laure; Tesler, Noemi; Kurth, Salome; Ringli, Maya; Buchmann, Andreas; Jenni, Oskar G; Huber, Reto

2017-01-01

Reliable markers for brain maturation are important to identify neural deviations that eventually predict the development of mental illnesses. Recent studies have proposed topographical EEG-derived slow wave activity (SWA) during NREM sleep as a mirror of cortical development. However, studies about the longitudinal stability as well as the relationship with behavioral skills are needed before SWA topography may be considered such a reliable marker. We examined six subjects longitudinally (over 5.1 years) using high-density EEG and a visuomotor learning task. All subjects showed a steady increase of SWA at a frontal electrode and a decrease in central electrodes. Despite these large changes in EEG power, SWA topography was relatively stable within each subject during development indicating individual trait-like characteristics. Moreover, the SWA changes in the central cluster were related to the development of specific visuomotor skills. Taken together with the previous work in this domain, our results suggest that EEG sleep SWA represents a marker for motor skill development and further supports the idea that SWA mirrors cortical development during childhood and adolescence. © 2016 Wiley Periodicals, Inc.

Computation of Surface Laplacian for tri-polar ring electrodes on high-density realistic geometry head model.

PubMed

Junwei Ma; Han Yuan; Sunderam, Sridhar; Besio, Walter; Lei Ding

2017-07-01

Neural activity inside the human brain generate electrical signals that can be detected on the scalp. Electroencephalograph (EEG) is one of the most widely utilized techniques helping physicians and researchers to diagnose and understand various brain diseases. Due to its nature, EEG signals have very high temporal resolution but poor spatial resolution. To achieve higher spatial resolution, a novel tri-polar concentric ring electrode (TCRE) has been developed to directly measure Surface Laplacian (SL). The objective of the present study is to accurately calculate SL for TCRE based on a realistic geometry head model. A locally dense mesh was proposed to represent the head surface, where the local dense parts were to match the small structural components in TCRE. Other areas without dense mesh were used for the purpose of reducing computational load. We conducted computer simulations to evaluate the performance of the proposed mesh and evaluated possible numerical errors as compared with a low-density model. Finally, with achieved accuracy, we presented the computed forward lead field of SL for TCRE for the first time in a realistic geometry head model and demonstrated that it has better spatial resolution than computed SL from classic EEG recordings.

An Inflatable and Wearable Wireless System for Making 32-Channel Electroencephalogram Measurements.

PubMed

Yu, Yi-Hsin; Lu, Shao-Wei; Chuang, Chun-Hsiang; King, Jung-Tai; Chang, Che-Lun; Chen, Shi-An; Chen, Sheng-Fu; Lin, Chin-Teng

2016-07-01

Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain-computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG system is effective in measuring audio event-related potential, measuring visual event-related potential, and rapid serial visual presentation. Results of this work demonstrate that the proposed EEG cap system performs well in making EEG measurements and is feasible for practical applications.

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.

The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data.

PubMed

Gabard-Durnam, Laurel J; Mendez Leal, Adriana S; Wilkinson, Carol L; Levin, April R

2018-01-01

Electroenchephalography (EEG) recordings collected with developmental populations present particular challenges from a data processing perspective. These EEGs have a high degree of artifact contamination and often short recording lengths. As both sample sizes and EEG channel densities increase, traditional processing approaches like manual data rejection are becoming unsustainable. Moreover, such subjective approaches preclude standardized metrics of data quality, despite the heightened importance of such measures for EEGs with high rates of initial artifact contamination. There is presently a paucity of automated resources for processing these EEG data and no consistent reporting of data quality measures. To address these challenges, we propose the Harvard Automated Processing Pipeline for EEG (HAPPE) as a standardized, automated pipeline compatible with EEG recordings of variable lengths and artifact contamination levels, including high-artifact and short EEG recordings from young children or those with neurodevelopmental disorders. HAPPE processes event-related and resting-state EEG data from raw files through a series of filtering, artifact rejection, and re-referencing steps to processed EEG suitable for time-frequency-domain analyses. HAPPE also includes a post-processing report of data quality metrics to facilitate the evaluation and reporting of data quality in a standardized manner. Here, we describe each processing step in HAPPE, perform an example analysis with EEG files we have made freely available, and show that HAPPE outperforms seven alternative, widely-used processing approaches. HAPPE removes more artifact than all alternative approaches while simultaneously preserving greater or equivalent amounts of EEG signal in almost all instances. We also provide distributions of HAPPE's data quality metrics in an 867 file dataset as a reference distribution and in support of HAPPE's performance across EEG data with variable artifact contamination and recording lengths. HAPPE software is freely available under the terms of the GNU General Public License at https://github.com/lcnhappe/happe.

The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data

PubMed Central

Gabard-Durnam, Laurel J.; Mendez Leal, Adriana S.; Wilkinson, Carol L.; Levin, April R.

2018-01-01

Electroenchephalography (EEG) recordings collected with developmental populations present particular challenges from a data processing perspective. These EEGs have a high degree of artifact contamination and often short recording lengths. As both sample sizes and EEG channel densities increase, traditional processing approaches like manual data rejection are becoming unsustainable. Moreover, such subjective approaches preclude standardized metrics of data quality, despite the heightened importance of such measures for EEGs with high rates of initial artifact contamination. There is presently a paucity of automated resources for processing these EEG data and no consistent reporting of data quality measures. To address these challenges, we propose the Harvard Automated Processing Pipeline for EEG (HAPPE) as a standardized, automated pipeline compatible with EEG recordings of variable lengths and artifact contamination levels, including high-artifact and short EEG recordings from young children or those with neurodevelopmental disorders. HAPPE processes event-related and resting-state EEG data from raw files through a series of filtering, artifact rejection, and re-referencing steps to processed EEG suitable for time-frequency-domain analyses. HAPPE also includes a post-processing report of data quality metrics to facilitate the evaluation and reporting of data quality in a standardized manner. Here, we describe each processing step in HAPPE, perform an example analysis with EEG files we have made freely available, and show that HAPPE outperforms seven alternative, widely-used processing approaches. HAPPE removes more artifact than all alternative approaches while simultaneously preserving greater or equivalent amounts of EEG signal in almost all instances. We also provide distributions of HAPPE's data quality metrics in an 867 file dataset as a reference distribution and in support of HAPPE's performance across EEG data with variable artifact contamination and recording lengths. HAPPE software is freely available under the terms of the GNU General Public License at https://github.com/lcnhappe/happe. PMID:29535597

Single-trial decoding of auditory novelty responses facilitates the detection of residual consciousness

PubMed Central

King, J.R.; Faugeras, F.; Gramfort, A.; Schurger, A.; El Karoui, I.; Sitt, J.D.; Rohaut, B.; Wacongne, C.; Labyt, E.; Bekinschtein, T.; Cohen, L.; Naccache, L.; Dehaene, S.

2017-01-01

Detecting residual consciousness in unresponsive patients is a major clinical concern and a challenge for theoretical neuroscience. To tackle this issue, we recently designed a paradigm that dissociates two electro-encephalographic (EEG) responses to auditory novelty. Whereas a local change in pitch automatically elicits a mismatch negativity (MMN), a change in global sound sequence leads to a late P300b response. The latter component is thought to be present only when subjects consciously perceive the global novelty. Unfortunately, it can be difficult to detect because individual variability is high, especially in clinical recordings. Here, we show that multivariate pattern classifiers can extract subject-specific EEG patterns and predict single-trial local or global novelty responses. We first validate our method with 38 high-density EEG, MEG and intracranial EEG recordings. We empirically demonstrate that our approach circumvents the issues associated with multiple comparisons and individual variability while improving the statistics. Moreover, we confirm in control subjects that local responses are robust to distraction whereas global responses depend on attention. We then investigate 104 vegetative state (VS), minimally conscious state (MCS) and conscious state (CS) patients recorded with high-density EEG. For the local response, the proportion of significant decoding scores (M = 60%) does not vary with the state of consciousness. By contrast, for the global response, only 14% of the VS patients' EEG recordings presented a significant effect, compared to 31% in MCS patients' and 52% in CS patients'. In conclusion, single-trial multivariate decoding of novelty responses provides valuable information in non-communicating patients and paves the way towards real-time monitoring of the state of consciousness. PMID:23859924

Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures

PubMed Central

Zhang, Tinghe; Mao, Zijing; Xu, Xiaojing; Zhang, Lin; Pack, Daniel J.; Dong, Bing; Huang, Yufei

2018-01-01

Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing indoor environments have become a highly important research topic bearing significant impact in our society. While past research studies have attempted to determine predictors for performance, they do not provide satisfactory prediction ability. Therefore, in this preliminary study, we attempt to predict performance during office-work tasks triggered by different indoor room temperatures (22.2 °C and 30 °C) from human brain signals recorded using electroencephalography (EEG). Seven participants were recruited, from whom EEG, skin temperature, heart rate and thermal survey questionnaires were collected. Regression analyses were carried out to investigate the effectiveness of using EEG power spectral densities (PSD) as predictors of performance. Our results indicate EEG PSDs as predictors provide the highest R2 (> 0.70), that is 17 times higher than using other physiological signals as predictors and is more robust. Finally, the paper provides insight on the selected predictors based on brain activity patterns for low- and high-performance levels under different indoor-temperatures. PMID:29690601

Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures.

PubMed

Nayak, Tapsya; Zhang, Tinghe; Mao, Zijing; Xu, Xiaojing; Zhang, Lin; Pack, Daniel J; Dong, Bing; Huang, Yufei

2018-04-23

Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing indoor environments have become a highly important research topic bearing significant impact in our society. While past research studies have attempted to determine predictors for performance, they do not provide satisfactory prediction ability. Therefore, in this preliminary study, we attempt to predict performance during office-work tasks triggered by different indoor room temperatures (22.2 °C and 30 °C) from human brain signals recorded using electroencephalography (EEG). Seven participants were recruited, from whom EEG, skin temperature, heart rate and thermal survey questionnaires were collected. Regression analyses were carried out to investigate the effectiveness of using EEG power spectral densities (PSD) as predictors of performance. Our results indicate EEG PSDs as predictors provide the highest R ² (> 0.70), that is 17 times higher than using other physiological signals as predictors and is more robust. Finally, the paper provides insight on the selected predictors based on brain activity patterns for low- and high-performance levels under different indoor-temperatures.

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG.

PubMed

Zhang, Xiaoliang; Li, Jiali; Liu, Yugang; Zhang, Zutao; Wang, Zhuojun; Luo, Dianyuan; Zhou, Xiang; Zhu, Miankuan; Salman, Waleed; Hu, Guangdi; Wang, Chunbai

2017-03-01

The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver's brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.

Deviant dynamics of EEG resting state pattern in 22q11.2 deletion syndrome adolescents: A vulnerability marker of schizophrenia?

PubMed

Tomescu, Miralena I; Rihs, Tonia A; Becker, Robert; Britz, Juliane; Custo, Anna; Grouiller, Frédéric; Schneider, Maude; Debbané, Martin; Eliez, Stephan; Michel, Christoph M

2014-08-01

Previous studies have repeatedly found altered temporal characteristics of EEG microstates in schizophrenia. The aim of the present study was to investigate whether adolescents affected by the 22q11.2 deletion syndrome (22q11DS), known to have a 30 fold increased risk to develop schizophrenia, already show deviant EEG microstates. If this is the case, temporal alterations of EEG microstates in 22q11DS individuals could be considered as potential biomarkers for schizophrenia. We used high-density (204 channel) EEG to explore between-group microstate differences in 30 adolescents with 22q11DS and 28 age-matched controls. We found an increased presence of one microstate class (class C) in the 22q11DS adolescents with respect to controls that was associated with positive prodromal symptoms (hallucinations). A previous across-age study showed that the class C microstate was more present during adolescence and a combined EEG-fMRI study associated the class C microstate with the salience resting state network, a network known to be dysfunctional in schizophrenia. Therefore, the increased class C microstates could be indexing the increased risk of 22q11DS individuals to develop schizophrenia if confirmed by our ongoing longitudinal study comparing both the adult 22q11DS individuals with and without schizophrenia, as well as schizophrenic individuals with and without 22q11DS. Copyright © 2014 Elsevier B.V. All rights reserved.

Diurnal changes in glutamate + glutamine levels of healthy young adults assessed by proton magnetic resonance spectroscopy.

PubMed

Volk, Carina; Jaramillo, Valeria; Merki, Renato; O'Gorman Tuura, Ruth; Huber, Reto

2018-06-08

The glutamatergic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor is involved in synaptic plasticity processes, and animal studies have demonstrated altered expression across the sleep wake cycle. Accordingly, glutamate levels are reduced during non-rapid eye movement (NREM) sleep and the rate of this decrease is positively correlated with sleep EEG slow wave activity (SWA). Here, we combined proton magnetic resonance spectroscopy ( 1 H-MRS) and high-density sleep EEG to assess if 1 H-MRS is sensitive to diurnal changes of glutamate + glutamine (GLX) in healthy young adults and if potential overnight changes of GLX are correlated to SWA. 1 H-MRS was measured in the parietal lobe in the evening and in the subsequent morning. High-density sleep EEG was recorded overnight between the evening and morning scans. Our results revealed a significant overnight reduction in GLX, but no significant changes in other metabolites. The decrease in GLX positively correlated with the decrease of SWA. Our study demonstrates that quantification of diurnal changes in GLX is possible by means of 1 H-MRS and indicates that overnight changes in GLX are related to SWA, a marker that is closely linked to the restorative function of sleep. This relationship might be of particular interest in clinical populations in which sleep is disturbed. © 2018 Wiley Periodicals, 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

Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain

PubMed Central

Giacometti, Paolo; Perdue, Katherine L.; Diamond, Solomon G.

2014-01-01

Background Interpretation and analysis of electroencephalography (EEG) measurements relies on the correspondence of electrode scalp coordinates to structural and functional regions of the brain. New Method An algorithm is introduced for automatic calculation of the International 10–20, 10-10, and 10-5 scalp coordinates of EEG electrodes on a boundary element mesh of a human head. The EEG electrode positions are then used to generate parcellation regions of the cerebral cortex based on proximity to the EEG electrodes. Results The scalp electrode calculation method presented in this study effectively and efficiently identifies EEG locations without prior digitization of coordinates. The average of electrode proximity parcellations of the cortex were tabulated with respect to structural and functional regions of the brain in a population of 20 adult subjects. Comparison with Existing Methods Parcellations based on electrode proximity and EEG sensitivity were compared. The parcellation regions based on sensitivity and proximity were found to have 44.0 ± 11.3% agreement when demarcated by the International 10–20, 32.4 ± 12.6% by the 10-10, and 24.7 ± 16.3% by the 10-5 electrode positioning system. Conclusions The EEG positioning algorithm is a fast and easy method of locating EEG scalp coordinates without the need for digitized electrode positions. The parcellation method presented summarizes the EEG scalp locations with respect to brain regions without computation of a full EEG forward model solution. The reference table of electrode proximity versus cortical regions may be used by experimenters to select electrodes that correspond to anatomical and functional regions of interest. PMID:24769168

Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain.

PubMed

Giacometti, Paolo; Perdue, Katherine L; Diamond, Solomon G

2014-05-30

Interpretation and analysis of electroencephalography (EEG) measurements relies on the correspondence of electrode scalp coordinates to structural and functional regions of the brain. An algorithm is introduced for automatic calculation of the International 10-20, 10-10, and 10-5 scalp coordinates of EEG electrodes on a boundary element mesh of a human head. The EEG electrode positions are then used to generate parcellation regions of the cerebral cortex based on proximity to the EEG electrodes. The scalp electrode calculation method presented in this study effectively and efficiently identifies EEG locations without prior digitization of coordinates. The average of electrode proximity parcellations of the cortex were tabulated with respect to structural and functional regions of the brain in a population of 20 adult subjects. Parcellations based on electrode proximity and EEG sensitivity were compared. The parcellation regions based on sensitivity and proximity were found to have 44.0 ± 11.3% agreement when demarcated by the International 10-20, 32.4 ± 12.6% by the 10-10, and 24.7 ± 16.3% by the 10-5 electrode positioning system. The EEG positioning algorithm is a fast and easy method of locating EEG scalp coordinates without the need for digitized electrode positions. The parcellation method presented summarizes the EEG scalp locations with respect to brain regions without computation of a full EEG forward model solution. The reference table of electrode proximity versus cortical regions may be used by experimenters to select electrodes that correspond to anatomical and functional regions of interest. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

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.

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

Plastic changes following imitation-based speech and language therapy for aphasia: a high-density sleep EEG study.

PubMed

Sarasso, Simone; Määttä, Sara; Ferrarelli, Fabio; Poryazova, Rositsa; Tononi, Giulio; Small, Steven L

2014-02-01

BACKGROUND OBJECTIVE: measurement of plastic brain changes induced by a novel rehabilitative approach is a key requirement for validating its biological rationale linking the potential therapeutic gains to the changes in brain physiology. Based on an emerging notion linking cortical plastic changes to EEG sleep slow-wave activity (SWA) regulation, we aimed to assess the acute plastic changes induced by an imitation-based speech therapy in individuals with aphasia by comparing sleep SWA changes before and after therapy. A total of 13 left-hemispheric stroke patients underwent language assessment with the Western Aphasia Battery (WAB) before and after 2 consecutive high-density (hd) EEG sleep recordings interleaved by a daytime session of imitation-based speech therapy (Intensive Mouth Imitation and Talking for Aphasia Therapeutic Effects [IMITATE]). This protocol is thought to stimulate bilateral connections between the inferior parietal lobule and the ventral premotor areas. A single exposure to IMITATE resulted in increases in local EEG SWA during subsequent sleep over the same regions predicted by the therapeutic rationale, particularly over the right hemisphere (unaffected by the lesion). Furthermore, changes in SWA over the left-precentral areas predicted changes in WAB repetition scores in our group, supporting the role of perilesional areas in predicting positive functional responses. Our results suggest that SWA changes occurring in brain areas activated during imitation-based aphasia therapy may reflect the acute plastic changes induced by this intervention. Further testing will be needed to evaluate SWA as a non-invasive assessment of changes induced by the therapy and as a predictor of positive long-term clinical outcome.

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.

Temporal Dynamics of Awareness for Facial Identity Revealed with ERP

ERIC Educational Resources Information Center

Genetti, Melanie; Khateb, Asaid; Heinzer, Severine; Michel, Christoph M.; Pegna, Alan J.

2009-01-01

In this study, we investigated the scalp recorded event-related potential (ERP) responses related to visual awareness. A backward masking procedure was performed while high-density EEG recordings were carried out. Subjects were asked to detect a familiar face, presented at durations that varied parametrically between 16 and 266 ms. ERPs were…

Age-induced differences in brain neural activation elicited by visual emotional stimuli: A high-density EEG study.

PubMed

Tsolaki, Anthoula C; Kosmidou, Vasiliki E; Kompatsiaris, Ioannis Yiannis; Papadaniil, Chrysa; Hadjileontiadis, Leontios; Tsolaki, Magda

2017-01-06

Identifying the brain sources of neural activation during processing of emotional information remains a very challenging task. In this work, we investigated the response to different emotional stimuli and the effect of age on the neuronal activation. Two negative emotion conditions, i.e., 'anger' and 'fear' faces were presented to 22 adult female participants (11 young and 11 elderly) while acquiring high-density electroencephalogram (EEG) data of 256 channels. Brain source localization was utilized to study the modulations in the early N170 event-related-potential component. The results revealed alterations in the amplitude of N170 and the localization of areas with maximum neural activation. Furthermore, age-induced differences are shown in the topographic maps and the neural activation for both emotional stimuli. Overall, aging appeared to affect the limbic area and its implication to emotional processing. These findings can serve as a step toward the understanding of the way the brain functions and evolves with age which is a significant element in the design of assistive environments. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

The effects of age and gender on sleep EEG power spectral density in the middle years of life (ages 20-60 years old)

NASA Technical Reports Server (NTRS)

Carrier, J.; Land, S.; Buysse, D. J.; Kupfer, D. J.; Monk, T. H.

2001-01-01

The effects of age and gender on sleep EEG power spectral density were assessed in a group of 100 subjects aged 20 to 60 years. We propose a new statistical strategy (mixed-model using fixed-knot regression splines) to analyze quantitative EEG measures. The effect of gender varied according to frequency, but no interactions emerged between age and gender, suggesting that the aging process does not differentially influence men and women. Women had higher power density than men in delta, theta, low alpha, and high spindle frequency range. The effect of age varied according to frequency and across the night. The decrease in power with age was not restricted to slow-wave activity, but also included theta and sigma activity. With increasing age, the attenuation over the night in power density between 1.25 and 8.00 Hz diminished, and the rise in power between 12.25 and 14.00 Hz across the night decreased. Increasing age was associated with higher power in the beta range. These results suggest that increasing age may be related to an attenuation of homeostatic sleep pressure and to an increase in cortical activation during sleep.

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG

PubMed Central

Zhang, Xiaoliang; Li, Jiali; Liu, Yugang; Zhang, Zutao; Wang, Zhuojun; Luo, Dianyuan; Zhou, Xiang; Zhu, Miankuan; Salman, Waleed; Hu, Guangdi; Wang, Chunbai

2017-01-01

The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver’s brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety. PMID:28257073

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

Wake High-Density Electroencephalographic Spatiospectral Signatures of Insomnia

PubMed Central

Colombo, Michele A.; Ramautar, Jennifer R.; Wei, Yishul; Gomez-Herrero, Germán; Stoffers, Diederick; Wassing, Rick; Benjamins, Jeroen S.; Tagliazucchi, Enzo; van der Werf, Ysbrand D.; Cajochen, Christian; Van Someren, Eus J.W.

2016-01-01

Study Objectives: Although daytime complaints are a defining characteristic of insomnia, most EEG studies evaluated sleep only. We used high-density electroencephalography to investigate wake resting state oscillations characteristic of insomnia disorder (ID) at a fine-grained spatiospectral resolution. Methods: A case-control assessment during eyes open (EO) and eyes closed (EC) was performed in a laboratory for human physiology. Participants (n = 94, 74 female, 21–70 y) were recruited through www.sleepregistry.nl: 51 with ID, according to DSM-5 and 43 matched controls. Exclusion criteria were any somatic, neurological or psychiatric condition. Group differences in the spectral power topographies across multiple frequencies (1.5 to 40 Hz) were evaluated using permutation-based inference with Threshold-Free Cluster-Enhancement, to correct for multiple comparisons. Results: As compared to controls, participants with ID showed less power in a narrow upper alpha band (11–12.7 Hz, peak: 11.7 Hz) over bilateral frontal and left temporal regions during EO, and more power in a broad beta frequency range (16.3–40 Hz, peak: 19 Hz) globally during EC. Source estimates suggested global rather than cortically localized group differences. Conclusions: The widespread high power in a broad beta band reported previously during sleep in insomnia is present as well during eyes closed wakefulness, suggestive of a round-the-clock hyperarousal. Low power in the upper alpha band during eyes open is consistent with low cortical inhibition and attentional filtering. The fine-grained HD-EEG findings suggest that, while more feasible than PSG, wake EEG of short duration with a few well-chosen electrodes and frequency bands, can provide valuable features of insomnia. Citation: Colombo MA, Ramautar JR, Wei Y, Gomez-Herrero G, Stoffers D, Wassing R, Benjamins JS, Tagliazucchi E, van der Werf YD, Cajochen C, Van Someren EJW. Wake high-density electroencephalographic spatiospectral signatures of insomnia. SLEEP 2016;39(5):1015–1027. PMID:26951395

The infant mirror neuron system studied with high density EEG.

PubMed

Nyström, Pär

2008-01-01

The mirror neuron system has been suggested to play a role in many social capabilities such as action understanding, imitation, language and empathy. These are all capabilities that develop during infancy and childhood, but the human mirror neuron system has been poorly studied using neurophysiological measures. This study measured the brain activity of 6-month-old infants and adults using a high-density EEG net with the aim of identifying mirror neuron activity. The subjects viewed both goal-directed movements and non-goal-directed movements. An independent component analysis was used to extract the sources of cognitive processes. The desynchronization of the mu rhythm in adults has been shown to be a marker for activation of the mirror neuron system and was used as a criterion to categorize independent components between subjects. The results showed significant mu desynchronization in the adult group and significantly higher ERP activation in both adults and 6-month-olds for the goal-directed action observation condition. This study demonstrate that infants as young as 6 months display mirror neuron activity and is the first to present a direct ERP measure of the mirror neuron system in infants.

Assessing effect of meditation on cognitive workload using EEG signals

NASA Astrophysics Data System (ADS)

Jadhav, Narendra; Manthalkar, Ramchandra; Joshi, Yashwant

2017-06-01

Recent research suggests that meditation affects the structure and function of the brain. Cognitive load can be handled in effective way by the meditators. EEG signals are used to quantify cognitive load. The research of investigating effect of meditation on cognitive workload using EEG signals in pre and post-meditation is an open problem. The subjects for this study are young healthy 11 engineering students from our institute. The focused attention meditation practice is used for this study. EEG signals are recorded at the beginning of meditation and after four weeks of regular meditation using EMOTIV device. The subjects practiced meditation daily 20 minutes for 4 weeks. The 7 level arithmetic additions of single digit (low level) to three digits with carry (high level) are presented as cognitive load. The cognitive load indices such as arousal index, performance enhancement, neural activity, load index, engagement, and alertness are evaluated in pre and post meditation. The cognitive indices are improved in post meditation data. Power Spectral Density (PSD) feature is compared between pre and post-meditation across all subjects. The result hints that the subjects were handling cognitive load without stress (ease of cognitive functioning increased for the same load) after 4 weeks of meditation.

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.

Experienced Mindfulness Meditators Exhibit Higher Parietal-Occipital EEG Gamma Activity during NREM Sleep

PubMed Central

Ferrarelli, Fabio; Smith, Richard; Dentico, Daniela; Riedner, Brady A.; Zennig, Corinna; Benca, Ruth M.; Lutz, Antoine; Davidson, Richard J.; Tononi, Giulio

2013-01-01

Over the past several years meditation practice has gained increasing attention as a non-pharmacological intervention to provide health related benefits, from promoting general wellness to alleviating the symptoms of a variety of medical conditions. However, the effects of meditation training on brain activity still need to be fully characterized. Sleep provides a unique approach to explore the meditation-related plastic changes in brain function. In this study we performed sleep high-density electroencephalographic (hdEEG) recordings in long-term meditators (LTM) of Buddhist meditation practices (approximately 8700 mean hours of life practice) and meditation naive individuals. We found that LTM had increased parietal-occipital EEG gamma power during NREM sleep. This increase was specific for the gamma range (25–40 Hz), was not related to the level of spontaneous arousal during NREM and was positively correlated with the length of lifetime daily meditation practice. Altogether, these findings indicate that meditation practice produces measurable changes in spontaneous brain activity, and suggest that EEG gamma activity during sleep represents a sensitive measure of the long-lasting, plastic effects of meditative training on brain function. PMID:24015304

Analysis of slow-wave activity and slow-wave oscillations prior to somnambulism.

PubMed

Jaar, Olivier; Pilon, Mathieu; Carrier, Julie; Montplaisir, Jacques; Zadra, Antonio

2010-11-01

STUDY OBJECTIVIES: several studies have investigated slow wave sleep EEG parameters, including slow-wave activity (SWA) in relation to somnambulism, but results have been both inconsistent and contradictory. The first goal of the present study was to conduct a quantitative analysis of sleepwalkers' sleep EEG by studying fluctuations in spectral power for delta (1-4 Hz) and slow delta (0.5-1 Hz) before the onset of somnambulistic episodes. A secondary aim was to detect slow-wave oscillations to examine changes in their amplitude and density prior to behavioral episodes. twenty-two adult sleepwalkers were investigated polysomnographically following 25 h of sleep deprivation. analysis of patients' sleep EEG over the 200 sec prior to the episodes' onset revealed that the episodes were not preceded by a gradual increase in spectral power for either delta or slow delta over frontal, central, or parietal leads. However, time course comparisons revealed significant changes in the density of slow-wave oscillations as well as in very slow oscillations with significant increases occurring during the final 20 sec immediately preceding episode onset. the specificity of these sleep EEG parameters for the occurrence and diagnosis of NREM parasomnias remains to be determined.

Rapid changes in arousal states of healthy volunteers during robot-assisted gait training: a quantitative time-series electroencephalography study

PubMed Central

2014-01-01

Background Robot-assisted gait training (RAGT) is expected to be an effective rehabilitative intervention for patients with gait disturbances. However, the monotonous gait pattern provided by robotic guidance tends to induce sleepiness, and the resultant decreased arousal during RAGT may negatively affect gait training progress. This study assessed electroencephalography (EEG)-based, objective sleepiness during RAGT and examined whether verbal or nonverbal warning sounds are effective stimuli for counteracting such sleepiness. Methods Twelve healthy men walked on a treadmill for 6 min, while being guided by a Gait-Assistance Robot, under 3 experimental conditions: with sine-wave sound stimulation (SS), verbal sound stimulation (VS), and no sound stimulation (NS). The volunteers were provided with warning sound stimulation at 4 min (ST1), 4 min 20 s (ST2), 4 min 40 s (ST3), and 5 min (ST4) after the start of RAGT. EEGs were recorded at the central (Cz) and occipital (O1 and O2) regions (International 10–20 system) before and during RAGT, and 4-s segments of EEG data were extracted from the filtered data during the 8 experimental periods: middle of the eyes-closed condition; middle of the eyes-open condition; beginning of RAGT; immediately before ST1; immediately after ST1, ST2, ST3, and ST4. According to the method used in the Karolinska drowsiness test, the power densities of the theta, alpha 1, and alpha 2 bands were calculated as indices of objective sleepiness. Results Comparisons of the findings between baseline and before ST showed that the power densities of the alpha 1 and 2 bands tended to increase, whereas the theta power density increased significantly (P < .05). During NS, the power densities remained at a constant high level until after ST4. During SS and VS, the power densities were attenuated immediately to the same degree and maintained at a constant low level until after ST4. Conclusions This study is the first to demonstrate that EEG-measured arousal levels decrease within a short time during RAGT, but are restored and maintained by intermittent warning sound stimulation. PMID:24725811

Tick-Tock Goes the Croc: A High-Density EEG Study of Risk-Reactivity and Binge-Drinking.

PubMed

Kiat, John E; Cheadle, Jacob E

2018-05-31

Links between individual differences in risk processing and high-risk behaviors such as binge drinking have long been the focus of active research. However, investigations in this area almost exclusively utilize decision-making focused paradigms. This emphasis makes it difficult to assess links between risk behaviors and raw risk reactivity independent of decision and feedback processes. A deeper understanding of this association has the potential to shed light on the role of risk reactivity in high-risk behavior susceptibility. To contribute towards this aim, this study utilizes a popular risk-taking game, the crocodile dentist, to assess links between individual differences in decision-free risk-reactivity and reported binge drinking frequency levels. In this task, participants engage in a series of decision-free escalating risk responses. Risk-reactivity was assessed by measuring Late Positive Potential responses towards risk-taking action initiation cues using high-density 256-Channel EEG. The results indicate that, after controlling for overall alcohol consumption frequency, higher rates of reported binge drinking are associated with both increased general risk-taking responsivity and increased risk-reactivity escalation as a function of risk level. These findings highlight intriguing links between risk reactivity and binge drinking frequency, making key contributions in the areas of risk-taking and affective science.

Detection and description of non-linear interdependence in normal multichannel human EEG data.

PubMed

Breakspear, M; Terry, J R

2002-05-01

This study examines human scalp electroencephalographic (EEG) data for evidence of non-linear interdependence between posterior channels. The spectral and phase properties of those epochs of EEG exhibiting non-linear interdependence are studied. Scalp EEG data was collected from 40 healthy subjects. A technique for the detection of non-linear interdependence was applied to 2.048 s segments of posterior bipolar electrode data. Amplitude-adjusted phase-randomized surrogate data was used to statistically determine which EEG epochs exhibited non-linear interdependence. Statistically significant evidence of non-linear interactions were evident in 2.9% (eyes open) to 4.8% (eyes closed) of the epochs. In the eyes-open recordings, these epochs exhibited a peak in the spectral and cross-spectral density functions at about 10 Hz. Two types of EEG epochs are evident in the eyes-closed recordings; one type exhibits a peak in the spectral density and cross-spectrum at 8 Hz. The other type has increased spectral and cross-spectral power across faster frequencies. Epochs identified as exhibiting non-linear interdependence display a tendency towards phase interdependencies across and between a broad range of frequencies. Non-linear interdependence is detectable in a small number of multichannel EEG epochs, and makes a contribution to the alpha rhythm. Non-linear interdependence produces spatially distributed activity that exhibits phase synchronization between oscillations present at different frequencies. The possible physiological significance of these findings are discussed with reference to the dynamical properties of neural systems and the role of synchronous activity in the neocortex.

Investigation of phase synchronization of interictal EEG in right temporal lobe epilepsy

NASA Astrophysics Data System (ADS)

Yu, Haitao; Cai, Lihui; Wu, Xinyu; Song, Zhenxi; Wang, Jiang; Xia, Zijie; Liu, Jing; Cao, Yibin

2018-02-01

Epilepsy is commonly associated with abnormally synchronous activity of neurons located in epileptogenic zones. In this study, we investigated the synchronization characteristic of right temporal lobe epilepsy (RTLE). Multichannel electroencephalography (EEG) data were recorded from the RTLE patients during interictal period and normal controls. Power spectral density was first used to analyze the EEG power for two groups of subjects. It was found that the power of epileptics is increased in the whole brain compared with that of the control. We calculated phase lag index (PLI) to measure the phase synchronization between each pair of EEG signals. A higher degree of synchronization was observed in the epileptics especially between distant channels. In particular, the regional synchronization degree was negatively correlated with power spectral density and the correlation was weaker for epileptics. Moreover, the synchronization degree decayed with the increase of relative distance of channels for both the epilepsy and control, but the dependence was weakened in the former. The obtained results may provide new insights into the generation mechanism of epilepsy.

Assessing a learning process with functional ANOVA estimators of EEG power spectral densities.

PubMed

Gutiérrez, David; Ramírez-Moreno, Mauricio A

2016-04-01

We propose to assess the process of learning a task using electroencephalographic (EEG) measurements. In particular, we quantify changes in brain activity associated to the progression of the learning experience through the functional analysis-of-variances (FANOVA) estimators of the EEG power spectral density (PSD). Such functional estimators provide a sense of the effect of training in the EEG dynamics. For that purpose, we implemented an experiment to monitor the process of learning to type using the Colemak keyboard layout during a twelve-lessons training. Hence, our aim is to identify statistically significant changes in PSD of various EEG rhythms at different stages and difficulty levels of the learning process. Those changes are taken into account only when a probabilistic measure of the cognitive state ensures the high engagement of the volunteer to the training. Based on this, a series of statistical tests are performed in order to determine the personalized frequencies and sensors at which changes in PSD occur, then the FANOVA estimates are computed and analyzed. Our experimental results showed a significant decrease in the power of [Formula: see text] and [Formula: see text] rhythms for ten volunteers during the learning process, and such decrease happens regardless of the difficulty of the lesson. These results are in agreement with previous reports of changes in PSD being associated to feature binding and memory encoding.

Cortical light scattering during interictal epileptic spikes in frontal lobe epilepsy in children: A fast optical signal and electroencephalographic study.

PubMed

Manoochehri, Mana; Mahmoudzadeh, Mahdi; Bourel-Ponchel, Emilie; Wallois, Fabrice

2017-12-01

Interictal epileptic spikes (IES) represent a signature of the transient synchronous and excessive discharge of a large ensemble of cortical heterogeneous neurons. Epilepsy cannot be reduced to a hypersynchronous activation of neurons whose functioning is impaired, resulting on electroencephalogram (EEG) in epileptic seizures or IES. The complex pathophysiological mechanisms require a global approach to the interactions between neural synaptic and nonsynaptic, vascular, and metabolic systems. In the present study, we focused on the interaction between synaptic and nonsynaptic mechanisms through the simultaneous noninvasive multimodal multiscale recording of high-density EEG (HD-EEG; synaptic) and fast optical signal (FOS; nonsynaptic), which evaluate rapid changes in light scattering related to changes in membrane configuration occurring during neuronal activation of IES. To evaluate changes in light scattering occurring around IES, three children with frontal IES were simultaneously recorded with HD-EEG and FOS. To evaluate change in synchronization, time-frequency representation analysis of the HD-EEG was performed simultaneously around the IES. To independently evaluate our multimodal method, a control experiment with somatosensory stimuli was designed and applied to five healthy volunteers. Alternating increase-decrease-increase in optical signals occurred 200 ms before to 180 ms after the IES peak. These changes started before any changes in EEG signal. In addition, time-frequency domain EEG analysis revealed alternating decrease-increase-decrease in the EEG spectral power concomitantly with changes in the optical signal during IES. These results suggest a relationship between (de)synchronization and neuronal volume changes in frontal lobe epilepsy during IES. These changes in the neuronal environment around IES in frontal lobe epilepsy observed in children, as they have been in rats, raise new questions about the synaptic/nonsynaptic mechanisms that propel the neurons to hypersynchronization, as occurs during IES. We further demonstrate that this noninvasive multiscale multimodal approach is suitable for studying the pathophysiology of the IES in patients. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

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.

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.

Automatic cardiac cycle determination directly from EEG-fMRI data by multi-scale peak detection method.

PubMed

Wong, Chung-Ki; Luo, Qingfei; Zotev, Vadim; Phillips, Raquel; Chan, Kam Wai Clifford; Bodurka, Jerzy

2018-03-31

In simultaneous EEG-fMRI, identification of the period of cardioballistic artifact (BCG) in EEG is required for the artifact removal. Recording the electrocardiogram (ECG) waveform during fMRI is difficult, often causing inaccurate period detection. Since the waveform of the BCG extracted by independent component analysis (ICA) is relatively invariable compared to the ECG waveform, we propose a multiple-scale peak-detection algorithm to determine the BCG cycle directly from the EEG data. The algorithm first extracts the high contrast BCG component from the EEG data by ICA. The BCG cycle is then estimated by band-pass filtering the component around the fundamental frequency identified from its energy spectral density, and the peak of BCG artifact occurrence is selected from each of the estimated cycle. The algorithm is shown to achieve a high accuracy on a large EEG-fMRI dataset. It is also adaptive to various heart rates without the needs of adjusting the threshold parameters. The cycle detection remains accurate with the scan duration reduced to half a minute. Additionally, the algorithm gives a figure of merit to evaluate the reliability of the detection accuracy. The algorithm is shown to give a higher detection accuracy than the commonly used cycle detection algorithm fmrib_qrsdetect implemented in EEGLAB. The achieved high cycle detection accuracy of our algorithm without using the ECG waveforms makes possible to create and automate pipelines for processing large EEG-fMRI datasets, and virtually eliminates the need for ECG recordings for BCG artifact removal. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Multimodal 2D Brain Computer Interface.

PubMed

Almajidy, Rand K; Boudria, Yacine; Hofmann, Ulrich G; Besio, Walter; Mankodiya, Kunal

2015-08-01

In this work we used multimodal, non-invasive brain signal recording systems, namely Near Infrared Spectroscopy (NIRS), disc electrode electroencephalography (EEG) and tripolar concentric ring electrodes (TCRE) electroencephalography (tEEG). 7 healthy subjects participated in our experiments to control a 2-D Brain Computer Interface (BCI). Four motor imagery task were performed, imagery motion of the left hand, the right hand, both hands and both feet. The signal slope (SS) of the change in oxygenated hemoglobin concentration measured by NIRS was used for feature extraction while the power spectrum density (PSD) of both EEG and tEEG in the frequency band 8-30Hz was used for feature extraction. Linear Discriminant Analysis (LDA) was used to classify different combinations of the aforementioned features. The highest classification accuracy (85.2%) was achieved by using features from all the three brain signals recording modules. The improvement in classification accuracy was highly significant (p = 0.0033) when using the multimodal signals features as compared to pure EEG features.

A High-Density EEG Investigation into Steady State Binaural Beat Stimulation

PubMed Central

Goodin, Peter; Ciorciari, Joseph; Baker, Kate; Carrey, Anne-Marie; Harper, Michelle; Kaufman, Jordy

2012-01-01

Binaural beats are an auditory phenomenon that has been suggested to alter physiological and cognitive processes including vigilance and brainwave entrainment. Some personality traits measured by the NEO Five Factor Model have been found to alter entrainment using pulsing light stimuli, but as yet no studies have examined if this occurs using steady state presentation of binaural beats for a relatively short presentation of two minutes. This study aimed to examine if binaural beat stimulation altered vigilance or cortical frequencies and if personality traits were involved. Thirty-one participants were played binaural beat stimuli designed to elicit a response at either the Theta (7 Hz) or Beta (16 Hz) frequency bands while undertaking a zero-back vigilance task. EEG was recorded from a high-density electrode cap. No significant differences were found in vigilance or cortical frequency power during binaural beat stimulation compared to a white noise control period. Furthermore, no significant relationships were detected between the above and the Big Five personality traits. This suggests a short presentation of steady state binaural beats are not sufficient to alter vigilance or entrain cortical frequencies at the two bands examined and that certain personality traits were not more susceptible than others. PMID:22496862

A high-density EEG investigation into steady state binaural beat stimulation.

PubMed

Goodin, Peter; Ciorciari, Joseph; Baker, Kate; Carey, Anne-Marie; Carrey, Anne-Marie; Harper, Michelle; Kaufman, Jordy

2012-01-01

Binaural beats are an auditory phenomenon that has been suggested to alter physiological and cognitive processes including vigilance and brainwave entrainment. Some personality traits measured by the NEO Five Factor Model have been found to alter entrainment using pulsing light stimuli, but as yet no studies have examined if this occurs using steady state presentation of binaural beats for a relatively short presentation of two minutes. This study aimed to examine if binaural beat stimulation altered vigilance or cortical frequencies and if personality traits were involved. Thirty-one participants were played binaural beat stimuli designed to elicit a response at either the Theta (7 Hz) or Beta (16 Hz) frequency bands while undertaking a zero-back vigilance task. EEG was recorded from a high-density electrode cap. No significant differences were found in vigilance or cortical frequency power during binaural beat stimulation compared to a white noise control period. Furthermore, no significant relationships were detected between the above and the Big Five personality traits. This suggests a short presentation of steady state binaural beats are not sufficient to alter vigilance or entrain cortical frequencies at the two bands examined and that certain personality traits were not more susceptible than others.

Study of the Neurophysiology of Central Fatigue

DTIC Science & Technology

2014-11-05

AGENCY NAME(S) AND ADDRESS (ES) U.S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 cognitive, fatigue, fatigability...Fatigability in Parkinson Disease. Movement Disorders 2012;27:e6. 4. Wang C, Ding M, Kluger BM. High-density EEG study of cue-evoked preparatory...258. 3. Kluger B, Wang C, Proemsey J, Ding M. Neuronal Correlates of Executive Dysfunction and Fatigability in Parkinson Disease. Movement Disorders

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.

Evaluation of Dry Sensors for Neonatal EEG Recordings.

PubMed

Fridman, Igor; Cordeiro, Malaika; Rais-Bahrami, Khodayar; McDonald, Neil J; Reese, James J; Massaro, An N; Conry, Joan A; Chang, Taeun; Soussou, Walid; Tsuchida, Tammy N

2016-04-01

Neonatal seizures are a common neurologic diagnosis in neonatal intensive care units, occurring in approximately 14,000 newborns annually in the United States. Although the only reliable means of detecting and treating neonatal seizures is with an electroencephalography (EEG) recording, many neonates do not receive an EEG or experience delays in getting them. Barriers to obtaining neonatal EEGs include (1) lack of skilled EEG technologists to apply conventional wet electrodes to delicate neonatal skin, (2) poor signal quality because of improper skin preparation and artifact, and (3) extensive time needed to apply electrodes. Dry sensors have the potential to overcome these obstacles but have not previously been evaluated on neonates. Sequential and simultaneous recordings with wet and dry sensors were performed for 1 hour on 27 neonates from 35 to 42.5 weeks postmenstrual age. Recordings were analyzed for correlation and amplitude and were reviewed by neurophysiologists. Performance of dry sensors on simulated vernix was examined. Analysis of dry and wet signals showed good time-domain correlation (reaching >0.8), given the nonsuperimposed sensor positions and similar power spectral density curves. Neurophysiologist reviews showed no statistically significant difference between dry and wet data on most clinically relevant EEG background and seizure patterns. There was no skin injury after 1 hour of dry sensor recordings. In contrast to wet electrodes, impedance and electrical artifact of dry sensors were largely unaffected by simulated vernix. Dry sensors evaluated in this study have the potential to provide high-quality, timely EEG recordings on neonates with less risk of skin injury.

Evaluation of Dry Sensors for Neonatal EEG recordings

PubMed Central

Fridman, Igor; Cordeiro, Malaika; Rais-Bahrami, Khodayar; McDonald, Neil J.; Reese, James J.; Massaro, An N.; Conry, Joan A.; Chang, Taeun; Soussou, Walid; Tsuchida, Tammy N.

2015-01-01

Introduction Neonatal seizures are a common neurologic diagnosis in Neonatal Intensive Care Units (NICUs), occurring in approximately 14,000 newborns annually in the US. While the only reliable means of detecting and treating neonatal seizures is with an EEG recording, many neonates do not get an EEG or experience delays in getting them. Barriers to obtaining neonatal EEGs include: 1) lack of skilled EEG technologists to apply conventional wet electrodes to delicate neonatal skin, 2) poor signal quality due to improper skin preparation and artifact, 3) extensive time needed to apply electrodes. Dry sensors have the potential to overcome these obstacles but have not been previously evaluated on neonates. Methods Sequential and simultaneous recordings with wet and dry sensors were performed for one hour on 27 neonates from 35-42.5 weeks postmenstrual age. Recordings were analyzed for correlation and amplitude, and were reviewed by neurophysiologists. Performance of dry sensors on simulated vernix was examined. Results Analysis of dry and wet signals showed good time-domain correlation (reaching >0.8) given the non-superimposed sensor positions, and similar power spectral density curves. Neurophysiologist reviews showed no statistically significant difference between dry and wet data on most clinically-relevant EEG background and seizure patterns. There was no skin injury after 1 hr of dry sensor recordings. In contrast to wet electrodes, impedance and electrical artifact of dry sensors were largely unaffected by simulated vernix. Conclusions Dry sensors evaluated in this study have the potential to provide high-quality, timely EEG recordings on neonates with less risk of skin injury. PMID:26562208

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.

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.

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

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.

Neuroelectrical imaging study of music perception by children with unilateral and bilateral cochlear implants.

PubMed

Marsella, Pasquale; Scorpecci, Alessandro; Vecchiato, Giovanni; Colosimo, Alfredo; Maglione, Anton Giulio; Babiloni, Fabio

2014-05-01

To investigate by means of non-invasive neuroelectrical imaging the differences in the perceived pleasantness of music between children with cochlear implants (CI) and normal-hearing (NH) children. 5 NH children and 5 children who received a sequential bilateral CI were assessed by means of High-Resolution EEG with Source Reconstruction as they watched a musical cartoon. Implanted children were tested before and after the second implant. For each subject the scalp Power Spectral Density was calculated in order to investigate the EEG alpha asymmetry. The scalp topographic distribution of the EEG power spectrum in the alpha band was different in children using one CI as compared to NH children (see figure). With two CIs the cortical activation pattern changed significantly, becoming more similar to the one observed in NH children. The findings support the hypothesis that bilateral CI users have a closer-to-normal perception of the pleasantness of music than unilaterally implanted children.

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.

Stimulus-dependent deliberation process leading to a specific motor action demonstrated via a multi-channel EEG analysis

PubMed Central

Henz, Sonja; Kutz, Dieter F.; Werner, Jana; Hürster, Walter; Kolb, Florian P.; Nida-Ruemelin, Julian

2015-01-01

The aim of the study was to determine whether a deliberative process, leading to a motor action, is detectable in high density EEG recordings. Subjects were required to press one of two buttons. In a simple motor task the subject knew which button to press, whilst in a color-word Stroop task subjects had to press the right button with the right index finger when meaning and color coincided, or the left button with the left index finger when meaning and color were disparate. EEG recordings obtained during the simple motor task showed a sequence of positive (P) and negative (N) cortical potentials (P1-N1-P2) which are assumed to be related to the processing of the movement. The sequence of cortical potentials was similar in EEG recordings of subjects having to deliberate over how to respond, but the above sequence (P1-N1-P2) was preceded by slowly increasing negativity (N0), with N0 being assumed to represent the end of the deliberation process. Our data suggest the existence of neurophysiological correlates of deliberative processes. PMID:26190987

EEG changes as heat stress reactions in rats irradiated by high intensity 35 GHz millimeter waves.

PubMed

Xie, Taorong; Pei, Jian; Cui, Yibin; Zhang, Jie; Qi, Hongxing; Chen, Shude; Qiao, Dengjiang

2011-06-01

As the application of millimeter waves for civilian and military use increases, the possibility of overexposure to millimeter waves will also increase. This paper attempts to evaluate stress reactions evoked by 35 GHz millimeter waves. The stress reactions in Sprague-Dawley (SD) rats were quantitatively studied by analyzing electroencephalogram (EEG) changes induced by overexposure to 35 GHz millimeter waves. The relative changes in average energy of the EEG and its wavelet decompositions were used for extracting the stress reaction indicators. Incident average power densities (IAPDs) of 35 GHz millimeter waves from 0.5 W cm(-2) to 7.5 W cm(-2) were employed to investigate the relation between irradiation dose and the stress reactions in the rats. Different stress reaction periods evoked by irradiation were quantitatively evaluated by EEG results. The results illustrate that stress reactions are more intense during the first part of the irradiation than during the later part. The skin temperature increase produced by millimeter wave irradiation is the principle reason for stress reactions and skin injuries. As expected, at the higher levels of irradiation, the reaction time decreases and the reaction intensity increases.

Feature study of hysterical blindness EEG based on FastICA with combined-channel information.

PubMed

Qin, Xuying; Wang, Wei; Hu, Lintao; Wang, Xu; Yuan, Xiaojie

2015-01-01

An appropriate feature study of hysteria electroencephalograms (EEG) would provide new insights into neural mechanisms of the disease, and also make improvements in patient diagnosis and management. The objective of this paper is to provide an explanation for what causes a particular visual loss, by associating the features of hysterical blindness EEG with brain function. An idea for the novel feature extraction for hysterical blindness EEG, utilizing combined-channel information, was applied in this paper. After channels had been combined, the sliding-window-FastICA was applied to process the combined normal EEG and hysteria EEG, respectively. Kurtosis features were calculated from the processed signals. As the comparison feature, the power spectral density of normal and hysteria EEG were computed. According to the feature analysis results, a region of brain dysfunction was located at the occipital lobe, O1 and O2. Furthermore, new abnormality was found at the parietal lobe, C3, C4, P3, and P4, that provided us with a new perspective for understanding hysterical blindness. Indicated by the kurtosis results which were consistent with brain function and the clinical diagnosis, our method was found to be a useful tool to capture features in hysterical blindness EEG.

[A study of complexity and power spectrum of cortical EEG and hippocampal potential in rats under different behavioral states].

PubMed

Feng, Zhou-yan; Zheng, Xiao-xiang

2002-08-01

Objective. To study the complexity and the power spectrum of cortical EEG and hippocampal potential in rats under waking and sleep states. Method. Cortical EEG and hippocampal potential were collected by implanted electrodes in freely moving rats. Algorithmic complexity (Kc), approximate entropy (ApEn), power spectral density (PSD) and gravity frequency of PSD of the potential waves were calculated. Result. The complexity of hippocampal potential was higher than that of cortical EEG under every state. The complexity of cortical EEG was lowest under the state of non rapid eye movement (NREM) sleep. The complexity of hippocampal potential was highest under waking state. The total power of both potentials in 0.5- 30 Hz frequency band showed their highest values under NREM state. Conclusion. The values of Kc and ApEn are closely related to the distributions of PSD. When there are evident peaks in PSD, the complexities of signals will decrease. The complexities may be used to distinguish the difference between cortical EEG and hippocampal potential, or large differences between the same kind of potentials under different behavioral states.

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

Unsupervised EEG analysis for automated epileptic seizure detection

NASA Astrophysics Data System (ADS)

Birjandtalab, Javad; Pouyan, Maziyar Baran; Nourani, Mehrdad

2016-07-01

Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

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.

High-voltage electroencephalogram spindles in rats, aging and 5-HT2 antagonism.

PubMed

Moyanova, S; Kortenska, L; Kirov, R

1998-03-09

We examined the effects of serotonin-2 (5-hydroxytryptamine-2, 5-HT2) receptor antagonists on the so-called high-voltage spindles (HVS, electroencephalographic patterns, characterized by large amplitude rhythmic waves mainly in the alpha band), recorded from the frontal cortex of young, middle-aged and old freely-moving rats during waking immobility. The study was based on the assumption that the effects of 5-HT2 receptor antagonists on the HVS activity depend on the age of rats, because there is evidence for an age-related decrease in the 5-HT2 binding sites density. Four parameters of the electroencephalogram (EEG) were used to characterize the HVS activity: the square root-transformed EEG peak power in the alpha band, the frequency corresponding to this peak (both measured from the EEG power spectra using the fast Fourier transform), the HVS mean duration, and the HVS incidence (both measured from the EEG records). The EEG parameters were analyzed after i.p. administration of three 5-HT2 receptor antagonists: ketanserin, ritanserin and cyproheptadine. In young rats, the three drugs increased the alpha power, but did not change the alpha peak-corresponding frequency. Ketanserin and ritanserin did not change the HVS mean duration and HVS incidence, while cyproheptadine increased both these parameters in young rats. In middle-aged and old untreated rats, the HVS activity was significantly increased. The three 5-HT2 antagonists did not change the HVS activity in aged rats, which could be due to age-related suppression of the 5-HT2 receptor functions. Copyright 1998 Elsevier Science B.V.

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.

Differential modulation of global and local neural oscillations in REM sleep by homeostatic sleep regulation

PubMed Central

Kim, Bowon; Kocsis, Bernat; Hwang, Eunjin; Kim, Youngsoo; Strecker, Robert E.; McCarley, Robert W.; Choi, Jee Hyun

2017-01-01

Homeostatic rebound in rapid eye movement (REM) sleep normally occurs after acute sleep deprivation, but REM sleep rebound settles on a persistently elevated level despite continued accumulation of REM sleep debt during chronic sleep restriction (CSR). Using high-density EEG in mice, we studied how this pattern of global regulation is implemented in cortical regions with different functions and network architectures. We found that across all areas, slow oscillations repeated the behavioral pattern of persistent enhancement during CSR, whereas high-frequency oscillations showed progressive increases. This pattern followed a common rule despite marked topographic differences. The findings suggest that REM sleep slow oscillations may translate top-down homeostatic control to widely separated brain regions whereas fast oscillations synchronizing local neuronal ensembles escape this global command. These patterns of EEG oscillation changes are interpreted to reconcile two prevailing theories of the function of sleep, synaptic homeostasis and sleep dependent memory consolidation. PMID:28193862

Differential modulation of global and local neural oscillations in REM sleep by homeostatic sleep regulation.

PubMed

Kim, Bowon; Kocsis, Bernat; Hwang, Eunjin; Kim, Youngsoo; Strecker, Robert E; McCarley, Robert W; Choi, Jee Hyun

2017-02-28

Homeostatic rebound in rapid eye movement (REM) sleep normally occurs after acute sleep deprivation, but REM sleep rebound settles on a persistently elevated level despite continued accumulation of REM sleep debt during chronic sleep restriction (CSR). Using high-density EEG in mice, we studied how this pattern of global regulation is implemented in cortical regions with different functions and network architectures. We found that across all areas, slow oscillations repeated the behavioral pattern of persistent enhancement during CSR, whereas high-frequency oscillations showed progressive increases. This pattern followed a common rule despite marked topographic differences. The findings suggest that REM sleep slow oscillations may translate top-down homeostatic control to widely separated brain regions whereas fast oscillations synchronizing local neuronal ensembles escape this global command. These patterns of EEG oscillation changes are interpreted to reconcile two prevailing theories of the function of sleep, synaptic homeostasis and sleep dependent memory consolidation.

Estimation of the EEG power spectrum using MRI T(2) relaxation time in traumatic brain injury.

PubMed

Thatcher, R W; Biver, C; Gomez, J F; North, D; Curtin, R; Walker, R A; Salazar, A

2001-09-01

To study the relationship between magnetic resonance imaging (MRI) T(2) relaxation time and the power spectrum of the electroencephalogram (EEG) in long-term follow up of traumatic brain injury. Nineteen channel quantitative electroencephalograms or qEEG, tests of cognitive function and quantitative MRI T(2) relaxation times (qMRI) were measured in 18 mild to severe closed head injured outpatients 2 months to 4.6 years after injury and 11 normal controls. MRI T(2) and the Laplacian of T(2) were then correlated with the power spectrum of the scalp electrical potentials and current source densities of the qEEG. qEEG and qMRI T(2) were related by a frequency tuning with maxima in the alpha (8-12Hz) and the lower EEG frequencies (0.5-5Hz), which varied as a function of spatial location. The Laplacian of T(2) acted like a spatial-temporal "lens" by increasing the spatial-temporal resolution of correlation between 3-dimensional T(2) and the ear referenced alert but resting spontaneous qEEG. The severity of traumatic brain injury can be modeled by a linear transfer function that relates the molecular qMRI to qEEG resonant frequencies.

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.

Effects of partial sleep deprivation on slow waves during non-rapid eye movement sleep: A high density EEG investigation.

PubMed

Plante, David T; Goldstein, Michael R; Cook, Jesse D; Smith, Richard; Riedner, Brady A; Rumble, Meredith E; Jelenchick, Lauren; Roth, Andrea; Tononi, Giulio; Benca, Ruth M; Peterson, Michael J

2016-02-01

Changes in slow waves during non-rapid eye movement (NREM) sleep in response to acute total sleep deprivation are well-established measures of sleep homeostasis. This investigation utilized high-density electroencephalography (hdEEG) to examine topographic changes in slow waves during repeated partial sleep deprivation. Twenty-four participants underwent a 6-day sleep restriction protocol. Spectral and period-amplitude analyses of sleep hdEEG data were used to examine changes in slow wave energy, count, amplitude, and slope relative to baseline. Changes in slow wave energy were dependent on the quantity of NREM sleep utilized for analysis, with widespread increases during sleep restriction and recovery when comparing data from the first portion of the sleep period, but restricted to recovery sleep if the entire sleep episode was considered. Period-amplitude analysis was less dependent on the quantity of NREM sleep utilized, and demonstrated topographic changes in the count, amplitude, and distribution of slow waves, with frontal increases in slow wave amplitude, numbers of high-amplitude waves, and amplitude/slopes of low amplitude waves resulting from partial sleep deprivation. Topographic changes in slow waves occur across the course of partial sleep restriction and recovery. These results demonstrate a homeostatic response to partial sleep loss in humans. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Analysis of Slow-Wave Activity and Slow-Wave Oscillations Prior to Somnambulism

PubMed Central

Jaar, Olivier; Pilon, Mathieu; Carrier, Julie; Montplaisir, Jacques; Zadra, Antonio

2010-01-01

Study Objectivies: Several studies have investigated slow wave sleep EEG parameters, including slow-wave activity (SWA) in relation to somnambulism, but results have been both inconsistent and contradictory. The first goal of the present study was to conduct a quantitative analysis of sleepwalkers' sleep EEG by studying fluctuations in spectral power for delta (1-4 Hz) and slow delta (0.5-1 Hz) before the onset of somnambulistic episodes. A secondary aim was to detect slow-wave oscillations to examine changes in their amplitude and density prior to behavioral episodes. Participants: Twenty-two adult sleepwalkers were investigated polysomnographically following 25 h of sleep deprivation. Results: Analysis of patients' sleep EEG over the 200 sec prior to the episodes' onset revealed that the episodes were not preceded by a gradual increase in spectral power for either delta or slow delta over frontal, central, or parietal leads. However, time course comparisons revealed significant changes in the density of slow-wave oscillations as well as in very slow oscillations with significant increases occurring during the final 20 sec immediately preceding episode onset. Conclusions: The specificity of these sleep EEG parameters for the occurrence and diagnosis of NREM parasomnias remains to be determined. Citation: Jaar O; Pilon M; Carrier J; Montplaisir J; Zadra A. Analysis of slow-wave activity and slow-wave oscillations prior to somnambulism. SLEEP 2010;33(11):1511-1516. PMID:21102993

Sex-related differences in sleep slow wave activity in major depressive disorder: a high-density EEG investigation.

PubMed

Plante, David T; Landsness, Eric C; Peterson, Michael J; Goldstein, Michael R; Riedner, Brady A; Wanger, Timothy; Guokas, Jeffrey J; Tononi, Giulio; Benca, Ruth M

2012-09-18

Sleep disturbance plays an important role in major depressive disorder (MDD). Prior investigations have demonstrated that slow wave activity (SWA) during sleep is altered in MDD; however, results have not been consistent across studies, which may be due in part to sex-related differences in SWA and/or limited spatial resolution of spectral analyses. This study sought to characterize SWA in MDD utilizing high-density electroencephalography (hdEEG) to examine the topography of SWA across the cortex in MDD, as well as sex-related variation in SWA topography in the disorder. All-night recordings with 256 channel hdEEG were collected in 30 unipolar MDD subjects (19 women) and 30 age and sex-matched control subjects. Spectral analyses of SWA were performed to determine group differences. SWA was compared between MDD and controls, including analyses stratified by sex, using statistical non-parametric mapping to correct for multiple comparisons of topographic data. As a group, MDD subjects demonstrated significant increases in all-night SWA primarily in bilateral prefrontal channels. When stratified by sex, MDD women demonstrated global increases in SWA relative to age-matched controls that were most consistent in bilateral prefrontal regions; however, MDD men showed no significant differences relative to age-matched controls. Further analyses demonstrated increased SWA in MDD women was most prominent in the first portion of the night. Women, but not men with MDD demonstrate significant increases in SWA in multiple cortical areas relative to control subjects. Further research is warranted to investigate the role of SWA in MDD, and to clarify how increased SWA in women with MDD is related to the pathophysiology of the disorder.

EEG sleep activities react topographically different to GABAergic sleep modulation by flunitrazepam: relationship to regional distribution of benzodiazepine receptor subtypes?

PubMed

Scheuler, W

Spectral analysis was performed to study the response of various EEG sleep activities to a modification of GABAergic sleep regulation by flunitrazepam. We observed sleep stage- and sleep cycle-dependent differences in the topographic distribution of the reactions. An increase in power density was found in the frontal regions for the alpha 2 and sigma 1 frequency band whereas a decrease in power density was emphasized in the posterior regions for the delta and alpha 1 frequency band. These topographic differences might be related to the regional distribution of benzodiazepine receptor subtypes.

A New Statistical Model of Electroencephalogram Noise Spectra for Real-Time Brain-Computer Interfaces.

PubMed

Paris, Alan; Atia, George K; Vosoughi, Azadeh; Berman, Stephen A

2017-08-01

A characteristic of neurological signal processing is high levels of noise from subcellular ion channels up to whole-brain processes. In this paper, we propose a new model of electroencephalogram (EEG) background periodograms, based on a family of functions which we call generalized van der Ziel-McWhorter (GVZM) power spectral densities (PSDs). To the best of our knowledge, the GVZM PSD function is the only EEG noise model that has relatively few parameters, matches recorded EEG PSD's with high accuracy from 0 to over 30 Hz, and has approximately 1/f θ behavior in the midfrequencies without infinities. We validate this model using three approaches. First, we show how GVZM PSDs can arise in a population of ion channels at maximum entropy equilibrium. Second, we present a class of mixed autoregressive models, which simulate brain background noise and whose periodograms are asymptotic to the GVZM PSD. Third, we present two real-time estimation algorithms for steady-state visual evoked potential (SSVEP) frequencies, and analyze their performance statistically. In pairwise comparisons, the GVZM-based algorithms showed statistically significant accuracy improvement over two well-known and widely used SSVEP estimators. The GVZM noise model can be a useful and reliable technique for EEG signal processing. Understanding EEG noise is essential for EEG-based neurology and applications such as real-time brain-computer interfaces, which must make accurate control decisions from very short data epochs. The GVZM approach represents a successful new paradigm for understanding and managing this neurological noise.

No short-term effects of digital mobile radio telephone on the awake human electroencephalogram

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roeschke, J.; Mann, K.

1997-05-01

A recent study reported the results of an exploratory study of alterations of the quantitative sleep profile due to the effects of a digital mobile radio telephone. Rapid eye movement (REM) was suppressed, and the spectral power density in the 8--13 Hz frequency range during REM sleep was altered. The aim of the present study was to illuminate the influence of digital mobile radio telephone on the awake electroencephalogram (EEG) of healthy subjects. For this purpose, the authors investigated 34 male subjects in a single-blind cross-over design experiment by measuring spontaneous EEGs under closed-eyes condition from scalp positions C{sub 3}more » and C{sub 4} and comparing the effects of an active and an inactive digital mobile radio telephone (GSM) system. During exposure of nearly 3.5 min to the 900 MHz electromagnetic field pulsed at a frequency of 217 Hz and with a pulse width of 580 {micro}s, the authors could not detect any difference in the awake EEGs in terms of spectral power density measures.« less

Towards a unified understanding of event-related changes in the EEG: the firefly model of synchronization through cross-frequency phase modulation.

PubMed

Burgess, Adrian P

2012-01-01

Although event-related potentials (ERPs) are widely used to study sensory, perceptual and cognitive processes, it remains unknown whether they are phase-locked signals superimposed upon the ongoing electroencephalogram (EEG) or result from phase-alignment of the EEG. Previous attempts to discriminate between these hypotheses have been unsuccessful but here a new test is presented based on the prediction that ERPs generated by phase-alignment will be associated with event-related changes in frequency whereas evoked-ERPs will not. Using empirical mode decomposition (EMD), which allows measurement of narrow-band changes in the EEG without predefining frequency bands, evidence was found for transient frequency slowing in recognition memory ERPs but not in simulated data derived from the evoked model. Furthermore, the timing of phase-alignment was frequency dependent with the earliest alignment occurring at high frequencies. Based on these findings, the Firefly model was developed, which proposes that both evoked and induced power changes derive from frequency-dependent phase-alignment of the ongoing EEG. Simulated data derived from the Firefly model provided a close match with empirical data and the model was able to account for i) the shape and timing of ERPs at different scalp sites, ii) the event-related desynchronization in alpha and synchronization in theta, and iii) changes in the power density spectrum from the pre-stimulus baseline to the post-stimulus period. The Firefly Model, therefore, provides not only a unifying account of event-related changes in the EEG but also a possible mechanism for cross-frequency information processing.

Towards a Unified Understanding of Event-Related Changes in the EEG: The Firefly Model of Synchronization through Cross-Frequency Phase Modulation

PubMed Central

Burgess, Adrian P.

2012-01-01

Although event-related potentials (ERPs) are widely used to study sensory, perceptual and cognitive processes, it remains unknown whether they are phase-locked signals superimposed upon the ongoing electroencephalogram (EEG) or result from phase-alignment of the EEG. Previous attempts to discriminate between these hypotheses have been unsuccessful but here a new test is presented based on the prediction that ERPs generated by phase-alignment will be associated with event-related changes in frequency whereas evoked-ERPs will not. Using empirical mode decomposition (EMD), which allows measurement of narrow-band changes in the EEG without predefining frequency bands, evidence was found for transient frequency slowing in recognition memory ERPs but not in simulated data derived from the evoked model. Furthermore, the timing of phase-alignment was frequency dependent with the earliest alignment occurring at high frequencies. Based on these findings, the Firefly model was developed, which proposes that both evoked and induced power changes derive from frequency-dependent phase-alignment of the ongoing EEG. Simulated data derived from the Firefly model provided a close match with empirical data and the model was able to account for i) the shape and timing of ERPs at different scalp sites, ii) the event-related desynchronization in alpha and synchronization in theta, and iii) changes in the power density spectrum from the pre-stimulus baseline to the post-stimulus period. The Firefly Model, therefore, provides not only a unifying account of event-related changes in the EEG but also a possible mechanism for cross-frequency information processing. PMID:23049827

Fetal Cerebrovascular Resistance and Neonatal EEG Predict 18-month Neurodevelopmental Outcome in Infants with Congenital Heart Disease

PubMed Central

Williams, Ismee A.; Tarullo, Amanda R.; Grieve, Philip G.; Wilpers, Abigail; Vignola, Emilia F.; Myers, Michael M.; Fifer, William P.

2012-01-01

Objectives The purpose of this study was to investigate early markers of risk for neurobehavioral compromise in congenital heart disease (CHD) survivors. Methods Fetuses < 24 wks gestational age (GA) were enrolled in this prospective pilot study for serial Doppler assessment of the middle cerebral and umbilical artery. The cerebral-to-placental resistance ratio (CPR) and MCA pulsatility index (PI) z-scores for GA were calculated. After birth, subjects underwent high-density (128-lead) electroencephalogram (EEG) and beta frequency (12–24Hz) band EEG power, a measure of local neural synchrony, was analyzed. Neurodevelopment was assessed at 18-months with the Bayley Scales of Infant Development III (BSID). Results 13 subjects were enrolled: 4 with hypoplastic left heart syndrome (HLHS), 4 with transposition of the great arteries (TGA), and 5 with tetralogy of Fallot (TOF). Compared with subjects with normal CPR, those with CPR<1(N=7) had lower mean BSID cognitive scores (91.4±4.8 vs. 99.2±3.8, p=.008). Fetal MCA PI z-score also correlated with BSID cognitive score (r=.589, p=0.044) as did neonatal EEG left frontal polar (r=.58, p=.037) and left frontal (r=.77,p=.002) beta power. Furthermore, fetal Doppler measures were associated with EEG power: fetuses with CPR<1 had lower left frontal polar (t=2.36, p=.038) and left frontal (t=2.85, p=.016) beta power as newborns compared with fetuses with normal CPR, and fetal MCA PI z-score correlated with neonatal EEG left frontal polar (r=.596, p=.04) and left frontal (r=.598, p=.04) beta power. Conclusions In CHD fetuses with HLHS, TGA, and TOF, abnormal cerebrovascular resistance predicted decreased neonatal EEG left frontal beta power and lower 18-mo cognitive development scores. PMID:22351034

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.

Temporal correlation between two channels EEG of bipolar lead in the head midline is associated with sleep-wake stages.

PubMed

Li, Yanjun; Tang, Xiaoying; Xu, Zhi; Liu, Weifeng; Li, Jing

2016-03-01

Whether the temporal correlation between inter-leads Electroencephalogram (EEG) that located on the boundary between left and right brain hemispheres is associated with sleep stages or not is still unknown. The purpose of this paper is to evaluate the role of correlation coefficients between EEG leads Fpz-Cz and Pz-Oz for automatic classification of sleep stages. A total number of 39 EEG recordings (about 20 h each) were selected from the expanded sleep database in European data format for temporal correlation analysis. Original waveform of EEG was decomposed into sub-bands δ (1-4 Hz), θ (4-8 Hz), α (8-13 Hz) and β (13-30 Hz). The correlation coefficient between original EEG leads Fpz-Cz and Pz-Oz within frequency band 0.5-30 Hz was defined as r(EEG) and was calculated every 30 s, while that between the two leads EEG in sub-bands δ, θ, α and β were defined as r(δ), r(θ), r(α) and r(β), respectively. Classification of wakefulness and sleep was processed by fixed threshold that derived from the probability density function of correlation coefficients. There was no correlation between EEG leads Fpz-Cz and Pz-Oz during wakefulness (|r| < 0.1 for r(θ), r(α) and r(β), while 0.3 > r > 0.1 for r(EEG) and r(δ)), while low correlation existed during sleep (r ≈ -0.4 for r(EEG), r(δ), r(θ), r(α) and r(β)). There were significant differences (analysis of variance, P < 0.001) for r(EEG), r(δ), r(θ), r(α) and r(β) during sleep when in comparison with that during wakefulness, respectively. The accuracy for distinguishing states between wakefulness and sleep was 94.2, 93.4, 89.4, 85.2 and 91.4% in terms of r(EEG), r(δ), r(θ), r(α) and r(β), respectively. However, no correlation index between EEG leads Fpz-Cz and Pz-Oz could distinguish all five types of wakefulness, rapid eye movement (REM) sleep, N1 sleep, N2 sleep and N3 sleep. In conclusion, the temporal correlation between EEG bipolar leads Fpz-Cz and Pz-Oz are highly associated with sleep-wake stages. Moreover, high accuracy of sleep-wake classification could be achieved by the temporal correlation within frequency band 0.5-30 Hz between EEG leads Fpz-Cz and Pz-Oz.

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.

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

Electroencephalographic markers of robot-aided therapy in stroke patients for the evaluation of upper limb rehabilitation.

PubMed

Sale, Patrizio; Infarinato, Francesco; Del Percio, Claudio; Lizio, Roberta; Babiloni, Claudio; Foti, Calogero; Franceschini, Marco

2015-12-01

Stroke is the leading cause of permanent disability in developed countries; its effects may include sensory, motor, and cognitive impairment as well as a reduced ability to perform self-care and participate in social and community activities. A number of studies have shown that the use of robotic systems in upper limb motor rehabilitation programs provides safe and intensive treatment to patients with motor impairments because of a neurological injury. Furthermore, robot-aided therapy was shown to be well accepted and tolerated by all patients; however, it is not known whether a specific robot-aided rehabilitation can induce beneficial cortical plasticity in stroke patients. Here, we present a procedure to study neural underpinning of robot-aided upper limb rehabilitation in stroke patients. Neurophysiological recordings use the following: (a) 10-20 system electroencephalographic (EEG) electrode montage; (b) bipolar vertical and horizontal electrooculographies; and (c) bipolar electromyography from the operating upper limb. Behavior monitoring includes the following: (a) clinical data and (b) kinematic and dynamic of the operant upper limb movements. Experimental conditions include the following: (a) resting state eyes closed and eyes open, and (b) robotic rehabilitation task (maximum 80 s each block to reach 4-min EEG data; interblock pause of 1 min). The data collection is performed before and after a program of 30 daily rehabilitation sessions. EEG markers include the following: (a) EEG power density in the eyes-closed condition; (b) reactivity of EEG power density to eyes opening; and (c) reactivity of EEG power density to robotic rehabilitation task. The above procedure was tested on a subacute patient (29 poststroke days) and on a chronic patient (21 poststroke months). After the rehabilitation program, we observed (a) improved clinical condition; (b) improved performance during the robotic task; (c) reduced delta rhythms (1-4 Hz) and increased alpha rhythms (8-12 Hz) during the resting state eyes-closed condition; (d) increased alpha desynchronization to eyes opening; and (e) decreased alpha desynchronization during the robotic rehabilitation task. We conclude that the present procedure is suitable for evaluation of the neural underpinning of robot-aided upper limb rehabilitation.

Short Meditation Trainings Enhance Non-REM Sleep Low-Frequency Oscillations.

PubMed

Dentico, Daniela; Ferrarelli, Fabio; Riedner, Brady A; Smith, Richard; Zennig, Corinna; Lutz, Antoine; Tononi, Giulio; Davidson, Richard J

2016-01-01

We have recently shown higher parietal-occipital EEG gamma activity during sleep in long-term meditators compared to meditation-naive individuals. This gamma increase was specific for NREM sleep, was present throughout the entire night and correlated with meditation expertise, thus suggesting underlying long-lasting neuroplastic changes induced through prolonged training. The aim of this study was to explore the neuroplastic changes acutely induced by 2 intensive days of different meditation practices in the same group of practitioners. We also repeated baseline recordings in a meditation-naive cohort to account for time effects on sleep EEG activity. High-density EEG recordings of human brain activity were acquired over the course of whole sleep nights following intervention. Sound-attenuated sleep research room. Twenty-four long-term meditators and twenty-four meditation-naïve controls. Two 8-h sessions of either a mindfulness-based meditation or a form of meditation designed to cultivate compassion and loving kindness, hereafter referred to as compassion meditation. We found an increase in EEG low-frequency oscillatory activities (1-12 Hz, centered around 7-8 Hz) over prefrontal and left parietal electrodes across whole night NREM cycles. This power increase peaked early in the night and extended during the third cycle to high-frequencies up to the gamma range (25-40 Hz). There was no difference in sleep EEG activity between meditation styles in long-term meditators nor in the meditation naïve group across different time points. Furthermore, the prefrontal-parietal changes were dependent on meditation life experience. This low-frequency prefrontal-parietal activation likely reflects acute, meditation-related plastic changes occurring during wakefulness, and may underlie a top-down regulation from frontal and anterior parietal areas to the posterior parietal and occipital regions showing chronic, long-lasting plastic changes in long-term meditators.

Neural processing of emotions in traumatized children treated with Eye Movement Desensitization and Reprocessing therapy: a hdEEG study

PubMed Central

Trentini, Cristina; Pagani, Marco; Fania, Piercarlo; Speranza, Anna Maria; Nicolais, Giampaolo; Sibilia, Alessandra; Inguscio, Lucio; Verardo, Anna Rita; Fernandez, Isabel; Ammaniti, Massimo

2015-01-01

Eye Movement Desensitization and Reprocessing (EMDR) therapy has been proven efficacious in restoring affective regulation in post-traumatic stress disorder (PTSD) patients. However, its effectiveness on emotion processing in children with complex trauma has yet to be explored. High density electroencephalography (hdEEG) was used to investigate the effects of EMDR on brain responses to adults’ emotions on children with histories of early maltreatment. Ten school-aged children were examined before (T0) and within one month after the conclusion of EMDR (T1). hdEEGs were recorded while children passively viewed angry, afraid, happy, and neutral faces. Clinical scales were administered at the same time. Correlation analyses were performed to detect brain regions whose activity was linked to children’s traumatic symptom-related and emotional-adaptive problem scores. In all four conditions, hdEEG showed similar significantly higher activity on the right medial prefrontal and fronto-temporal limbic regions at T0, shifting toward the left medial and superior temporal regions at T1. Moreover, significant correlations were found between clinical scales and the same regions whose activity significantly differed between pre- and post-treatment. These preliminary results demonstrate that, after EMDR, children suffering from complex trauma show increased activity in areas implicated in high-order cognitive processing when passively viewing pictures of emotional expressions. These changes are associated with the decrease of depressive and traumatic symptoms, and with the improvement of emotional-adaptive functioning over time. PMID:26594183

Comparison of Medical and Consumer Wireless EEG Systems for Use in Clinical Trials.

PubMed

Ratti, Elena; Waninger, Shani; Berka, Chris; Ruffini, Giulio; Verma, Ajay

2017-01-01

Objectives: To compare quantitative EEG signal and test-retest reliability of medical grade and consumer EEG systems. Methods: Resting state EEG was acquired by two medical grade (B-Alert, Enobio) and two consumer (Muse, Mindwave) EEG systems in five healthy subjects during two study visits. EEG patterns, power spectral densities (PSDs) and test/retest reliability in eyes closed and eyes open conditions were compared across the four systems, focusing on Fp1, the only common electrode. Fp1 PSDs were obtained using Welch's modified periodogram method and averaged for the five subjects for each visit. The test/retest results were calculated as a ratio of Visit 1/Visit 2 Fp1 channel PSD at each 1 s epoch. Results: B-Alert, Enobio, and Mindwave Fp1 power spectra were similar. Muse showed a broadband increase in power spectra and the highest relative variation across test-retest acquisitions. Consumer systems were more prone to artifact due to eye blinks and muscle movement in the frontal region. Conclusions: EEG data can be successfully collected from all four systems tested. Although there was slightly more time required for application, medical systems offer clear advantages in data quality, reliability, and depth of analysis over the consumer systems. Significance: This evaluation provides evidence for informed selection of EEG systemsappropriate for clinical trials.

EEG and ocular correlates of circadian melatonin phase and human performance decrements during sleep loss

NASA Technical Reports Server (NTRS)

Cajochen, C.; Khalsa, S. B.; Wyatt, J. K.; Czeisler, C. A.; Dijk, D. J.

1999-01-01

The aim of this study was to quantify the associations between slow eye movements (SEMs), eye blink rate, waking electroencephalogram (EEG) power density, neurobehavioral performance, and the circadian rhythm of plasma melatonin in a cohort of 10 healthy men during up to 32 h of sustained wakefulness. The time course of neurobehavioral performance was characterized by fairly stable levels throughout the first 16 h of wakefulness followed by deterioration during the phase of melatonin secretion. This deterioration was closely associated with an increase in SEMs. Frontal low-frequency EEG activity (1-7 Hz) exhibited a prominent increase with time awake and little circadian modulation. EEG alpha activity exhibited circadian modulation. The dynamics of SEMs and EEG activity were phase locked to changes in neurobehavioral performance and lagged the plasma melatonin rhythm. The data indicate that frontal areas of the brain are more susceptible to sleep loss than occipital areas. Frontal EEG activity and ocular parameters may be used to monitor and predict changes in neurobehavioral performance associated with sleep loss and circadian misalignment.

Technical and clinical analysis of microEEG: a miniature wireless EEG device designed to record high-quality EEG in the emergency department

PubMed Central

2012-01-01

Background We describe and characterize the performance of microEEG compared to that of a commercially available and widely used clinical EEG machine. microEEG is a portable, battery-operated, wireless EEG device, developed by Bio-Signal Group to overcome the obstacles to routine use of EEG in emergency departments (EDs). Methods The microEEG was used to obtain EEGs from healthy volunteers in the EEG laboratory and ED. The standard system was used to obtain EEGs from healthy volunteers in the EEG laboratory, and studies recorded from patients in the ED or ICU were also used for comparison. In one experiment, a signal splitter was used to record simultaneous microEEG and standard EEG from the same electrodes. Results EEG signal analysis techniques indicated good agreement between microEEG and the standard system in 66 EEGs recorded in the EEG laboratory and the ED. In the simultaneous recording the microEEG and standard system signals differed only in a smaller amount of 60 Hz noise in the microEEG signal. In a blinded review by a board-certified clinical neurophysiologist, differences in technical quality or interpretability were insignificant between standard recordings in the EEG laboratory and microEEG recordings from standard or electrode cap electrodes in the ED or EEG laboratory. The microEEG data recording characteristics such as analog-to-digital conversion resolution (16 bits), input impedance (>100MΩ), and common-mode rejection ratio (85 dB) are similar to those of commercially available systems, although the microEEG is many times smaller (88 g and 9.4 × 4.4 × 3.8 cm). Conclusions Our results suggest that the technical qualities of microEEG are non-inferior to a standard commercially available EEG recording device. EEG in the ED is an unmet medical need due to space and time constraints, high levels of ambient electrical noise, and the cost of 24/7 EEG technologist availability. This study suggests that using microEEG with an electrode cap that can be applied easily and quickly can surmount these obstacles without compromising technical quality. PMID:23006616

Comparison between psycho-acoustics and physio-acoustic measurement to determine optimum reverberation time of pentatonic angklung music concert hall

NASA Astrophysics Data System (ADS)

Sudarsono, Anugrah S.; Merthayasa, I. G. N.; Suprijanto

2015-09-01

This research tried to compare psycho-acoustics and Physio-acoustic measurement to find the optimum reverberation time of soundfield from angklung music. Psycho-acoustic measurement was conducted using a paired comparison method and Physio-acoustic measurement was conducted with EEG Measurement on T3, T4, FP1, and FP2 measurement points. EEG measurement was conducted with 5 persons. Pentatonic angklung music was used as a stimulus with reverberation time variation. The variation was between 0.8 s - 1.6 s with 0.2 s step. EEG signal was analysed using a Power Spectral Density method on Alpha Wave, High Alpha Wave, and Theta Wave. Psycho-acoustic measurement on 50 persons showed that reverberation time preference of pentatonic angklung music was 1.2 second. The result was similar to Theta Wave measurement on FP2 measurement point. High Alpha wave on T4 measurement gave different results, but had similar patterns with psycho-acoustic measurement

Increased overall cortical connectivity with syndrome specific local decreases suggested by atypical sleep-EEG synchronization in Williams syndrome.

PubMed

Gombos, Ferenc; Bódizs, Róbert; Kovács, Ilona

2017-07-21

Williams syndrome (7q11.23 microdeletion) is characterized by specific alterations in neurocognitive architecture and functioning, as well as disordered sleep. Here we analyze the region, sleep state and frequency-specific EEG synchronization of whole night sleep recordings of 21 Williams syndrome and 21 typically developing age- and gender-matched subjects by calculating weighted phase lag indexes. We found broadband increases in inter- and intrahemispheric neural connectivity for both NREM and REM sleep EEG of Williams syndrome subjects. These effects consisted of increased theta, high sigma, and beta/low gamma synchronization, whereas alpha synchronization was characterized by a peculiar Williams syndrome-specific decrease during NREM states (intra- and interhemispheric centro-temporal) and REM phases of sleep (occipital intra-area synchronization). We also found a decrease in short range, occipital connectivity of NREM sleep EEG theta activity. The striking increased overall synchronization of sleep EEG in Williams syndrome subjects is consistent with the recently reported increase in synaptic and dendritic density in stem-cell based Williams syndrome models, whereas decreased alpha and occipital connectivity might reflect and underpin the altered microarchitecture of primary visual cortex and disordered visuospatial functioning of Williams syndrome subjects.

QEEG and LORETA in Teenagers With Conduct Disorder and Psychopathic Traits.

PubMed

Calzada-Reyes, Ana; Alvarez-Amador, Alfredo; Galán-García, Lídice; Valdés-Sosa, Mitchell

2017-05-01

Few studies have investigated the impact of the psychopathic traits on the EEG of teenagers with conduct disorder (CD). To date, there is no other research studying low-resolution brain electromagnetic tomography (LORETA) technique using quantitative EEG (QEEG) analysis in adolescents with CD and psychopathic traits. To find electrophysiological differences specifically related to the psychopathic traits. The current investigation compares the QEEG and the current source density measures between adolescents with CD and psychopathic traits and adolescents with CD without psychopathic traits. The resting EEG activity and LORETA for the EEG fast spectral bands were evaluated in 42 teenagers with CD, 25 with and 17 without psychopathic traits according to the Antisocial Process Screening Device. All adolescents were assessed using the DSM-IV-TR criteria. The EEG visual inspection characteristics and the use of frequency domain quantitative analysis techniques (narrow band spectral parameters) are described. QEEG analysis showed a pattern of beta activity excess on the bilateral frontal-temporal regions and decreases of alpha band power on the left central-temporal and right frontal-central-temporal regions in the psychopathic traits group. Current source density calculated at 17.18 Hz showed an increase within fronto-temporo-striatal regions in the psychopathic relative to the nonpsychopathic traits group. These findings indicate that QEEG analysis and techniques of source localization may reveal differences in brain electrical activity among teenagers with CD and psychopathic traits, which was not obvious to visual inspection. Taken together, these results suggest that abnormalities in a fronto-temporo-striatal network play a relevant role in the neurobiological basis of psychopathic behavior.

Neuroelectrical imaging investigation of cortical activity during listening to music in prelingually deaf children with cochlear implants.

PubMed

Marsella, Pasquale; Scorpecci, Alessandro; Vecchiato, Giovanni; Maglione, Anton Giulio; Colosimo, Alfredo; Babiloni, Fabio

2014-05-01

To date, no objective measure of the pleasantness of music perception by children with cochlear implants has been reported. The EEG alpha asymmetries of pre-frontal cortex activation are known to relate to emotional/affective engagement in a perceived stimulus. More specifically, according to the "withdrawal/approach" model, an unbalanced de-synchronization of the alpha activity in the left prefrontal cortex has been associated with a positive affective state/approach toward a stimulus, and an unbalanced de-synchronization of the same activity in the right prefrontal cortex with a negative affective state/withdrawal from a stimulus. In the present study, High-Resolution EEG with Source Reconstruction was used to compare the music-induced alpha asymmetries of the prefrontal cortex in a group of prelingually deaf implanted children and in a control group of normal-hearing children. Six normal-hearing and six age-matched deaf children using a unilateral cochlear implants underwent High-Resolution EEG recordings as they were listening to a musical cartoon. Musical stimuli were delivered in three versions: Normal, Distort (reverse audio flow) and Mute. The EEG alpha rhythm asymmetry was analyzed: Power Spectral Density was calculated for each Region of Interest, together with a right-left imbalance index. A map of cortical activation was then reconstructed on a realistic cortical model. Asymmetries of EEG alpha rhythm in the prefrontal cortices were observed in both groups. In the normal-hearing children, the asymmetries were consistent with the withdrawal/approach model, whereas in cochlear implant users they were not. Moreover, in implanted children a different pattern of alpha asymmetries in extrafrontal cortical areas was noticed as compared to normal-hearing subjects. The peculiar pattern of alpha asymmetries in implanted children's prefrontal cortex in response to musical stimuli suggests an inability by these subjects to discriminate normal from dissonant music and to appreciate the pleasantness of normal music. High-Resolution EEG may prove to be a promising tool for objectively measuring prefrontal cortex alpha asymmetries in child cochlear implant users. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Short Meditation Trainings Enhance Non-REM Sleep Low-Frequency Oscillations

PubMed Central

Dentico, Daniela; Ferrarelli, Fabio; Riedner, Brady A.; Smith, Richard; Zennig, Corinna; Lutz, Antoine; Tononi, Giulio; Davidson, Richard J.

2016-01-01

Study Objectives We have recently shown higher parietal-occipital EEG gamma activity during sleep in long-term meditators compared to meditation-naive individuals. This gamma increase was specific for NREM sleep, was present throughout the entire night and correlated with meditation expertise, thus suggesting underlying long-lasting neuroplastic changes induced through prolonged training. The aim of this study was to explore the neuroplastic changes acutely induced by 2 intensive days of different meditation practices in the same group of practitioners. We also repeated baseline recordings in a meditation-naive cohort to account for time effects on sleep EEG activity. Design High-density EEG recordings of human brain activity were acquired over the course of whole sleep nights following intervention. Setting Sound-attenuated sleep research room. Patients or Participants Twenty-four long-term meditators and twenty-four meditation-naïve controls. Interventions Two 8-h sessions of either a mindfulness-based meditation or a form of meditation designed to cultivate compassion and loving kindness, hereafter referred to as compassion meditation. Measurements and Results We found an increase in EEG low-frequency oscillatory activities (1–12 Hz, centered around 7–8 Hz) over prefrontal and left parietal electrodes across whole night NREM cycles. This power increase peaked early in the night and extended during the third cycle to high-frequencies up to the gamma range (25–40 Hz). There was no difference in sleep EEG activity between meditation styles in long-term meditators nor in the meditation naïve group across different time points. Furthermore, the prefrontal-parietal changes were dependent on meditation life experience. Conclusions This low-frequency prefrontal-parietal activation likely reflects acute, meditation-related plastic changes occurring during wakefulness, and may underlie a top-down regulation from frontal and anterior parietal areas to the posterior parietal and occipital regions showing chronic, long-lasting plastic changes in long-term meditators. PMID:26900914

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

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.

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

Heart Rate Variability Can Be Used to Estimate Sleepiness-related Decrements in Psychomotor Vigilance during Total Sleep Deprivation

PubMed Central

Chua, Eric Chern-Pin; Tan, Wen-Qi; Yeo, Sing-Chen; Lau, Pauline; Lee, Ivan; Mien, Ivan Ho; Puvanendran, Kathiravelu; Gooley, Joshua J.

2012-01-01

Study Objectives: To assess whether changes in psychomotor vigilance during sleep deprivation can be estimated using heart rate variability (HRV). Design: HRV, ocular, and electroencephalogram (EEG) measures were compared for their ability to predict lapses on the Psychomotor Vigilance Task (PVT). Setting: Chronobiology and Sleep Laboratory, Duke-NUS Graduate Medical School Singapore. Participants: Twenty-four healthy Chinese men (mean age ± SD = 25.9 ± 2.8 years). Interventions: Subjects were kept awake continuously for 40 hours under constant environmental conditions. Every 2 hours, subjects completed a 10-minute PVT to assess their ability to sustain visual attention. Measurements and Results: During each PVT, we examined the electrocardiogram (ECG), EEG, and percentage of time that the eyes were closed (PERCLOS). Similar to EEG power density and PERCLOS measures, the time course of ECG RR-interval power density in the 0.02- 0.08-Hz range correlated with the 40-hour profile of PVT lapses. Based on receiver operating characteristic curves, RR-interval power density performed as well as EEG power density at identifying a sleepiness-related increase in PVT lapses above threshold. RR-interval power density (0.02-0.08 Hz) also classified subject performance with sensitivity and specificity similar to that of PERCLOS. Conclusions: The ECG carries information about a person's vigilance state. Hence, HRV measures could potentially be used to predict when an individual is at increased risk of attentional failure. Our results suggest that HRV monitoring, either alone or in combination with other physiologic measures, could be incorporated into safety devices to warn drowsy operators when their performance is impaired. Citation: Chua ECP; Tan WQ; Yeo SC; Lau P; Lee I; Mien IH; Puvanendran K; Gooley JJ. Heart rate variability can be used to estimate sleepiness-related decrements in psychomotor vigilance during total sleep deprivation. SLEEP 2012;35(3):325-334. PMID:22379238

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.

Permanency analysis on human electroencephalogram signals for pervasive Brain-Computer Interface systems.

PubMed

Sadeghi, Koosha; Junghyo Lee; Banerjee, Ayan; Sohankar, Javad; Gupta, Sandeep K S

2017-07-01

Brain-Computer Interface (BCI) systems use some permanent features of brain signals to recognize their corresponding cognitive states with high accuracy. However, these features are not perfectly permanent, and BCI system should be continuously trained over time, which is tedious and time consuming. Thus, analyzing the permanency of signal features is essential in determining how often to repeat training. In this paper, we monitor electroencephalogram (EEG) signals, and analyze their behavior through continuous and relatively long period of time. In our experiment, we record EEG signals corresponding to rest state (eyes open and closed) from one subject everyday, for three and a half months. The results show that signal features such as auto-regression coefficients remain permanent through time, while others such as power spectral density specifically in 5-7 Hz frequency band are not permanent. In addition, eyes open EEG data shows more permanency than eyes closed data.

The influence of low frequency sound on the changes of EEG signal morphology

NASA Astrophysics Data System (ADS)

Damijan, Z.; Wiciak, J.

2006-11-01

The effects of low frequency sound on the changes of morphology of the spectral power density function of EEG signals were studied as a part of the research program f = 40 Hz, Lp = 110 dB HP. The research program involved 33 experiments. A quantitative analysis was conducted of the driving response effect for the fundamental frequency and its harmonics to find the frequency of the driving response effect occurrence depending on the sex of participants.

Heart-Brain Interactions in the MR Environment: Characterization of the Ballistocardiogram in EEG Signals Collected During Simultaneous fMRI.

PubMed

Marino, Marco; Liu, Quanying; Del Castello, Mariangela; Corsi, Cristiana; Wenderoth, Nicole; Mantini, Dante

2018-05-01

The ballistocardiographic (BCG) artifact is linked to cardiac activity and occurs in electroencephalographic (EEG) recordings acquired inside the magnetic resonance (MR) environment. Its variability in terms of amplitude, waveform shape and spatial distribution over subject's scalp makes its attenuation a challenging task. In this study, we aimed to provide a detailed characterization of the BCG properties, including its temporal dependency on cardiac events and its spatio-temporal dynamics. To this end, we used high-density EEG data acquired during simultaneous functional MR imaging in six healthy volunteers. First, we investigated the relationship between cardiac activity and BCG occurrences in the EEG recordings. We observed large variability in the delay between ECG and subsequent BCG events (ECG-BCG delay) across subjects and non-negligible epoch-by-epoch variations at the single subject level. The inspection of spatial-temporal variations revealed a prominent non-stationarity of the BCG signal. We identified five main BCG waves, which were common across subjects. Principal component analysis revealed two spatially distinct patterns to explain most of the variance (85% in total). These components are possibly related to head rotation and pulse-driven scalp expansion, respectively. Our results may inspire the development of novel, more effective methods for the removal of the BCG, capable of isolating and attenuating artifact occurrences while preserving true neuronal activity.

Comparative analysis of background EEG activity in childhood absence epilepsy during valproate treatment: a standardized, low-resolution, brain electromagnetic tomography (sLORETA) study.

PubMed

Shin, Jung-Hyun; Eom, Tae-Hoon; Kim, Young-Hoon; Chung, Seung-Yun; Lee, In-Goo; Kim, Jung-Min

2017-07-01

Valproate (VPA) is an antiepileptic drug (AED) used for initial monotherapy in treating childhood absence epilepsy (CAE). EEG might be an alternative approach to explore the effects of AEDs on the central nervous system. We performed a comparative analysis of background EEG activity during VPA treatment by using standardized, low-resolution, brain electromagnetic tomography (sLORETA) to explore the effect of VPA in patients with CAE. In 17 children with CAE, non-parametric statistical analyses using sLORETA were performed to compare the current density distribution of four frequency bands (delta, theta, alpha, and beta) between the untreated and treated condition. Maximum differences in current density were found in the left inferior frontal gyrus for the delta frequency band (log-F-ratio = -1.390, P > 0.05), the left medial frontal gyrus for the theta frequency band (log-F-ratio = -0.940, P > 0.05), the left inferior frontal gyrus for the alpha frequency band (log-F-ratio = -0.590, P > 0.05), and the left anterior cingulate for the beta frequency band (log-F-ratio = -1.318, P > 0.05). However, none of these differences were significant (threshold log-F-ratio = ±1.888, P < 0.01; threshold log-F-ratio = ±1.722, P < 0.05). Because EEG background is accepted as normal in CAE, VPA would not be expected to significantly change abnormal thalamocortical oscillations on a normal EEG background. Therefore, our results agree with currently accepted concepts but are not consistent with findings in some previous studies.

Electroencephalography Spectral Power Density in First-Episode Mania: A Comparative Study with Subsequent Remission Period.

PubMed

Güven, Sertaç; Kesebir, Sermin; Demirer, R Murat; Bilici, Mustafa

2015-06-01

Our aim in this study was to investigate spectral power density (PSD) in first-episode mania and subsequent remission period and to evaluate their difference. Sixty-nine consecutive cases referring to our hospital within the previous 1 year, who were evaluated as bipolar disorder manic episode according to The Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) at the first episode and had the informed consent form signed by first degree relatives, were included in this study. Exclusion criteria included having previous depressive episode, using drugs which could influence electroencephalographic activity before electroencephalography (EEG), and having previous neurological disease, particularly epilepsy, head trauma, and/or loss of consciousness. EEG records were obtained using a digital device in 16 channels; 23 surface electrodes were placed according to the International 10-20 system. Spectral power density (dbμV/Hz) of EEG signal provided information on the power carried out by EEG waves in defined frequancy range per unit frequency in the present study. A peak power value detected on the right with FP2P4 and on the left with F7T3 electrodes were found to be higher in the manic episode than in the remission period (p=0.018 and 0.025). In the remission period, in cases with psychotic symptoms during the manic period, F4C4 peak power value was found to be lower than that in cases with no psychotic findings during the manic period (p=0.027). There was no relation was found between YMRS scores and peak power scores. Electrophysiological corollary of mood episode is present from the onset of the disease, and it differs between the manic and remission periods of bipolar disorder. In the remission period, peak power values of PSD distinguish cases with psychotic findings from cases without psychotic findings when they were manic.

Spatiotemporal dynamics of the brain at rest--exploring EEG microstates as electrophysiological signatures of BOLD resting state networks.

PubMed

Yuan, Han; Zotev, Vadim; Phillips, Raquel; Drevets, Wayne C; Bodurka, Jerzy

2012-05-01

Neuroimaging research suggests that the resting cerebral physiology is characterized by complex patterns of neuronal activity in widely distributed functional networks. As studied using functional magnetic resonance imaging (fMRI) of the blood-oxygenation-level dependent (BOLD) signal, the resting brain activity is associated with slowly fluctuating hemodynamic signals (~10s). More recently, multimodal functional imaging studies involving simultaneous acquisition of BOLD-fMRI and electroencephalography (EEG) data have suggested that the relatively slow hemodynamic fluctuations of some resting state networks (RSNs) evinced in the BOLD data are related to much faster (~100 ms) transient brain states reflected in EEG signals, that are referred to as "microstates". To further elucidate the relationship between microstates and RSNs, we developed a fully data-driven approach that combines information from simultaneously recorded, high-density EEG and BOLD-fMRI data. Using independent component analysis (ICA) of the combined EEG and fMRI data, we identified thirteen microstates and ten RSNs that are organized independently in their temporal and spatial characteristics, respectively. We hypothesized that the intrinsic brain networks that are active at rest would be reflected in both the EEG data and the fMRI data. To test this hypothesis, the rapid fluctuations associated with each microstate were correlated with the BOLD-fMRI signal associated with each RSN. We found that each RSN was characterized further by a specific electrophysiological signature involving from one to a combination of several microstates. Moreover, by comparing the time course of EEG microstates to that of the whole-brain BOLD signal, on a multi-subject group level, we unraveled for the first time a set of microstate-associated networks that correspond to a range of previously described RSNs, including visual, sensorimotor, auditory, attention, frontal, visceromotor and default mode networks. These results extend our understanding of the electrophysiological signature of BOLD RSNs and demonstrate the intrinsic connection between the fast neuronal activity and slow hemodynamic fluctuations. Copyright © 2012 Elsevier Inc. All rights reserved.

Functional brain networks in healthy subjects under acupuncture stimulation: An EEG study based on nonlinear synchronization likelihood analysis

NASA Astrophysics Data System (ADS)

Yu, Haitao; Liu, Jing; Cai, Lihui; Wang, Jiang; Cao, Yibin; Hao, Chongqing

2017-02-01

Electroencephalogram (EEG) signal evoked by acupuncture stimulation at "Zusanli" acupoint is analyzed to investigate the modulatory effect of manual acupuncture on the functional brain activity. Power spectral density of EEG signal is first calculated based on the autoregressive Burg method. It is shown that the EEG power is significantly increased during and after acupuncture in delta and theta bands, but decreased in alpha band. Furthermore, synchronization likelihood is used to estimate the nonlinear correlation between each pairwise EEG signals. By applying a threshold to resulting synchronization matrices, functional networks for each band are reconstructed and further quantitatively analyzed to study the impact of acupuncture on network structure. Graph theoretical analysis demonstrates that the functional connectivity of the brain undergoes obvious change under different conditions: pre-acupuncture, acupuncture, and post-acupuncture. The minimum path length is largely decreased and the clustering coefficient keeps increasing during and after acupuncture in delta and theta bands. It is indicated that acupuncture can significantly modulate the functional activity of the brain, and facilitate the information transmission within different brain areas. The obtained results may facilitate our understanding of the long-lasting effect of acupuncture on the brain function.

Hemispheric asymmetry of electroencephalography-based functional brain networks.

PubMed

Jalili, Mahdi

2014-11-12

Electroencephalography (EEG)-based functional brain networks have been investigated frequently in health and disease. It has been shown that a number of graph theory metrics are disrupted in brain disorders. EEG-based brain networks are often studied in the whole-brain framework, where all the nodes are grouped into a single network. In this study, we studied the brain networks in two hemispheres and assessed whether there are any hemispheric-specific patterns in the properties of the networks. To this end, resting state closed-eyes EEGs from 44 healthy individuals were processed and the network structures were extracted separately for each hemisphere. We examined neurophysiologically meaningful graph theory metrics: global and local efficiency measures. The global efficiency did not show any hemispheric asymmetry, whereas the local connectivity showed rightward asymmetry for a range of intermediate density values for the constructed networks. Furthermore, the age of the participants showed significant direct correlations with the global efficiency of the left hemisphere, but only in the right hemisphere, with local connectivity. These results suggest that only local connectivity of EEG-based functional networks is associated with brain hemispheres.

Test-retest reliability of cognitive EEG

NASA Technical Reports Server (NTRS)

McEvoy, L. K.; Smith, M. E.; Gevins, A.

2000-01-01

OBJECTIVE: Task-related EEG is sensitive to changes in cognitive state produced by increased task difficulty and by transient impairment. If task-related EEG has high test-retest reliability, it could be used as part of a clinical test to assess changes in cognitive function. The aim of this study was to determine the reliability of the EEG recorded during the performance of a working memory (WM) task and a psychomotor vigilance task (PVT). METHODS: EEG was recorded while subjects rested quietly and while they performed the tasks. Within session (test-retest interval of approximately 1 h) and between session (test-retest interval of approximately 7 days) reliability was calculated for four EEG components: frontal midline theta at Fz, posterior theta at Pz, and slow and fast alpha at Pz. RESULTS: Task-related EEG was highly reliable within and between sessions (r0.9 for all components in WM task, and r0.8 for all components in the PVT). Resting EEG also showed high reliability, although the magnitude of the correlation was somewhat smaller than that of the task-related EEG (r0.7 for all 4 components). CONCLUSIONS: These results suggest that under appropriate conditions, task-related EEG has sufficient retest reliability for use in assessing clinical changes in cognitive status.

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.

Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.

PubMed

Lie, Octavian V; van Mierlo, Pieter

2017-01-01

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (<60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

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.

Amplitude-integrated EEG colored according to spectral edge frequency.

PubMed

Kobayashi, Katsuhiro; Mimaki, Nobuyoshi; Endoh, Fumika; Inoue, Takushi; Yoshinaga, Harumi; Ohtsuka, Yoko

2011-10-01

To improve the interpretability of figures containing an amplitude-integrated electroencephalogram (aEEG), we devised a color scale that allows us to incorporate spectral edge frequency (SEF) information into aEEG figures. Preliminary clinical assessment of this novel technique, which we call aEEG/SEF, was performed using neonatal and early infantile seizure data. We created aEEG, color density spectral array (DSA), and aEEG/SEF figures for focal seizures recorded in seven infants. Each seizure was paired with an interictal period from the same patient. After receiving instructions on how to interpret the figures, eight test reviewers examined each of the 72 figures displaying compressed data in aEEG, DSA, or aEEG/SEF form (12 seizures and 12 corresponding interictal periods) and attempted to identify each as a seizure or otherwise. They were not provided with any information regarding the original record. The median number of correctly identified seizures, out of a total of 12, was 7 (58.3%) for aEEG figures, 8 (66.7%) for DSA figures and 10 (83.3%) for aEEG/SEF figures; the differences among these are statistically significant (p=0.011). All reviewers concluded that aEEG/SEF figures were the easiest to interpret. The aEEG/SEF data presentation technique is a valid option in aEEG recordings of seizures. Copyright © 2011 Elsevier B.V. All rights reserved.

Non-linear Analysis of Scalp EEG by Using Bispectra: The Effect of the Reference Choice

PubMed Central

Chella, Federico; D'Andrea, Antea; Basti, Alessio; Pizzella, Vittorio; Marzetti, Laura

2017-01-01

Bispectral analysis is a signal processing technique that makes it possible to capture the non-linear and non-Gaussian properties of the EEG signals. It has found various applications in EEG research and clinical practice, including the assessment of anesthetic depth, the identification of epileptic seizures, and more recently, the evaluation of non-linear cross-frequency brain functional connectivity. However, the validity and reliability of the indices drawn from bispectral analysis of EEG signals are potentially biased by the use of a non-neutral EEG reference. The present study aims at investigating the effects of the reference choice on the analysis of the non-linear features of EEG signals through bicoherence, as well as on the estimation of cross-frequency EEG connectivity through two different non-linear measures, i.e., the cross-bicoherence and the antisymmetric cross-bicoherence. To this end, four commonly used reference schemes were considered: the vertex electrode (Cz), the digitally linked mastoids, the average reference, and the Reference Electrode Standardization Technique (REST). The reference effects were assessed both in simulations and in a real EEG experiment. The simulations allowed to investigated: (i) the effects of the electrode density on the performance of the above references in the estimation of bispectral measures; and (ii) the effects of the head model accuracy in the performance of the REST. For real data, the EEG signals recorded from 10 subjects during eyes open resting state were examined, and the distortions induced by the reference choice in the patterns of alpha-beta bicoherence, cross-bicoherence, and antisymmetric cross-bicoherence were assessed. The results showed significant differences in the findings depending on the chosen reference, with the REST providing superior performance than all the other references in approximating the ideal neutral reference. In conclusion, this study highlights the importance of considering the effects of the reference choice in the interpretation and comparison of the results of bispectral analysis of scalp EEG. PMID:28559790

Resting-state EEG power and coherence vary between migraine phases.

PubMed

Cao, Zehong; Lin, Chin-Teng; Chuang, Chun-Hsiang; Lai, Kuan-Lin; Yang, Albert C; Fuh, Jong-Ling; Wang, Shuu-Jiun

2016-12-01

Migraine is characterized by a series of phases (inter-ictal, pre-ictal, ictal, and post-ictal). It is of great interest whether resting-state electroencephalography (EEG) is differentiable between these phases. We compared resting-state EEG energy intensity and effective connectivity in different migraine phases using EEG power and coherence analyses in patients with migraine without aura as compared with healthy controls (HCs). EEG power and isolated effective coherence of delta (1-3.5 Hz), theta (4-7.5 Hz), alpha (8-12.5 Hz), and beta (13-30 Hz) bands were calculated in the frontal, central, temporal, parietal, and occipital regions. Fifty patients with episodic migraine (1-5 headache days/month) and 20 HCs completed the study. Patients were classified into inter-ictal, pre-ictal, ictal, and post-ictal phases (n = 22, 12, 8, 8, respectively), using 36-h criteria. Compared to HCs, inter-ictal and ictal patients, but not pre- or post-ictal patients, had lower EEG power and coherence, except for a higher effective connectivity in fronto-occipital network in inter-ictal patients (p < .05). Compared to data obtained from the inter-ictal group, EEG power and coherence were increased in the pre-ictal group, with the exception of a lower effective connectivity in fronto-occipital network (p < .05). Inter-ictal and ictal patients had decreased EEG power and coherence relative to HCs, which were "normalized" in the pre-ictal or post-ictal groups. Resting-state EEG power density and effective connectivity differ between migraine phases and provide an insight into the complex neurophysiology of migraine.

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.

Sleep homeostasis in the female rat during the estrous cycle.

PubMed

Schwierin, B; Borbély, A A; Tobler, I

1998-11-16

To investigate whether sleep homeostasis in the female rat is modulated by the estrous cycle, the vigilance states, EEG power spectra and cortical temperature (TCRT) were assessed on the basis of 4-day continuous recordings. A regulatory response was elicited by 6-h sleep deprivation (SD) during the proestrous (PRO) and the estrous (EST) day and compared to the baseline recordings. The vigilance states varied across the estrous cycle. In the PRO dark period the amount of sleep was reduced. The decrease in rapid-eye-movement (REM) sleep was already evident towards the end of the preceding light period, and an increased fragmentation of sleep was present throughout PRO. Compared to the other days of the estrous cycle, slow-wave activity (SWA; EEG power density 0.75-4.75 Hz) in nonREM (NREM) sleep was lower in PRO at the end of the light period and in the beginning of the dark period. High-frequency activity (HFA; EEG power density 10.25-25.0 Hz) was increased in the dark period of PRO. The SD performed during the first 6 h of the light period of PRO and EST enhanced SWA in NREM sleep and reduced sleep fragmentation during the subsequent 6 h. The extent and time course of the response to SD did not differ between the two phases of the estrous cycle. It is concluded that despite the marked baseline variations of the vigilance states and the EEG, homeostatic regulation is little affected by the estrous cycle. Copyright 1998 Elsevier Science B.V.

Predicting epileptic seizures from scalp EEG based on attractor state analysis.

PubMed

Chu, Hyunho; Chung, Chun Kee; Jeong, Woorim; Cho, Kwang-Hyun

2017-05-01

Epilepsy is the second most common disease of the brain. Epilepsy makes it difficult for patients to live a normal life because it is difficult to predict when seizures will occur. In this regard, if seizures could be predicted a reasonable period of time before their occurrence, epilepsy patients could take precautions against them and improve their safety and quality of life. In this paper, we investigate a novel seizure precursor based on attractor state analysis for seizure prediction. We analyze the transition process from normal to seizure attractor state and investigate a precursor phenomenon seen before reaching the seizure attractor state. From the result of an analysis, we define a quantified spectral measure in scalp EEG for seizure prediction. From scalp EEG recordings, the Fourier coefficients of six EEG frequency bands are extracted, and the defined spectral measure is computed based on the coefficients for each half-overlapped 20-second-long window. The computed spectral measure is applied to seizure prediction using a low-complexity methodology. Within scalp EEG, we identified an early-warning indicator before an epileptic seizure occurs. Getting closer to the bifurcation point that triggers the transition from normal to seizure state, the power spectral density of low frequency bands of the perturbation of an attractor in the EEG, showed a relative increase. A low-complexity seizure prediction algorithm using this feature was evaluated, using ∼583h of scalp EEG in which 143 seizures in 16 patients were recorded. With the test dataset, the proposed method showed high sensitivity (86.67%) with a false prediction rate of 0.367h -1 and average prediction time of 45.3min. A novel seizure prediction method using scalp EEG, based on attractor state analysis, shows potential for application with real epilepsy patients. This is the first study in which the seizure-precursor phenomenon of an epileptic seizure is investigated based on attractor-based analysis of the macroscopic dynamics of the brain. With the scalp EEG, we first propose use of a spectral feature identified for seizure prediction, in which the dynamics of an attractor are excluded, and only the perturbation dynamics from the attractor are considered. Copyright © 2017 Elsevier B.V. All rights reserved.

Wavelet-based study of valence-arousal model of emotions on EEG signals with LabVIEW.

PubMed

Guzel Aydin, Seda; Kaya, Turgay; Guler, Hasan

2016-06-01

This paper illustrates the wavelet-based feature extraction for emotion assessment using electroencephalogram (EEG) signal through graphical coding design. Two-dimensional (valence-arousal) emotion model was studied. Different emotions (happy, joy, melancholy, and disgust) were studied for assessment. These emotions were stimulated by video clips. EEG signals obtained from four subjects were decomposed into five frequency bands (gamma, beta, alpha, theta, and delta) using "db5" wavelet function. Relative features were calculated to obtain further information. Impact of the emotions according to valence value was observed to be optimal on power spectral density of gamma band. The main objective of this work is not only to investigate the influence of the emotions on different frequency bands but also to overcome the difficulties in the text-based program. This work offers an alternative approach for emotion evaluation through EEG processing. There are a number of methods for emotion recognition such as wavelet transform-based, Fourier transform-based, and Hilbert-Huang transform-based methods. However, the majority of these methods have been applied with the text-based programming languages. In this study, we proposed and implemented an experimental feature extraction with graphics-based language, which provides great convenience in bioelectrical signal processing.

The neural correlates of dreaming

PubMed Central

Siclari, F.; Baird, B.; Perogamvros, L.; Bernardi, G.; LaRocque, J. J.; Riedner, B.; Boly, M.; Postle, B. R.; Tononi, G.

2017-01-01

Consciousness never fades during wake. However, if awakened from sleep, sometimes we report dreams and sometimes no experiences. Traditionally, dreaming has been identified with REM sleep, characterized by a wake-like, globally ‘activated’, high-frequency EEG. However, dreaming also occurs in NREM sleep, characterized by prominent low-frequency activity. This challenges our understanding of the neural correlates of conscious experiences in sleep. Using high-density EEG, we contrasted the presence and absence of dreaming within NREM and REM sleep. In both NREM and REM sleep, reports of dream experience were associated with a local decrease in low-frequency activity in posterior cortical regions. High-frequency activity within these regions correlated with specific dream contents. Monitoring this posterior ‘hot zone’ predicted whether an individual reported dreaming or the absence of experiences during NREM sleep in real time, suggesting that it may constitute a core correlate of conscious experiences in sleep. PMID:28394322

The Multidimensional Aspects of Sleep Spindles and Their Relationship to Word-Pair Memory Consolidation.

PubMed

Lustenberger, Caroline; Wehrle, Flavia; Tüshaus, Laura; Achermann, Peter; Huber, Reto

2015-07-01

Several studies proposed a link between sleep spindles and sleep dependent memory consolidation in declarative learning tasks. In addition to these state-like aspects of sleep spindles, they have also trait-like characteristics, i.e., were related to general cognitive performance, an important distinction that has often been neglected in correlative studies. Furthermore, from the multitude of different sleep spindle measures, often just one specific aspect was analyzed. Thus, we aimed at taking multidimensional aspects of sleep spindles into account when exploring their relationship to word-pair memory consolidation. Each subject underwent 2 study nights with all-night high-density electroencephalographic (EEG) recordings. Sleep spindles were automatically detected in all EEG channels. Subjects were trained and tested on a word-pair learning task in the evening, and retested in the morning to assess sleep related memory consolidation (overnight retention). Trait-like aspects refer to the mean of both nights and state-like aspects were calculated as the difference between night 1 and night 2. Sleep laboratory. Twenty healthy male subjects (age: 23.3 ± 2.1 y). Overnight retention was negatively correlated with trait-like aspects of fast sleep spindle density and positively with slow spindle density on a global level. In contrast, state-like aspects were observed for integrated slow spindle activity, which was positively related to the differences in overnight retention in specific regions. Our results demonstrate the importance of a multidimensional approach when investigating the relationship between sleep spindles and memory consolidation and thereby provide a more complete picture explaining divergent findings in the literature. © 2015 Associated Professional Sleep Societies, LLC.

Effects of oral temazepam on slow waves during non-rapid eye movement sleep in healthy young adults: A high-density EEG investigation.

PubMed

Plante, D T; Goldstein, M R; Cook, J D; Smith, R; Riedner, B A; Rumble, M E; Jelenchick, L; Roth, A; Tononi, G; Benca, R M; Peterson, M J

2016-03-01

Slow waves are characteristic waveforms that occur during non-rapid eye movement (NREM) sleep that play an integral role in sleep quality and brain plasticity. Benzodiazepines are commonly used medications that alter slow waves, however, their effects may depend on the time of night and measure used to characterize slow waves. Prior investigations have utilized minimal scalp derivations to evaluate the effects of benzodiazepines on slow waves, and thus the topography of changes to slow waves induced by benzodiazepines has yet to be fully elucidated. This study used high-density electroencephalography (hdEEG) to evaluate the effects of oral temazepam on slow wave activity, incidence, and morphology during NREM sleep in 18 healthy adults relative to placebo. Temazepam was associated with significant decreases in slow wave activity and incidence, which were most prominent in the latter portions of the sleep period. However, temazepam was also associated with a decrease in the magnitude of high-amplitude slow waves and their slopes in the first NREM sleep episode, which was most prominent in frontal derivations. These findings suggest that benzodiazepines produce changes in slow waves throughout the night that vary depending on cortical topography and measures used to characterize slow waves. Further research that explores the relationships between benzodiazepine-induced changes to slow waves and the functional effects of these waveforms is indicated. Copyright © 2016 Elsevier B.V. All rights reserved.

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

Slower EEG alpha generation, synchronization and "flow"-possible biomarkers of cognitive impairment and neuropathology of minor stroke.

PubMed

Petrovic, Jelena; Milosevic, Vuk; Zivkovic, Miroslava; Stojanov, Dragan; Milojkovic, Olga; Kalauzi, Aleksandar; Saponjic, Jasna

2017-01-01

We investigated EEG rhythms, particularly alpha activity, and their relationship to post-stroke neuropathology and cognitive functions in the subacute and chronic stages of minor strokes. We included 10 patients with right middle cerebral artery (MCA) ischemic strokes and 11 healthy controls. All the assessments of stroke patients were done both in the subacute and chronic stages. Neurological impairment was measured using the National Institute of Health Stroke Scale (NIHSS), whereas cognitive functions were assessed using the Montreal Cognitive Assessment (MoCA) and MoCA memory index (MoCA-MIS). The EEG was recorded using a 19 channel EEG system with standard EEG electrode placement. In particular, we analyzed the EEGs derived from the four lateral frontal (F3, F7, F4, F8), and corresponding lateral posterior (P3, P4, T5, T6) electrodes. Quantitative EEG analysis included: the group FFT spectra, the weighted average of alpha frequency (αAVG), the group probability density distributions of all conventional EEG frequency band relative amplitudes (EEG microstructure), the inter- and intra-hemispheric coherences, and the topographic distribution of alpha carrier frequency phase potentials (PPs). Statistical analysis was done using a Kruskal-Wallis ANOVA with a post-hoc Mann-Whitney U two-tailed test, and Spearman's correlation. We demonstrated transient cognitive impairment alongside a slower alpha frequency ( α AVG) in the subacute right MCA stroke patients vs. the controls. This slower alpha frequency showed no amplitude change, but was highly synchronized intra-hemispherically, overlying the ipsi-lesional hemisphere, and inter-hemispherically, overlying the frontal cortex. In addition, the disturbances in EEG alpha activity in subacute stroke patients were expressed as a decrease in alpha PPs over the frontal cortex and an altered "alpha flow", indicating the sustained augmentation of inter-hemispheric interactions. Although the stroke induced slower alpha was a transient phenomenon, the increased alpha intra-hemispheric synchronization, overlying the ipsi-lesional hemisphere, the increased alpha F3-F4 inter-hemispheric synchronization, the delayed alpha waves, and the newly established inter-hemispheric "alpha flow" within the frontal cortex, remained as a permanent consequence of the minor stroke. This newly established frontal inter-hemispheric "alpha flow" represented a permanent consequence of the "hidden" stroke neuropathology, despite the fact that cognitive impairment has been returned to the control values. All the detected permanent changes at the EEG level with no cognitive impairment after a minor stroke could be a way for the brain to compensate for the lesion and restore the lost function. Our study indicates slower EEG alpha generation, synchronization and "flow" as potential biomarkers of cognitive impairment onset and/or compensatory post-stroke re-organizational processes.

Effects of high-dose ethanol intoxication and hangover on cognitive flexibility.

PubMed

Wolff, Nicole; Gussek, Philipp; Stock, Ann-Kathrin; Beste, Christian

2018-01-01

The effects of high-dose ethanol intoxication on cognitive flexibility processes are not well understood, and processes related to hangover after intoxication have remained even more elusive. Similarly, it is unknown in how far the complexity of cognitive flexibility processes is affected by intoxication and hangover effects. We performed a neurophysiological study applying high density electroencephalography (EEG) recording to analyze event-related potentials (ERPs) and perform source localization in a task switching paradigm which varied the complexity of task switching by means of memory demands. The results show that high-dose ethanol intoxication only affects task switching (i.e. cognitive flexibility processes) when memory processes are required to control task switching mechanisms, suggesting that even high doses of ethanol compromise cognitive processes when they are highly demanding. The EEG and source localization data show that these effects unfold by modulating response selection processes in the anterior cingulate cortex. Perceptual and attentional selection processes as well as working memory processes were only unspecifically modulated. In all subprocesses examined, there were no differences between the sober and hangover states, thus suggesting a fast recovery of cognitive flexibility after high-dose ethanol intoxication. We assume that the gamma-aminobutyric acid (GABAergic) system accounts for the observed effects, while they can hardly be explained by the dopaminergic system. © 2016 Society for the Study of Addiction.

Continuous electroencephalography predicts delayed cerebral ischemia after subarachnoid hemorrhage: A prospective study of diagnostic accuracy.

PubMed

Rosenthal, Eric S; Biswal, Siddharth; Zafar, Sahar F; O'Connor, Kathryn L; Bechek, Sophia; Shenoy, Apeksha V; Boyle, Emily J; Shafi, Mouhsin M; Gilmore, Emily J; Foreman, Brandon P; Gaspard, Nicolas; Leslie-Mazwi, Thabele M; Rosand, Jonathan; Hoch, Daniel B; Ayata, Cenk; Cash, Sydney S; Cole, Andrew J; Patel, Aman B; Westover, M Brandon

2018-04-16

Delayed cerebral ischemia (DCI) is a common, disabling complication of subarachnoid hemorrhage (SAH). Preventing DCI is a key focus of neurocritical care, but interventions carry risk and cannot be applied indiscriminately. Although retrospective studies have identified continuous electroencephalographic (cEEG) measures associated with DCI, no study has characterized the accuracy of cEEG with sufficient rigor to justify using it to triage patients to interventions or clinical trials. We therefore prospectively assessed the accuracy of cEEG for predicting DCI, following the Standards for Reporting Diagnostic Accuracy Studies. We prospectively performed cEEG in nontraumatic, high-grade SAH patients at a single institution. The index test consisted of clinical neurophysiologists prospectively reporting prespecified EEG alarms: (1) decreasing relative alpha variability, (2) decreasing alpha-delta ratio, (3) worsening focal slowing, or (4) late appearing epileptiform abnormalities. The diagnostic reference standard was DCI determined by blinded, adjudicated review. Primary outcome measures were sensitivity and specificity of cEEG for subsequent DCI, determined by multistate survival analysis, adjusted for baseline risk. One hundred three of 227 consecutive patients were eligible and underwent cEEG monitoring (7.7-day mean duration). EEG alarms occurred in 96.2% of patients with and 19.6% without subsequent DCI (1.9-day median latency, interquartile range = 0.9-4.1). Among alarm subtypes, late onset epileptiform abnormalities had the highest predictive value. Prespecified EEG findings predicted DCI among patients with low (91% sensitivity, 83% specificity) and high (95% sensitivity, 77% specificity) baseline risk. cEEG accurately predicts DCI following SAH and may help target therapies to patients at highest risk of secondary brain injury. Ann Neurol 2018. © 2018 American Neurological Association.

Complexity of Multi-Dimensional Spontaneous EEG Decreases during Propofol Induced General Anaesthesia

PubMed Central

Schartner, Michael; Seth, Anil; Noirhomme, Quentin; Boly, Melanie; Bruno, Marie-Aurelie; Laureys, Steven; Barrett, Adam

2015-01-01

Emerging neural theories of consciousness suggest a correlation between a specific type of neural dynamical complexity and the level of consciousness: When awake and aware, causal interactions between brain regions are both integrated (all regions are to a certain extent connected) and differentiated (there is inhomogeneity and variety in the interactions). In support of this, recent work by Casali et al (2013) has shown that Lempel-Ziv complexity correlates strongly with conscious level, when computed on the EEG response to transcranial magnetic stimulation. Here we investigated complexity of spontaneous high-density EEG data during propofol-induced general anaesthesia. We consider three distinct measures: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability in the constitution of the set of active channels; and (iii) the novel synchrony coalition entropy (SCE), which measures the variability in the constitution of the set of synchronous channels. After some simulations on Kuramoto oscillator models which demonstrate that these measures capture distinct ‘flavours’ of complexity, we show that there is a robustly measurable decrease in the complexity of spontaneous EEG during general anaesthesia. PMID:26252378

Significant improvement in one-dimensional cursor control using Laplacian electroencephalography over electroencephalography

NASA Astrophysics Data System (ADS)

Boudria, Yacine; Feltane, Amal; Besio, Walter

2014-06-01

Objective. Brain-computer interfaces (BCIs) based on electroencephalography (EEG) have been shown to accurately detect mental activities, but the acquisition of high levels of control require extensive user training. Furthermore, EEG has low signal-to-noise ratio and low spatial resolution. The objective of the present study was to compare the accuracy between two types of BCIs during the first recording session. EEG and tripolar concentric ring electrode (TCRE) EEG (tEEG) brain signals were recorded and used to control one-dimensional cursor movements. Approach. Eight human subjects were asked to imagine either ‘left’ or ‘right’ hand movement during one recording session to control the computer cursor using TCRE and disc electrodes. Main results. The obtained results show a significant improvement in accuracies using TCREs (44%-100%) compared to disc electrodes (30%-86%). Significance. This study developed the first tEEG-based BCI system for real-time one-dimensional cursor movements and showed high accuracies with little training.

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

Classification of EEG Signals Based on Pattern Recognition Approach.

PubMed

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90-7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11-89.63% and 91.60-81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.

Classification of EEG Signals Based on Pattern Recognition Approach

PubMed Central

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a “pattern recognition” approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90–7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11–89.63% and 91.60–81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy. PMID:29209190

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

The Utility of EEG in Attention Deficit Hyperactivity Disorder: A Replication Study.

PubMed

Swatzyna, Ronald J; Tarnow, Jay D; Roark, Alexandra; Mardick, Jacob

2017-07-01

The routine use of stimulants in pediatrics has increased dramatically over the past 3 decades and the long-term consequences have yet to be fully studied. Since 1978 there have been 7 articles identifying electroencephalogram (EEG) abnormalities, particularly epileptiform discharges in children with attention deficit hyperactivity disorder (ADHD). Many have studied the prevalence of these discharges in this population with varying results. An article published in 2011 suggests that EEG technology should be considered prior to prescribing stimulants to children diagnosed with ADHD due to a high prevalence of epileptiform discharges. The 2011 study found a higher prevalence (26%) of epileptiform discharges when using 23-hour and sleep-deprived EEGs in comparison with other methods of activation (hyperventilation or photostimulation) and conventional EEG. We sought to replicate the 2011 results using conventional EEG with the added qEEG technologies of automatic spike detection and low-resolution electromagnetic tomography analysis (LORETA) brain mapping. Our results showed 32% prevalence of epileptiform discharges, which suggests that an EEG should be considered prior to prescribing stimulant medications.

The Role of Skull Modeling in EEG Source Imaging for Patients with Refractory Temporal Lobe Epilepsy.

PubMed

Montes-Restrepo, Victoria; Carrette, Evelien; Strobbe, Gregor; Gadeyne, Stefanie; Vandenberghe, Stefaan; Boon, Paul; Vonck, Kristl; Mierlo, Pieter van

2016-07-01

We investigated the influence of different skull modeling approaches on EEG source imaging (ESI), using data of six patients with refractory temporal lobe epilepsy who later underwent successful epilepsy surgery. Four realistic head models with different skull compartments, based on finite difference methods, were constructed for each patient: (i) Three models had skulls with compact and spongy bone compartments as well as air-filled cavities, segmented from either computed tomography (CT), magnetic resonance imaging (MRI) or a CT-template and (ii) one model included a MRI-based skull with a single compact bone compartment. In all patients we performed ESI of single and averaged spikes marked in the clinical 27-channel EEG by the epileptologist. To analyze at which time point the dipole estimations were closer to the resected zone, ESI was performed at two time instants: the half-rising phase and peak of the spike. The estimated sources for each model were validated against the resected area, as indicated by the postoperative MRI. Our results showed that single spike analysis was highly influenced by the signal-to-noise ratio (SNR), yielding estimations with smaller distances to the resected volume at the peak of the spike. Although averaging reduced the SNR effects, it did not always result in dipole estimations lying closer to the resection. The proposed skull modeling approaches did not lead to significant differences in the localization of the irritative zone from clinical EEG data with low spatial sampling density. Furthermore, we showed that a simple skull model (MRI-based) resulted in similar accuracy in dipole estimation compared to more complex head models (based on CT- or CT-template). Therefore, all the considered head models can be used in the presurgical evaluation of patients with temporal lobe epilepsy to localize the irritative zone from low-density clinical EEG recordings.

Practice advisory: The utility of EEG theta/beta power ratio in ADHD diagnosis

PubMed Central

Gloss, David; Varma, Jay K.; Pringsheim, Tamara; Nuwer, Marc R.

2016-01-01

Objective: To evaluate the evidence for EEG theta/beta power ratio for diagnosing, or helping to diagnose, attention-deficit/hyperactivity disorder (ADHD). Methods: We identified relevant studies and classified them using American Academy of Neurology criteria. Results: Two Class I studies assessing the ability of EEG theta/beta power ratio and EEG frontal beta power to identify patients with ADHD correctly identified 166 of 185 participants. Both studies evaluated theta/beta power ratio and frontal beta power in suspected ADHD or in syndromes typically included in an ADHD differential diagnosis. A bivariate model combining the diagnostic studies shows that the combination of EEG frontal beta power and theta/beta power ratio has relatively high sensitivity and specificity but is insufficiently accurate. Conclusions: It is unknown whether a combination of standard clinical examination and EEG theta/beta power ratio increases diagnostic certainty of ADHD compared with clinical examination alone. Recommendations: Level B: Clinicians should inform patients with suspected ADHD and their families that the combination of EEG theta/beta power ratio and frontal beta power should not replace a standard clinical evaluation. There is a risk for significant harm to patients from ADHD misdiagnosis because of the unacceptably high false-positive diagnostic rate of EEG theta/beta power ratio and frontal beta power. Level R: Clinicians should inform patients with suspected ADHD and their families that the EEG theta/beta power ratio should not be used to confirm an ADHD diagnosis or to support further testing after a clinical evaluation, unless such diagnostic assessments occur in a research setting. PMID:27760867

Beyond the double banana: improved recognition of temporal lobe seizures in long-term EEG.

PubMed

Rosenzweig, Ivana; Fogarasi, András; Johnsen, Birger; Alving, Jørgen; Fabricius, Martin Ejler; Scherg, Michael; Neufeld, Miri Y; Pressler, Ronit; Kjaer, Troels W; van Emde Boas, Walter; Beniczky, Sándor

2014-02-01

To investigate whether extending the 10-20 array with 6 electrodes in the inferior temporal chain and constructing computed montages increases the diagnostic value of ictal EEG activity originating in the temporal lobe. In addition, the accuracy of computer-assisted spectral source analysis was investigated. Forty EEG samples were reviewed by 7 EEG experts in various montages (longitudinal and transversal bipolar, common average, source derivation, source montage, current source density, and reference-free montages) using 2 electrode arrays (10-20 and the extended one). Spectral source analysis used source montage to calculate density spectral array, defining the earliest oscillatory onset. From this, phase maps were calculated for localization. The reference standard was the decision of the multidisciplinary epilepsy surgery team on the seizure onset zone. Clinical performance was compared with the double banana (longitudinal bipolar montage, 10-20 array). Adding the inferior temporal electrode chain, computed montages (reference free, common average, and source derivation), and voltage maps significantly increased the sensitivity. Phase maps had the highest sensitivity and identified ictal activity at earlier time-point than visual inspection. There was no significant difference concerning specificity. The findings advocate for the use of these digital EEG technology-derived analysis methods in clinical practice.

On-going electroencephalographic rhythms related to cortical arousal in wild-type mice: the effect of aging.

PubMed

Del Percio, Claudio; Drinkenburg, Wilhelmus; Lopez, Susanna; Infarinato, Francesco; Bastlund, Jesper Frank; Laursen, Bettina; Pedersen, Jan T; Christensen, Ditte Zerlang; Forloni, Gianluigi; Frasca, Angelisa; Noè, Francesco M; Bentivoglio, Marina; Fabene, Paolo Francesco; Bertini, Giuseppe; Colavito, Valeria; Kelley, Jonathan; Dix, Sophie; Richardson, Jill C; Babiloni, Claudio

2017-01-01

Resting state electroencephalographic (EEG) rhythms reflect the fluctuation of cortical arousal and vigilance in a typical clinical setting, namely the EEG recording for few minutes with eyes closed (i.e., passive condition) and eyes open (i.e., active condition). Can this procedure be back-translated to C57 (wild type) mice for aging studies? On-going EEG rhythms were recorded from a frontoparietal bipolar channel in 85 (19 females) C57 mice. Male mice were subdivided into 3 groups: 25 young (4.5-6 months), 18 middle-aged (12-15 months), and 23 old (20-24 months) mice to test the effect of aging. EEG power density was compared between short periods (about 5 minutes) of awake quiet behavior (passive) and dynamic exploration of the cage (active). Compared with the passive condition, the active condition induced decreased EEG power at 1-2 Hz and increased EEG power at 6-10 Hz in the group of 85 mice. Concerning the aging effects, the passive condition showed higher EEG power at 1-2 Hz in the old group than that in the others. Furthermore, the active condition exhibited a maximum EEG power at 6-8 Hz in the former group and 8-10 Hz in the latter. In the present conditions, delta and theta EEG rhythms reflected changes in cortical arousal and vigilance in freely behaving C57 mice across aging. These changes resemble the so-called slowing of resting state EEG rhythms observed in humans across physiological and pathological aging. The present EEG procedures may be used to enhance preclinical phases of drug discovery in mice for understanding the neurophysiological effects of new compounds against brain aging. Copyright © 2016 Elsevier Inc. All rights reserved.

Effect of mental fatigue on the central nervous system: an electroencephalography study

PubMed Central

2012-01-01

Background Fatigue can be classified as mental and physical depending on its cause, and each type of fatigue has a multi-factorial nature. We examined the effect of mental fatigue on the central nervous system using electroencephalography (EEG) in eighteen healthy male volunteers. Methods After enrollment, subjects were randomly assigned to two groups in a single-blinded, crossover fashion to perform two types of mental fatigue-inducing experiments. Each experiment consisted of four 30-min fatigue-inducing 0- or 2-back test sessions and two evaluation sessions performed just before and after the fatigue-inducing sessions. During the evaluation session, the participants were assessed using EEG. Eleven electrodes were attached to the head skin, from positions F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, O1, and O2. Results In the 2-back test, the beta power density on the Pz electrode and the alpha power densities on the P3 and O2 electrodes were decreased, and the theta power density on the Cz electrode was increased after the fatigue-inducing mental task sessions. In the 0-back test, no electrodes were altered after the fatigue-inducing sessions. Conclusions Different types of mental fatigue produced different kinds of alterations of the spontaneous EEG variables. Our findings provide new perspectives on the neural mechanisms underlying mental fatigue. PMID:22954020

Sleep EEG Changes during Adolescence: An Index of a Fundamental Brain Reorganization

ERIC Educational Resources Information Center

Feinberg, Irwin; Campbell, Ian G.

2010-01-01

Delta (1-4 Hz) EEG power in non-rapid eye movement (NREM) sleep declines massively during adolescence. This observation stimulated the hypothesis that during adolescence the human brain undergoes an extensive reorganization driven by synaptic elimination. The parallel declines in synaptic density, delta wave amplitude and cortical metabolic rate…

Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis

PubMed Central

Toppi, Jlenia; Astolfi, Laura; Risetti, Monica; Anzolin, Alessandra; Kober, Silvia E.; Wood, Guilherme; Mattia, Donatella

2018-01-01

Several non-invasive imaging methods have contributed to shed light on the brain mechanisms underlying working memory (WM). The aim of the present study was to depict the topology of the relevant EEG-derived brain networks associated to distinct operations of WM function elicited by the Sternberg Item Recognition Task (SIRT) such as encoding, storage, and retrieval in healthy, middle age (46 ± 5 years) adults. High density EEG recordings were performed in 17 participants whilst attending a visual SIRT. Neural correlates of WM were assessed by means of a combination of EEG signal processing methods (i.e., time-varying connectivity estimation and graph theory), in order to extract synthetic descriptors of the complex networks underlying the encoding, storage, and retrieval phases of WM construct. The group analysis revealed that the encoding phase exhibited a significantly higher small-world topology of EEG networks with respect to storage and retrieval in all EEG frequency oscillations, thus indicating that during the encoding of items the global network organization could “optimally” promote the information flow between WM sub-networks. We also found that the magnitude of such configuration could predict subject behavioral performance when memory load increases as indicated by the negative correlation between Reaction Time and the local efficiency values estimated during the encoding in the alpha band in both 4 and 6 digits conditions. At the local scale, the values of the degree index which measures the degree of in- and out- information flow between scalp areas were found to specifically distinguish the hubs within the relevant sub-networks associated to each of the three different WM phases, according to the different role of the sub-network of regions in the different WM phases. Our findings indicate that the use of EEG-derived connectivity measures and their related topological indices might offer a reliable and yet affordable approach to monitor WM components and thus theoretically support the clinical assessment of cognitive functions in presence of WM decline/impairment, as it occurs after stroke. PMID:29379425

Automated MRI Segmentation for Individualized Modeling of Current Flow in the Human Head

PubMed Central

Huang, Yu; Dmochowski, Jacek P.; Su, Yuzhuo; Datta, Abhishek; Rorden, Christopher; Parra, Lucas C.

2013-01-01

Objective High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography (HD-EEG) require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images (MRI) requires labor-intensive manual segmentation, even when leveraging available automated segmentation tools. Also, accurate placement of many high-density electrodes on individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process. Approach A fully automated segmentation technique based on Statical Parametric Mapping 8 (SPM8), including an improved tissue probability map (TPM) and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on 4 healthy subjects and 7 stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets. Main results The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view (FOV) extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly. Significance Fully automated individualized modeling may now be feasible for large-sample EEG research studies and tDCS clinical trials. PMID:24099977

Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features.

PubMed

Han, Chang-Hee; Lim, Jeong-Hwan; Lee, Jun-Hak; Kim, Kangsan; Im, Chang-Hwan

2016-01-01

It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG) features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training.

Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features

PubMed Central

Lim, Jeong-Hwan; Lee, Jun-Hak; Kim, Kangsan

2016-01-01

It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG) features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training. PMID:27631005

Increased Intra-Participant Variability in Children with Autistic Spectrum Disorders: Evidence from Single-Trial Analysis of Evoked EEG

PubMed Central

Milne, Elizabeth

2011-01-01

Intra-participant variability in clinical conditions such as autistic spectrum disorder (ASD) is an important indicator of pathophysiological processing. The data reported here illustrate that trial-by-trial variability can be reliably measured from EEG, and that intra-participant EEG variability is significantly greater in those with ASD than in neuro-typical matched controls. EEG recorded at the scalp is a linear mixture of activity arising from muscle artifacts and numerous concurrent brain processes. To minimize these additional sources of variability, EEG data were subjected to two different methods of spatial filtering. (i) The data were decomposed using infomax independent component analysis, a method of blind source separation which un-mixes the EEG signal into components with maximally independent time-courses, and (ii) a surface Laplacian transform was performed (current source density interpolation) in order to reduce the effects of volume conduction. Data are presented from 13 high functioning adolescents with ASD without co-morbid ADHD, and 12 neuro-typical age-, IQ-, and gender-matched controls. Comparison of variability between the ASD and neuro-typical groups indicated that intra-participant variability of P1 latency and P1 amplitude was greater in the participants with ASD, and inter-trial α-band phase coherence was lower in the participants with ASD. These data support the suggestion that individuals with ASD are less able to synchronize the activity of stimulus-related cell assemblies than neuro-typical individuals, and provide empirical evidence in support of theories of increased neural noise in ASD. PMID:21716921

Association of EEG alpha variants and alpha power with alcohol dependence in Mexican American young adults.

PubMed

Ehlers, Cindy L; Phillips, Evelyn

2007-02-01

Several studies support an association between electroencephalogram (EEG) voltage and alcohol dependence. However, the distribution of EEG variants also appears to differ depending on an individual's ethnic heritage, suggesting significant genetic stratification of this EEG phenotype. The present study's aims were to investigate the incidence of EEG alpha variants and spectral power in the alpha frequency range in Mexican American young adults based on gender, and personal and family history of alcohol dependence. Clinical ratings (high-, medium-, and low alpha voltage variants) and spectral characteristics of the EEG in the alpha frequency range (7.5-12 Hz) were investigated in young adult (age 18-25 years) Mexican American men (n=98) and women (n=138) who were recruited from the community. Nineteen percent (n=45) of the participants had a low-voltage alpha EEG variant, 18% had a high-voltage variant, and 63% had a medium-voltage variant. There were no significant differences in the distribution of the EEG variants based on family history of alcohol dependence. There was a significant relationship between gender and the three alpha variants (chi2=9.7; df=2; P<.008), and there were no male participants with alcohol dependence with high alpha variants (chi2=5.8; df=2; P<.056). Alcohol dependence, but not a family history of alcohol dependence, was associated with lower spectral power in the alpha frequency range in the right (F=4.4; df=1,96; P<.04) and left (F=5.3; df=1.96; P<.02) occipital areas in the men but not in the women. In conclusion, in this select population of Mexican American young adults, male gender and alcohol dependence are associated with an absence of high-voltage alpha variants and lower alpha power in the EEG. These data suggest that EEG low voltage, a highly heritable trait, may represent an important endophenotype in male Mexican Americans that may aid in linking brain function with genetic factors underlying alcohol dependence in this ethnic group.

Chronic high-caloric diet modifies sleep homeostasis in mice.

PubMed

Panagiotou, Maria; Meijer, Johanna H; Deboer, Tom

2018-05-08

Obesity prevalence and sleep habit changes are commonplace nowadays, due to modern lifestyle. A bidirectional relationship likely exists between sleep quality and metabolic disruptions, that could impact quality of life. In our study, we investigated the effects of a chronic high-caloric diet on sleep architecture and sleep regulation in mice. We studied the effect of three months high-caloric diet (HCD, 45% fat) on sleep and the sleep electroencephalogram (EEG) in C57BL/6J mice during 24-h baseline (BL) recordings, and after 6-h sleep deprivation (SD). We examined the effect of HCD on sleep homeostasis, by performing parameter estimation analysis and simulations of the sleep homeostatic Process S, a measure of sleep pressure, which is reflected in the non-rapid-eye-movement (NREM) sleep slow-wave-activity (SWA, EEG power density between 0.5-4.0 Hz). Compared to controls (n=11, 30.7±0.8g), mice fed with HCD (n=9, 47.6±0.8g) showed an increased likelihood of consecutive NREM-REM sleep cycles, increased REM sleep and decreased NREM sleep EEG SWA. After SD these effects were more pronounced. The simulation resulted in a close fit between the time course of SWA and Process S in both groups. HCD fed mice had a slower time constant (Ti = 15.98 h) for the increase in homeostatic sleep pressure compared to controls (5.95 h) indicating a reduced effect of waking on the increase in sleep pressure. Our results suggest that chronic HCD consumption impacts sleep regulation. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

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

Assessing the depth of hypnosis of xenon anaesthesia with the EEG.

PubMed

Stuttmann, Ralph; Schultz, Arthur; Kneif, Thomas; Krauss, Terence; Schultz, Barbara

2010-04-01

Xenon was approved as an inhaled anaesthetic in Germany in 2005 and in other countries of the European Union in 2007. Owing to its low blood/gas partition coefficient, xenons effects on the central nervous system show a fast onset and offset and, even after long xenon anaesthetics, the wake-up times are very short. The aim of this study was to examine which electroencephalogram (EEG) stages are reached during xenon application and whether these stages can be identified by an automatic EEG classification. Therefore, EEG recordings were performed during xenon anaesthetics (EEG monitor: Narcotrend®). A total of 300 EEG epochs were assessed visually with regard to the EEG stages. These epochs were also classified automatically by the EEG monitor Narcotrend® using multivariate algorithms. There was a high correlation between visual and automatic classification (Spearman's rank correlation coefficient r=0.957, prediction probability Pk=0.949). Furthermore, it was observed that very deep stages of hypnosis were reached which are characterised by EEG activity in the low frequency range (delta waves). The burst suppression pattern was not seen. In deep hypnosis, in contrast to the xenon EEG, the propofol EEG was characterised by a marked superimposed higher frequency activity. To ensure an optimised dosage for the single patient, anaesthetic machines for xenon should be combined with EEG monitoring. To date, only a few anaesthetic machines for xenon are available. Because of the high price of xenon, new and further developments of machines focus on optimizing xenon consumption.

Suppression of competing speech through entrainment of cortical oscillations

PubMed Central

D'Zmura, Michael; Srinivasan, Ramesh

2013-01-01

People are highly skilled at attending to one speaker in the presence of competitors, but the neural mechanisms supporting this remain unclear. Recent studies have argued that the auditory system enhances the gain of a speech stream relative to competitors by entraining (or “phase-locking”) to the rhythmic structure in its acoustic envelope, thus ensuring that syllables arrive during periods of high neuronal excitability. We hypothesized that such a mechanism could also suppress a competing speech stream by ensuring that syllables arrive during periods of low neuronal excitability. To test this, we analyzed high-density EEG recorded from human adults while they attended to one of two competing, naturalistic speech streams. By calculating the cross-correlation between the EEG channels and the speech envelopes, we found evidence of entrainment to the attended speech's acoustic envelope as well as weaker yet significant entrainment to the unattended speech's envelope. An independent component analysis (ICA) decomposition of the data revealed sources in the posterior temporal cortices that displayed robust correlations to both the attended and unattended envelopes. Critically, in these components the signs of the correlations when attended were opposite those when unattended, consistent with the hypothesized entrainment-based suppressive mechanism. PMID:23515789

Discrete Scale Invariance of Human Large EEG Voltage Deflections is More Prominent in Waking than Sleep Stage 2.

PubMed

Zorick, Todd; Mandelkern, Mark A

2015-01-01

Electroencephalography (EEG) is typically viewed through the lens of spectral analysis. Recently, multiple lines of evidence have demonstrated that the underlying neuronal dynamics are characterized by scale-free avalanches. These results suggest that techniques from statistical physics may be used to analyze EEG signals. We utilized a publicly available database of fourteen subjects with waking and sleep stage 2 EEG tracings per subject, and observe that power-law dynamics of critical-state neuronal avalanches are not sufficient to fully describe essential features of EEG signals. We hypothesized that this could reflect the phenomenon of discrete scale invariance (DSI) in EEG large voltage deflections (LVDs) as being more prominent in waking consciousness. We isolated LVDs, and analyzed logarithmically transformed LVD size probability density functions (PDF) to assess for DSI. We find evidence of increased DSI in waking, as opposed to sleep stage 2 consciousness. We also show that the signatures of DSI are specific for EEG LVDs, and not a general feature of fractal simulations with similar statistical properties to EEG. Removing only LVDs from waking EEG produces a reduction in power in the alpha and beta frequency bands. These findings may represent a new insight into the understanding of the cortical dynamics underlying consciousness.

A Comparative Study of Standardized Infinity Reference and Average Reference for EEG of Three Typical Brain States

PubMed Central

Zheng, Gaoxing; Qi, Xiaoying; Li, Yuzhu; Zhang, Wei; Yu, Yuguo

2018-01-01

The choice of different reference electrodes plays an important role in deciphering the functional meaning of electroencephalography (EEG) signals. In recent years, the infinity zero reference using the reference electrode standard technique (REST) has been increasingly applied, while the average reference (AR) was generally advocated as the best available reference option in previous classical EEG studies. Here, we designed EEG experiments and performed a direct comparison between the influences of REST and AR on EEG-revealed brain activity features for three typical brain behavior states (eyes-closed, eyes-open and music-listening). The analysis results revealed the following observations: (1) there is no significant difference in the alpha-wave-blocking effect during the eyes-open state compared with the eyes-closed state for both REST and AR references; (2) there was clear frontal EEG asymmetry during the resting state, and the degree of lateralization under REST was higher than that under AR; (3) the global brain functional connectivity density (FCD) and local FCD have higher values for REST than for AR under different behavior states; and (4) the value of the small-world network characteristic in the eyes-closed state is significantly (in full, alpha, beta and gamma frequency bands) higher than that in the eyes-open state, and the small-world effect under the REST reference is higher than that under AR. In addition, the music-listening state has a higher small-world network effect than the eyes-closed state. The above results suggest that typical EEG features might be more clearly presented by applying the REST reference than by applying AR when using a 64-channel recording. PMID:29593490

Mismatch Negativity Responses in Children with a Diagnosis of Childhood Apraxia of Speech (CAS)

ERIC Educational Resources Information Center

Froud, Karen; Khamis-Dakwar, Reem

2012-01-01

Purpose: To evaluate whether a hypothesis suggesting that apraxia of speech results from phonological overspecification could be relevant for childhood apraxia of speech (CAS). Method: High-density EEG was recorded from 5 children with CAS and 5 matched controls, ages 5-8 years, with and without CAS, as they listened to randomized sequences of CV…

Reduction of the Dimensionality of the EEG Channels during Scoliosis Correction Surgeries Using a Wavelet Decomposition Technique

PubMed Central

Al-Kadi, Mahmoud I.; Reaz, Mamun Bin Ibne; Ali, Mohd Alauddin Mohd; Liu, Chian Yong

2014-01-01

This paper presents a comparison between the electroencephalogram (EEG) channels during scoliosis correction surgeries. Surgeons use many hand tools and electronic devices that directly affect the EEG channels. These noises do not affect the EEG channels uniformly. This research provides a complete system to find the least affected channel by the noise. The presented system consists of five stages: filtering, wavelet decomposing (Level 4), processing the signal bands using four different criteria (mean, energy, entropy and standard deviation), finding the useful channel according to the criteria's value and, finally, generating a combinational signal from Channels 1 and 2. Experimentally, two channels of EEG data were recorded from six patients who underwent scoliosis correction surgeries in the Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) (the Medical center of National University of Malaysia). The combinational signal was tested by power spectral density, cross-correlation function and wavelet coherence. The experimental results show that the system-outputted EEG signals are neatly switched without any substantial changes in the consistency of EEG components. This paper provides an efficient procedure for analyzing EEG signals in order to avoid averaging the channels that lead to redistribution of the noise on both channels, reducing the dimensionality of the EEG features and preparing the best EEG stream for the classification and monitoring stage. PMID:25051031

Automatic removal of eye-movement and blink artifacts from EEG signals.

PubMed

Gao, Jun Feng; Yang, Yong; Lin, Pan; Wang, Pei; Zheng, Chong Xun

2010-03-01

Frequent occurrence of electrooculography (EOG) artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In this paper, a robust method is presented to automatically eliminate eye-movement and eye-blink artifacts from EEG signals. Independent Component Analysis (ICA) is used to decompose EEG signals into independent components. Moreover, the features of topographies and power spectral densities of those components are extracted to identify eye-movement artifact components, and a support vector machine (SVM) classifier is adopted because it has higher performance than several other classifiers. The classification results show that feature-extraction methods are unsuitable for identifying eye-blink artifact components, and then a novel peak detection algorithm of independent component (PDAIC) is proposed to identify eye-blink artifact components. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove EOG artifacts effectively from EEG signals with little distortion of the underlying brain signals.

P300 speller BCI with a mobile EEG system: comparison to a traditional amplifier

NASA Astrophysics Data System (ADS)

De Vos, Maarten; Kroesen, Markus; Emkes, Reiner; Debener, Stefan

2014-06-01

Objective. In a previous study, we presented a low-cost, small and wireless EEG system enabling the recording of single-trial P300 amplitudes in a truly mobile, outdoor walking condition (Debener et al (2012 Psychophysiology 49 1449-53)). Small and wireless mobile EEG systems have substantial practical advantages as they allow for brain activity recordings in natural environments, but these systems may compromise the EEG signal quality. In this study, we aim to evaluate the EEG signal quality that can be obtained with the mobile system. Approach. We compared our mobile 14-channel EEG system with a state-of-the-art wired laboratory EEG system in a popular brain-computer interface (BCI) application. N = 13 individuals repeatedly performed a 6 × 6 matrix P300 spelling task. Between conditions, only the amplifier was changed, while electrode placement and electrode preparation, recording conditions, experimental stimulation and signal processing were identical. Main results. Analysis of training and testing accuracies and information transfer rate (ITR) revealed that the wireless mobile EEG amplifier performed as good as the wired laboratory EEG system. A very high correlation for testing ITR between both amplifiers was evident (r = 0.92). Moreover the P300 topographies and amplitudes were very similar for both devices, as reflected by high degrees of association (r > = 0.77). Significance. We conclude that efficient P300 spelling with a small, lightweight and quick to set up mobile EEG amplifier is possible. This technology facilitates the transfer of BCI applications from the laboratory to natural daily life environments, one of the key challenges in current BCI research.

EEG Patterns Related to Cognitive Tasks of Varying Complexity.

ERIC Educational Resources Information Center

Dunn, Denise A.; And Others

A study was conducted that attempted to show changes in electroencephalographic (EEG) patterns (identified using topographic EEG mapping) when children were required to perform the relatively simple task of button pressing during an eyes-open baseline session of low cognitive demand and a complex reaction time (RT) task of high cognitive demand.…

[Physiological and hygienic assessment of the impact of mobile phones with various radiation intensity on the functional state of brain of children and adolescents according to electroencephalographic data].

PubMed

Vyatleva, O A; Teksheva, L M; Kurgansky, A M

To test the effect of mobile phones (MP) of various radiation intensities on the functional state of the brain in children and adolescents, a sham-controlled EEG-study was conducted in a group of thirteen 6-13 years old children, including eight 6-10 years old children. The study showed that a 3-minute exposure to the MP causes the significant decline in alpha-band absolute power, which depends on the radiation intensity and the user’s age. Different from sham, an EEG-effect of MP with the energy flux density (EFD) about 100 mW/cm2 was registered both in total, and in a younger (6-10 yr) group. Its bilateral character, more prominent in the hemisphere that is ipsilateral to MP, indicates that this intensity of the radiation influences not only the superficial cortical areas of the ipsilateral hemisphere, but also the deep structures of the brain. MP with the EFD less than 1 mW/cm2 differed from sham by EEG-effect only in the group of children who are 6-10 years old. Its local, ipsilateral character indicates to the superficial influence of such intensity of the radiation on the cortex of the ipsilateral hemisphere. The results show that for the regulation of MP-radiation it’s necessary to consider age features of the brain’s response. The high significance of the EFD, as an index in the assessment of the impact of MP on the EEG of children, is shown. Since almost all schoolchildren are the users of mobile phones, the situation with the valuation of MP-effects on children of various ages, requires hygienic solution.

EEG Brain Activity in Dynamic Health Qigong Training: Same Effects for Mental Practice and Physical Training?

PubMed

Henz, Diana; Schöllhorn, Wolfgang I

2017-01-01

In recent years, there has been significant uptake of meditation and related relaxation techniques, as a means of alleviating stress and fostering an attentive mind. Several electroencephalogram (EEG) studies have reported changes in spectral band frequencies during Qigong meditation indicating a relaxed state. Much less is reported on effects of brain activation patterns induced by Qigong techniques involving bodily movement. In this study, we tested whether (1) physical Qigong training alters EEG theta and alpha activation, and (2) mental practice induces the same effect as a physical Qigong training. Subjects performed the dynamic Health Qigong technique Wu Qin Xi (five animals) physically and by mental practice in a within-subjects design. Experimental conditions were randomized. Two 2-min (eyes-open, eyes-closed) EEG sequences under resting conditions were recorded before and immediately after each 15-min exercise. Analyses of variance were performed for spectral power density data. Increased alpha power was found in posterior regions in mental practice and physical training for eyes-open and eyes-closed conditions. Theta power was increased after mental practice in central areas in eyes-open conditions, decreased in fronto-central areas in eyes-closed conditions. Results suggest that mental, as well as physical Qigong training, increases alpha activity and therefore induces a relaxed state of mind. The observed differences in theta activity indicate different attentional processes in physical and mental Qigong training. No difference in theta activity was obtained in physical and mental Qigong training for eyes-open and eyes-closed resting state. In contrast, mental practice of Qigong entails a high degree of internalized attention that correlates with theta activity, and that is dependent on eyes-open and eyes-closed resting state.

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.

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.

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.

Brain signatures of moral sensitivity in adolescents with early social deprivation.

PubMed

Escobar, María Josefina; Huepe, David; Decety, Jean; Sedeño, Lucas; Messow, Marie Kristin; Baez, Sandra; Rivera-Rei, Álvaro; Canales-Johnson, Andrés; Morales, Juan Pablo; Gómez, David Maximiliano; Schröeder, Johannes; Manes, Facundo; López, Vladimir; Ibánez, Agustín

2014-06-19

The present study examined neural responses associated with moral sensitivity in adolescents with a background of early social deprivation. Using high-density electroencephalography (hdEEG), brain activity was measured during an intentional inference task, which assesses rapid moral decision-making regarding intentional or unintentional harm to people and objects. We compared the responses to this task in a socially deprived group (DG) with that of a control group (CG). The event-related potentials (ERPs) results showed atypical early and late frontal cortical markers associated with attribution of intentionality during moral decision-making in DG (especially regarding intentional harm to people). The source space of the hdEEG showed reduced activity for DG compared with CG in the right prefrontal cortex, bilaterally in the ventromedial prefrontal cortex (vmPFC), and right insula. Moreover, the reduced response in vmPFC for DG was predicted by higher rates of externalizing problems. These findings demonstrate the importance of the social environment in early moral development, supporting a prefrontal maturation model of social deprivation.

How do children fall asleep? A high-density EEG study of slow waves in the transition from wake to sleep.

PubMed

Spiess, Mathilde; Bernardi, Giulio; Kurth, Salome; Ringli, Maya; Wehrle, Flavia M; Jenni, Oskar G; Huber, Reto; Siclari, Francesca

2018-05-17

Slow waves, the hallmarks of non-rapid eye-movement (NREM) sleep, are thought to reflect maturational changes that occur in the cerebral cortex throughout childhood and adolescence. Recent work in adults has revealed evidence for two distinct synchronization processes involved in the generation of slow waves, which sequentially come into play in the transition to sleep. In order to understand how these two processes are affected by developmental changes, we compared slow waves between children and young adults in the falling asleep period. The sleep onset period (starting 30s before end of alpha activity and ending at the first slow wave sequence) was extracted from 72 sleep onset high-density EEG recordings (128 electrodes) of 49 healthy subjects (age 8-25). Using an automatic slow wave detection algorithm, the number, amplitude and slope of slow waves were analyzed and compared between children (age 8-11) and young adults (age 20-25). Slow wave number and amplitude increased linearly in the falling asleep period in children, while in young adults, isolated high-amplitude slow waves (type I) dominated initially and numerous smaller slow waves (type II) with progressively increasing amplitude occurred later. Compared to young adults, children displayed faster increases in slow wave amplitude and number across the falling asleep period in central and posterior brain regions, respectively, and also showed larger slow waves during wakefulness immediately prior to sleep. Children do not display the two temporally dissociated slow wave synchronization processes in the falling asleep period observed in adults, suggesting that maturational factors underlie the temporal segregation of these two processes. Our findings provide novel perspectives for studying how sleep-related behaviors and dreaming differ between children and adults. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

Rapid eye movements during sleep in mice: High trait-like stability qualifies rapid eye movement density for characterization of phenotypic variation in sleep patterns of rodents

PubMed Central

2011-01-01

Background In humans, rapid eye movements (REM) density during REM sleep plays a prominent role in psychiatric diseases. Especially in depression, an increased REM density is a vulnerability marker for depression. In clinical practice and research measurement of REM density is highly standardized. In basic animal research, almost no tools are available to obtain and systematically evaluate eye movement data, although, this would create increased comparability between human and animal sleep studies. Methods We obtained standardized electroencephalographic (EEG), electromyographic (EMG) and electrooculographic (EOG) signals from freely behaving mice. EOG electrodes were bilaterally and chronically implanted with placement of the electrodes directly between the musculus rectus superior and musculus rectus lateralis. After recovery, EEG, EMG and EOG signals were obtained for four days. Subsequent to the implantation process, we developed and validated an Eye Movement scoring in Mice Algorithm (EMMA) to detect REM as singularities of the EOG signal, based on wavelet methodology. Results The distribution of wakefulness, non-REM (NREM) sleep and rapid eye movement (REM) sleep was typical of nocturnal rodents with small amounts of wakefulness and large amounts of NREM sleep during the light period and reversed proportions during the dark period. REM sleep was distributed correspondingly. REM density was significantly higher during REM sleep than NREM sleep. REM bursts were detected more often at the end of the dark period than the beginning of the light period. During REM sleep REM density showed an ultradian course, and during NREM sleep REM density peaked at the beginning of the dark period. Concerning individual eye movements, REM duration was longer and amplitude was lower during REM sleep than NREM sleep. The majority of single REM and REM bursts were associated with micro-arousals during NREM sleep, but not during REM sleep. Conclusions Sleep-stage specific distributions of REM in mice correspond to human REM density during sleep. REM density, now also assessable in animal models through our approach, is increased in humans after acute stress, during PTSD and in depression. This relationship can now be exploited to match animal models more closely to clinical situations, especially in animal models of depression. PMID:22047102

Pilot study of EEG in neonates born to mothers with gestational diabetes mellitus.

PubMed

Léveillé-, Pauline; Hamel, Mathieu; Ardilouze, Jean-Luc; Pasquier, Jean-Charles; Deacon, Charles; Whittingstall, Kevin; Plourde, Mélanie

2018-05-01

The goal was to evaluate whether there was neurodevelopmental deficits in newborns born to mothers with gestational diabetes mellitus (GDM) compared to control newborns born to healthy mothers. Forty-six pregnant women (21 controls and 25 GDM) were recruited. Electroencephalogram (EEG) was recorded in the newborns within 48 h after birth. The EEG signal was quantitatively analyzed using power spectral density (PSD); coherence between hemispheres was calculated in paired channels of frontal, temporal, central and occipital regions. The left centro-occipital PSD in control newborns was 12% higher than in GDM newborns (p = 0.036) but was not significant after adjustment for gestational age. While coherence was higher in the frontal regions compared to the occipital regions (p < 0.001), there was no difference between the groups for the fronto-temporal, frontal-central, centro-occipital and tempo-occipital regions. Our results support that EEG differences between groups were mainly modified by gestational age and less by GDM status of the mothers. However, there is a need to confirm this result with a higher number of mother-newborns. Quantitative EEG in GDM newborns within 48 h after birth is feasible. This study emphasizes the importance of controlling blood glucose during GDM to protect infant brain development. Copyright © 2018 ISDN. Published by Elsevier Ltd. 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.

Simultaneous trimodal PET-MR-EEG imaging: Do EEG caps generate artefacts in PET images?

PubMed

Rajkumar, Ravichandran; Rota Kops, Elena; Mauler, Jörg; Tellmann, Lutz; Lerche, Christoph; Herzog, Hans; Shah, N Jon; Neuner, Irene

2017-01-01

Trimodal simultaneous acquisition of positron emission tomography (PET), magnetic resonance imaging (MRI), and electroencephalography (EEG) has become feasible due to the development of hybrid PET-MR scanners. To capture the temporal dynamics of neuronal activation on a millisecond-by-millisecond basis, an EEG system is appended to the quantitative high resolution PET-MR imaging modality already established in our institute. One of the major difficulties associated with the development of simultaneous trimodal acquisition is that the components traditionally used in each modality can cause interferences in its counterpart. The mutual interferences of MRI components and PET components on PET and MR images, and the influence of EEG electrodes on functional MRI images have been studied and reported on. Building on this, this study aims to investigate the influence of the EEG cap on the quality and quantification of PET images acquired during simultaneous PET-MR measurements. A preliminary transmission scan study on the ECAT HR+ scanner, using an Iida phantom, showed visible attenuation effect due to the EEG cap. The BrainPET-MR emission images of the Iida phantom with [18F]Fluordeoxyglucose, as well as of human subjects with the EEG cap, did not show significant effects of the EEG cap, even though the applied attenuation correction did not take into account the attenuation of the EEG cap itself.

Topography of Slow Sigma Power during Sleep is Associated with Processing Speed in Preschool Children.

PubMed

Doucette, Margaret R; Kurth, Salome; Chevalier, Nicolas; Munakata, Yuko; LeBourgeois, Monique K

2015-11-04

Cognitive development is influenced by maturational changes in processing speed, a construct reflecting the rapidity of executing cognitive operations. Although cognitive ability and processing speed are linked to spindles and sigma power in the sleep electroencephalogram (EEG), little is known about such associations in early childhood, a time of major neuronal refinement. We calculated EEG power for slow (10-13 Hz) and fast (13.25-17 Hz) sigma power from all-night high-density electroencephalography (EEG) in a cross-sectional sample of healthy preschool children (n = 10, 4.3 ± 1.0 years). Processing speed was assessed as simple reaction time. On average, reaction time was 1409 ± 251 ms; slow sigma power was 4.0 ± 1.5 μV²; and fast sigma power was 0.9 ± 0.2 μV². Both slow and fast sigma power predominated over central areas. Only slow sigma power was correlated with processing speed in a large parietal electrode cluster (p < 0.05, r ranging from -0.6 to -0.8), such that greater power predicted faster reaction time. Our findings indicate regional correlates between sigma power and processing speed that are specific to early childhood and provide novel insights into the neurobiological features of the EEG that may underlie developing cognitive abilities.

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

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.

Topological properties of flat electroencephalography's state space

NASA Astrophysics Data System (ADS)

Ken, Tan Lit; Ahmad, Tahir bin; Mohd, Mohd Sham bin; Ngien, Su Kong; Suwa, Tohru; Meng, Ong Sie

2016-02-01

Neuroinverse problem are often associated with complex neuronal activity. It involves locating problematic cell which is highly challenging. While epileptic foci localization is possible with the aid of EEG signals, it relies greatly on the ability to extract hidden information or pattern within EEG signals. Flat EEG being an enhancement of EEG is a way of viewing electroencephalograph on the real plane. In the perspective of dynamical systems, Flat EEG is equivalent to epileptic seizure hence, making it a great platform to study epileptic seizure. Throughout the years, various mathematical tools have been applied on Flat EEG to extract hidden information that is hardly noticeable by traditional visual inspection. While these tools have given worthy results, the journey towards understanding seizure process completely is yet to be succeeded. Since the underlying structure of Flat EEG is dynamic and is deemed to contain wealthy information regarding brainstorm, it would certainly be appealing to explore in depth its structures. To better understand the complex seizure process, this paper studies the event of epileptic seizure via Flat EEG in a more general framework by means of topology, particularly, on the state space where the event of Flat EEG lies.

Using the nonlinear control of anaesthesia-induced hypersensitivity of EEG at burst suppression level to test the effects of radiofrequency radiation on brain function

PubMed Central

Lipping, Tarmo; Rorarius, Michael; Jäntti, Ville; Annala, Kari; Mennander, Ari; Ferenets, Rain; Toivonen, Tommi; Toivo, Tim; Värri, Alpo; Korpinen, Leena

2009-01-01

Background In this study, investigating the effects of mobile phone radiation on test animals, eleven pigs were anaesthetised to the level where burst-suppression pattern appears in the electroencephalogram (EEG). At this level of anaesthesia both human subjects and animals show high sensitivity to external stimuli which produce EEG bursts during suppression. The burst-suppression phenomenon represents a nonlinear control system, where low-amplitude EEG abruptly switches to very high amplitude bursts. This switching can be triggered by very minor stimuli and the phenomenon has been described as hypersensitivity. To test if also radio frequency (RF) stimulation can trigger this nonlinear control, the animals were exposed to pulse modulated signal of a GSM mobile phone at 890 MHz. In the first phase of the experiment electromagnetic field (EMF) stimulation was randomly switched on and off and the relation between EEG bursts and EMF stimulation onsets and endpoints were studied. In the second phase a continuous RF stimulation at 31 W/kg was applied for 10 minutes. The ECG, the EEG, and the subcutaneous temperature were recorded. Results No correlation between the exposure and the EEG burst occurrences was observed in phase I measurements. No significant changes were observed in the EEG activity of the pigs during phase II measurements although several EEG signal analysis methods were applied. The temperature measured subcutaneously from the pigs' head increased by 1.6°C and the heart rate by 14.2 bpm on the average during the 10 min exposure periods. Conclusion The hypothesis that RF radiation would produce sensory stimulation of somatosensory, auditory or visual system or directly affect the brain so as to produce EEG bursts during suppression was not confirmed. PMID:19615084

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.

Thalamocortical and intracortical laminar connectivity determines sleep spindle properties.

PubMed

Krishnan, Giri P; Rosen, Burke Q; Chen, Jen-Yung; Muller, Lyle; Sejnowski, Terrence J; Cash, Sydney S; Halgren, Eric; Bazhenov, Maxim

2018-06-27

Sleep spindles are brief oscillatory events during non-rapid eye movement (NREM) sleep. Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance, suggesting spindle involvement in memory consolidation. Here, using computational models, we identified network mechanisms that may explain differences in spindle properties across cortical structures. First, we report that differences in spindle occurrence between MEG and EEG data may arise from the contrasting properties of the core and matrix thalamocortical systems. The matrix system, projecting superficially, has wider thalamocortical fanout compared to the core system, which projects to middle layers, and requires the recruitment of a larger population of neurons to initiate a spindle. This property was sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers, as observed in the EEG signal. In contrast, spindles in the core system occurred more frequently but less synchronously, as observed in the MEG recordings. Furthermore, consistent with human recordings, in the model, spindles occurred independently in the core system but the matrix system spindles commonly co-occurred with core spindles. We also found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system, leading to widespread spindle activity. Our study predicts that plasticity of intra- and inter-cortical connectivity can potentially be a mechanism for increased spindle density as has been observed during learning.

Working memory training using EEG neurofeedback in normal young adults.

PubMed

Xiong, Shi; Cheng, Chen; Wu, Xia; Guo, Xiaojuan; Yao, Li; Zhang, Jiacai

2014-01-01

Recent studies have shown that working memory (WM) performance can be improved by intensive and adaptive computerized training. Here, we explored the WM training effect using Electroencephalography (EEG) neurofeedback (NF) in normal young adults. In the first study, we identified the EEG features related to WM in normal young adults. The receiver operating characteristic (ROC) curve showed that the power ratio of the theta-to-alpha rhythms in the anterior-parietal region, accurately classified a high percentage of the EEG trials recorded during WM and fixation control (FC) tasks. Based on these results, a second study aimed to assess the training effects of the theta-to-alpha ratio and tested the hypothesis that up-regulating the power ratio can improve working memory behavior. Our results demonstrated that these normal young adults succeeded in improving their WM performance with EEG NF, and the pre- and post-test evaluations also indicated that WM performance increase in experimental group was significantly greater than control groups. In summary, our findings provided preliminarily evidence that WM performance can be improved through learned regulation of the EEG power ratio using EEG NF.

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.

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.

"Cuts in Action": A High-Density EEG Study Investigating the Neural Correlates of Different Editing Techniques in Film.

PubMed

Heimann, Katrin S; Uithol, Sebo; Calbi, Marta; Umiltà, Maria A; Guerra, Michele; Gallese, Vittorio

2017-08-01

In spite of their striking differences with real-life perception, films are perceived and understood without effort. Cognitive film theory attributes this to the system of continuity editing, a system of editing guidelines outlining the effect of different cuts and edits on spectators. A major principle in this framework is the 180° rule, a rule recommendation that, to avoid spectators' attention to the editing, two edited shots of the same event or action should not be filmed from angles differing in a way that expectations of spatial continuity are strongly violated. In the present study, we used high-density EEG to explore the neural underpinnings of this rule. In particular, our analysis shows that cuts and edits in general elicit early ERP component indicating the registration of syntactic violations as known from language, music, and action processing. However, continuity edits and cuts-across the line differ from each other regarding later components likely to be indicating the differences in spatial remapping as well as in the degree of conscious awareness of one's own perception. Interestingly, a time-frequency analysis of the occipital alpha rhythm did not support the hypothesis that such differences in processing routes are mainly linked to visual attention. On the contrary, our study found specific modulations of the central mu rhythm ERD as an indicator of sensorimotor activity, suggesting that sensorimotor networks might play an important role. We think that these findings shed new light on current discussions about the role of attention and embodied perception in film perception and should be considered when explaining spectators' different experience of different kinds of cuts. Copyright © 2016 Cognitive Science Society, Inc.

A transition in brain state during propofol-induced unconsciousness.

PubMed

Mukamel, Eran A; Pirondini, Elvira; Babadi, Behtash; Wong, Kin Foon Kevin; Pierce, Eric T; Harrell, P Grace; Walsh, John L; Salazar-Gomez, Andres F; Cash, Sydney S; Eskandar, Emad N; Weiner, Veronica S; Brown, Emery N; Purdon, Patrick L

2014-01-15

Rhythmic oscillations shape cortical dynamics during active behavior, sleep, and general anesthesia. Cross-frequency phase-amplitude coupling is a prominent feature of cortical oscillations, but its role in organizing conscious and unconscious brain states is poorly understood. Using high-density EEG and intracranial electrocorticography during gradual induction of propofol general anesthesia in humans, we discovered a rapid drug-induced transition between distinct states with opposite phase-amplitude coupling and different cortical source distributions. One state occurs during unconsciousness and may be similar to sleep slow oscillations. A second state occurs at the loss or recovery of consciousness and resembles an enhanced slow cortical potential. These results provide objective electrophysiological landmarks of distinct unconscious brain states, and could be used to help improve EEG-based monitoring for general anesthesia.

High-density EEG characterization of brain responses to auditory rhythmic stimuli during wakefulness and NREM sleep.

PubMed

Lustenberger, Caroline; Patel, Yogi A; Alagapan, Sankaraleengam; Page, Jessica M; Price, Betsy; Boyle, Michael R; Fröhlich, Flavio

2018-04-01

Auditory rhythmic sensory stimulation modulates brain oscillations by increasing phase-locking to the temporal structure of the stimuli and by increasing the power of specific frequency bands, resulting in Auditory Steady State Responses (ASSR). The ASSR is altered in different diseases of the central nervous system such as schizophrenia. However, in order to use the ASSR as biological markers for disease states, it needs to be understood how different vigilance states and underlying brain activity affect the ASSR. Here, we compared the effects of auditory rhythmic stimuli on EEG brain activity during wake and NREM sleep, investigated the influence of the presence of dominant sleep rhythms on the ASSR, and delineated the topographical distribution of these modulations. Participants (14 healthy males, 20-33 years) completed on the same day a 60 min nap session and two 30 min wakefulness sessions (before and after the nap). During these sessions, amplitude modulated (AM) white noise auditory stimuli at different frequencies were applied. High-density EEG was continuously recorded and time-frequency analyses were performed to assess ASSR during wakefulness and NREM periods. Our analysis revealed that depending on the electrode location, stimulation frequency applied and window/frequencies analysed the ASSR was significantly modulated by sleep pressure (before and after sleep), vigilance state (wake vs. NREM sleep), and the presence of slow wave activity and sleep spindles. Furthermore, AM stimuli increased spindle activity during NREM sleep but not during wakefulness. Thus, (1) electrode location, sleep history, vigilance state and ongoing brain activity needs to be carefully considered when investigating ASSR and (2) auditory rhythmic stimuli during sleep might represent a powerful tool to boost sleep spindles. Copyright © 2017 Elsevier Inc. All rights reserved.

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG.

PubMed

Bai, Ou; Lin, Peter; Vorbach, Sherry; Li, Jiang; Furlani, Steve; Hallett, Mark

2007-12-01

To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG). Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention. Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training. Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.

Neurophysiology of perceived confidence.

PubMed

Graziano, Martin; Parra, Lucas C; Sigman, Mariano

2010-01-01

In a partial report paradigm, subjects observe during a brief presentation a cluttered field and after some time - typically ranging from 100 ms to a second - are asked to report a subset of the presented elements. A vast buffer of information is transiently available to be broadcasted which, if not retrieved in time, fades rapidly without reaching consciousness. An interesting feature of this experiment is that objective performance and subjective confidence is decoupled. This converts this paradigm in an ideal vehicle to understand the brain dynamics of the construction of confidence. Here we report a high-density EEG experiment in which we infer elements of the EEG response which are indicative of subjective confidence. We find that an early response during encoding partially correlates with perceived confidence. However, the bulk of the weight of subjective confidence is determined during a late, N400-like waveform, during the retrieval stage. This shows that we can find markers of access to internal, subjective states, that are uncoupled from objective response and stimulus properties of the task, and we propose that this can be used with decoding methods of EEG to infer subjective mental states.

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

Using Elman recurrent neural networks with conjugate gradient algorithm in determining the anesthetic the amount of anesthetic medicine to be applied.

PubMed

Güntürkün, Rüştü

2010-08-01

In this study, Elman recurrent neural networks have been defined by using conjugate gradient algorithm in order to determine the depth of anesthesia in the continuation stage of the anesthesia and to estimate the amount of medicine to be applied at that moment. The feed forward neural networks are also used for comparison. The conjugate gradient algorithm is compared with back propagation (BP) for training of the neural Networks. The applied artificial neural network is composed of three layers, namely the input layer, the hidden layer and the output layer. The nonlinear activation function sigmoid (sigmoid function) has been used in the hidden layer and the output layer. EEG data has been recorded with Nihon Kohden 9200 brand 22-channel EEG device. The international 8-channel bipolar 10-20 montage system (8 TB-b system) has been used in assembling the recording electrodes. EEG data have been recorded by being sampled once in every 2 milliseconds. The artificial neural network has been designed so as to have 60 neurons in the input layer, 30 neurons in the hidden layer and 1 neuron in the output layer. The values of the power spectral density (PSD) of 10-second EEG segments which correspond to the 1-50 Hz frequency range; the ratio of the total power of PSD values of the EEG segment at that moment in the same range to the total of PSD values of EEG segment taken prior to the anesthesia.

Electroencephalogram spindle activity during dexmedetomidine sedation and physiological sleep.

PubMed

Huupponen, E; Maksimow, A; Lapinlampi, P; Särkelä, M; Saastamoinen, A; Snapir, A; Scheinin, H; Scheinin, M; Meriläinen, P; Himanen, S-L; Jääskeläinen, S

2008-02-01

Dexmedetomidine, a selective alpha(2)-adrenoceptor agonist, induces a unique, sleep-like state of sedation. The objective of the present work was to study human electroencephalogram (EEG) sleep spindles during dexmedetomidine sedation and compare them with spindles during normal physiological sleep, to test the hypothesis that dexmedetomidine exerts its effects via normal sleep-promoting pathways. EEG was continuously recorded from a bipolar frontopolar-laterofrontal derivation with Entropy Module (GE Healthcare) during light and deep dexmedetomidine sedation (target-controlled infusions set at 0.5 and 3.2 ng/ml) in 11 healthy subjects, and during physiological sleep in 10 healthy control subjects. Sleep spindles were visually scored and quantitatively analyzed for density, duration, amplitude (band-pass filtering) and frequency content (matching pursuit approach), and compared between the two groups. In visual analysis, EEG activity during dexmedetomidine sedation was similar to physiological stage 2 (S2) sleep with slight to moderate amount of slow-wave activity and abundant sleep spindle activity. In quantitative EEG analyses, sleep spindles were similar during dexmedetomidine sedation and normal sleep. No statistically significant differences were found in spindle density, amplitude or frequency content, but the spindles during dexmedetomidine sedation had longer duration (mean 1.11 s, SD 0.14 s) than spindles in normal sleep (mean 0.88 s, SD 0.14 s; P=0.0014). Analysis of sleep spindles shows that dexmedetomidine produces a state closely resembling physiological S2 sleep in humans, which gives further support to earlier experimental evidence for activation of normal non-rapid eye movement sleep-promoting pathways by this sedative agent.

Traumatic Brain Injury Detection Using Electrophysiological Methods

PubMed Central

Rapp, Paul E.; Keyser, David O.; Albano, Alfonso; Hernandez, Rene; Gibson, Douglas B.; Zambon, Robert A.; Hairston, W. David; Hughes, John D.; Krystal, Andrew; Nichols, Andrew S.

2015-01-01

Measuring neuronal activity with electrophysiological methods may be useful in detecting neurological dysfunctions, such as mild traumatic brain injury (mTBI). This approach may be particularly valuable for rapid detection in at-risk populations including military service members and athletes. Electrophysiological methods, such as quantitative electroencephalography (qEEG) and recording event-related potentials (ERPs) may be promising; however, the field is nascent and significant controversy exists on the efficacy and accuracy of the approaches as diagnostic tools. For example, the specific measures derived from an electroencephalogram (EEG) that are most suitable as markers of dysfunction have not been clearly established. A study was conducted to summarize and evaluate the statistical rigor of evidence on the overall utility of qEEG as an mTBI detection tool. The analysis evaluated qEEG measures/parameters that may be most suitable as fieldable diagnostic tools, identified other types of EEG measures and analysis methods of promise, recommended specific measures and analysis methods for further development as mTBI detection tools, identified research gaps in the field, and recommended future research and development thrust areas. The qEEG study group formed the following conclusions: (1) Individual qEEG measures provide limited diagnostic utility for mTBI. However, many measures can be important features of qEEG discriminant functions, which do show significant promise as mTBI detection tools. (2) ERPs offer utility in mTBI detection. In fact, evidence indicates that ERPs can identify abnormalities in cases where EEGs alone are non-disclosing. (3) The standard mathematical procedures used in the characterization of mTBI EEGs should be expanded to incorporate newer methods of analysis including non-linear dynamical analysis, complexity measures, analysis of causal interactions, graph theory, and information dynamics. (4) Reports of high specificity in qEEG evaluations of TBI must be interpreted with care. High specificities have been reported in carefully constructed clinical studies in which healthy controls were compared against a carefully selected TBI population. The published literature indicates, however, that similar abnormalities in qEEG measures are observed in other neuropsychiatric disorders. While it may be possible to distinguish a clinical patient from a healthy control participant with this technology, these measures are unlikely to discriminate between, for example, major depressive disorder, bipolar disorder, or TBI. The specificities observed in these clinical studies may well be lost in real world clinical practice. (5) The absence of specificity does not preclude clinical utility. The possibility of use as a longitudinal measure of treatment response remains. However, efficacy as a longitudinal clinical measure does require acceptable test–retest reliability. To date, very few test–retest reliability studies have been published with qEEG data obtained from TBI patients or from healthy controls. This is a particular concern because high variability is a known characteristic of the injured central nervous system. PMID:25698950

Traumatic brain injury detection using electrophysiological methods.

PubMed

Rapp, Paul E; Keyser, David O; Albano, Alfonso; Hernandez, Rene; Gibson, Douglas B; Zambon, Robert A; Hairston, W David; Hughes, John D; Krystal, Andrew; Nichols, Andrew S

2015-01-01

Measuring neuronal activity with electrophysiological methods may be useful in detecting neurological dysfunctions, such as mild traumatic brain injury (mTBI). This approach may be particularly valuable for rapid detection in at-risk populations including military service members and athletes. Electrophysiological methods, such as quantitative electroencephalography (qEEG) and recording event-related potentials (ERPs) may be promising; however, the field is nascent and significant controversy exists on the efficacy and accuracy of the approaches as diagnostic tools. For example, the specific measures derived from an electroencephalogram (EEG) that are most suitable as markers of dysfunction have not been clearly established. A study was conducted to summarize and evaluate the statistical rigor of evidence on the overall utility of qEEG as an mTBI detection tool. The analysis evaluated qEEG measures/parameters that may be most suitable as fieldable diagnostic tools, identified other types of EEG measures and analysis methods of promise, recommended specific measures and analysis methods for further development as mTBI detection tools, identified research gaps in the field, and recommended future research and development thrust areas. The qEEG study group formed the following conclusions: (1) Individual qEEG measures provide limited diagnostic utility for mTBI. However, many measures can be important features of qEEG discriminant functions, which do show significant promise as mTBI detection tools. (2) ERPs offer utility in mTBI detection. In fact, evidence indicates that ERPs can identify abnormalities in cases where EEGs alone are non-disclosing. (3) The standard mathematical procedures used in the characterization of mTBI EEGs should be expanded to incorporate newer methods of analysis including non-linear dynamical analysis, complexity measures, analysis of causal interactions, graph theory, and information dynamics. (4) Reports of high specificity in qEEG evaluations of TBI must be interpreted with care. High specificities have been reported in carefully constructed clinical studies in which healthy controls were compared against a carefully selected TBI population. The published literature indicates, however, that similar abnormalities in qEEG measures are observed in other neuropsychiatric disorders. While it may be possible to distinguish a clinical patient from a healthy control participant with this technology, these measures are unlikely to discriminate between, for example, major depressive disorder, bipolar disorder, or TBI. The specificities observed in these clinical studies may well be lost in real world clinical practice. (5) The absence of specificity does not preclude clinical utility. The possibility of use as a longitudinal measure of treatment response remains. However, efficacy as a longitudinal clinical measure does require acceptable test-retest reliability. To date, very few test-retest reliability studies have been published with qEEG data obtained from TBI patients or from healthy controls. This is a particular concern because high variability is a known characteristic of the injured central nervous system.

Gray matter density in relation to different facets of verbal creativity.

PubMed

Fink, Andreas; Koschutnig, Karl; Hutterer, Lisa; Steiner, Elisabeth; Benedek, Mathias; Weber, Bernhard; Reishofer, Gernot; Papousek, Ilona; Weiss, Elisabeth M

2014-07-01

Neuroscience studies on creativity have revealed highly variegated findings that often seem to be inconsistent. As recently argued in Fink and Benedek (Neurosci Biobehav Rev, 2012), this might be primarily due to the broad diversity in defining and measuring creativity as well as to the diversity of experimental procedures and methodologies used in this field of research. In specifically focusing on one measure of brain activation and on the well-established process of creative ideation (i.e., divergent thinking), EEG studies revealed a quite consistent and replicable pattern of right-lateralized brain activity over posterior parietal and occipital sites. In this study, we related regional gray matter density (as assessed by means of voxel-based morphometry) to different facets of psychometrically determined verbal creativity in a sample of 71 participants. Results revealed that verbal creativity was significantly and positively associated with gray matter density in clusters involving the right cuneus and the right precuneus. Enhanced gray matter density in these regions may be indicative of vivid imaginative abilities in more creative individuals. These findings complement existing functional studies on creative ideation which are, taken as a whole, among the most consistent findings in this field.

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

Assessing the memorization of TV commercials with the use of high resolution EEG: a pilot study.

PubMed

Astolfi, L; Soranzo, R; Cincotti, F; Mattia, D; Scarano, G; Gaudiano, I; Marciani, M G; Salinari, S; De Vico Fallani, F; Babiloni, F

2008-01-01

The present work intends to evaluate the functional characteristics of the cerebral network during the successful memory encoding of TV commercials. We estimated the functional networks in the frequency domain from a set of high-resolution EEG data. High resolution EEG recordings were performed in a group of healthy subjects and the cortical activity during the observation of TV commercials was evaluated in several regions of interest coincident with the Brodmann areas (BAs). Summarizing the main results of the present study, a sign of the memorization of a particular set of TV commercials have been found in a group of investigated subjects with the aid of advanced modern tools for the acquisition and the processing of EEG data. The cerebral processes involved during the observation of TV commercials that were remembered successively by the population examined (RMB dataset) are generated by the posterior parietal cortices and the prefrontal areas, rather bilaterally and are irrespective of the frequency bands analyzed. Such results are compatible with previously results obtained from EEG recordings with superficial electrodes as well as with the brain activations observed with the use of MEG and fMRI devices.

Individual neurophysiological profile in external effects investigation

NASA Astrophysics Data System (ADS)

Schastlivtseva, Daria; Tatiana Kotrovskaya, D..

Cortex biopotentials are the significant elements in human psychophysiological individuality. Considered that cortical biopotentials are diverse and individually stable, therefore there is the existence of certain dependence between the basic properties of higher nervous activity and cerebral bioelectric activity. The main purpose of the study was to reveal the individual neurophysiological profile and CNS initial functional state manifestation in human electroencephalogram (EEG) under effect of inert gases (argon, xenon, helium), hypoxia, pressure changes (0.02 and 0.2 MPa). We obtained 5-minute eyes closed background EEG on 19 scalp positions using Ag/AgCl electrodes mounted in an electrode cap. All EEG signals were re-referenced to average earlobes; Fast Furies Transformation analysis was used to calculate the relative power spectrum of delta-, theta-, alpha- and beta frequency band in artifact-free EEG. The study involved 26 healthy men who provided written informed consent, aged 20 to 35 years. Data obtained depend as individual EEG type and initial central nervous functional state as intensity, duration and mix of factors. Pronounced alpha rhythm in the raw EEG correlated with their adaptive capacity under studied factor exposure. Representation change and zonal distribution perversion of EEG alpha rhythm were accompanied by emotional instability, increased anxiety and difficulty adapting subjects. High power factor or combination factor with psychological and emotional or physical exertion minimizes individual EEG pattern.

Role of ictal baseline shifts and ictal high-frequency oscillations in stereo-electroencephalography analysis of mesial temporal lobe seizures.

PubMed

Wu, Shasha; Kunhi Veedu, Hari Prasad; Lhatoo, Samden D; Koubeissi, Mohamad Z; Miller, Jonathan P; Lüders, Hans O

2014-05-01

To assess the role of ictal baseline shifts (IBS) and ictal high-frequency oscillations (iHFOs) in intracranial electroencephalography (EEG) presurgical evaluation by analysis of the spatial and temporal relationship of IBS, iHFOs with ictal conventional stereo-electroencephalography (icEEG) in mesial temporal lobe seizures (MTLS). We studied 15 adult patients with medically refractory MTLS who underwent monitoring with depth electrodes. Seventy-five ictal EEG recordings at 1,000 Hz sampling rate were studied. Visual comparison of icEEG, IBS, and iHFOs were performed using Nihon-Kohden Neurofax systems (acquisition range 0.016-300 Hz). Each recorded ictal EEG was analyzed with settings appropriate for displaying icEEG, IBS, and iHFOs. IBS and iHFOs were observed in all patients and in 91% and 81% of intracranial seizures, respectively. IBS occurred before (22%), at (57%), or after (21%) icEEG onset. In contrast, iHFOs occurred at (30%) or after (70%) icEEG onset. The onset of iHFOs was 11.5 s later than IBS onset (p < 0.0001). All of the earliest onset of IBS and 70% of the onset of iHFOs overlapped with the ictal onset zone (IOZ). Compared with iHFOs, interictal HFOs (itHFOs) were less correlated with IOZ. In contrast to icEEG, IBS and iHFOs had smaller spatial distributions in 70% and 100% of the seizures, respectively. An IBS dipole was observed in 66% of the seizures. Eighty-seven percent of the dipoles had a negative pole at the anterior/medial part of amygdala/hippocampus complex (A-H complex) and a positive pole at the posterior/lateral part of the A-H complex. The results suggest that evaluation of IBS and iHFOs, in addition to routine icEEG, helps in more accurately defining the IOZ. This study also shows that the onset and the spatial distribution of icEEG, IBS, and iHFOs do not overlap, suggesting that they reflect different cellular or network dynamics. Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.

High-throughput ocular artifact reduction in multichannel electroencephalography (EEG) using component subspace projection.

PubMed

Ma, Junshui; Bayram, Sevinç; Tao, Peining; Svetnik, Vladimir

2011-03-15

After a review of the ocular artifact reduction literature, a high-throughput method designed to reduce the ocular artifacts in multichannel continuous EEG recordings acquired at clinical EEG laboratories worldwide is proposed. The proposed method belongs to the category of component-based methods, and does not rely on any electrooculography (EOG) signals. Based on a concept that all ocular artifact components exist in a signal component subspace, the method can uniformly handle all types of ocular artifacts, including eye-blinks, saccades, and other eye movements, by automatically identifying ocular components from decomposed signal components. This study also proposes an improved strategy to objectively and quantitatively evaluate artifact reduction methods. The evaluation strategy uses real EEG signals to synthesize realistic simulated datasets with different amounts of ocular artifacts. The simulated datasets enable us to objectively demonstrate that the proposed method outperforms some existing methods when no high-quality EOG signals are available. Moreover, the results of the simulated datasets improve our understanding of the involved signal decomposition algorithms, and provide us with insights into the inconsistency regarding the performance of different methods in the literature. The proposed method was also applied to two independent clinical EEG datasets involving 28 volunteers and over 1000 EEG recordings. This effort further confirms that the proposed method can effectively reduce ocular artifacts in large clinical EEG datasets in a high-throughput fashion. Copyright © 2011 Elsevier B.V. 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.

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

Envelope responses in single-trial EEG indicate attended speaker in a 'cocktail party'.

PubMed

Horton, Cort; Srinivasan, Ramesh; D'Zmura, Michael

2014-08-01

Recent studies have shown that auditory cortex better encodes the envelope of attended speech than that of unattended speech during multi-speaker ('cocktail party') situations. We investigated whether these differences were sufficiently robust within single-trial electroencephalographic (EEG) data to accurately determine where subjects attended. Additionally, we compared this measure to other established EEG markers of attention. High-resolution EEG was recorded while subjects engaged in a two-speaker 'cocktail party' task. Cortical responses to speech envelopes were extracted by cross-correlating the envelopes with each EEG channel. We also measured steady-state responses (elicited via high-frequency amplitude modulation of the speech) and alpha-band power, both of which have been sensitive to attention in previous studies. Using linear classifiers, we then examined how well each of these features could be used to predict the subjects' side of attention at various epoch lengths. We found that the attended speaker could be determined reliably from the envelope responses calculated from short periods of EEG, with accuracy improving as a function of sample length. Furthermore, envelope responses were far better indicators of attention than changes in either alpha power or steady-state responses. These results suggest that envelope-related signals recorded in EEG data can be used to form robust auditory BCI's that do not require artificial manipulation (e.g., amplitude modulation) of stimuli to function.

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…

EEG in children with spelling disabilities.

PubMed

Byring, R F; Salmi, T K; Sainio, K O; Orn, H P

1991-10-01

A total of 23 13-year-old boys with spelling disabilities and 21 matched controls were studied. EEG was recorded for visual and quantitative analysis, including FFT band powers and normalized slope descriptors (NSD). Visual analysis showed general excess of slow activity, as well as an excess of temporal slow wave activity in the index group. Quantitative analysis showed low alpha and beta powers, and low "activity" and high "complexity" (NSD) in parieto-occipital derivations in the index group. Quantitative EEG (qEEG) parameter ratios between temporal and parieto-occipital derivations were increased in the index group, implying a lack of spatial differentiation in these EEGs. In covariance analysis the qEEG parameter differences between the index group and controls were partly explained by the neurotic traits made evident in psychological tests. This implies that psychopathological artifacts should be considered in qEEG examinations of children with cognitive handicaps. Differences in anterior/posterior qEEG ratios were, however, little affected by any confounding factors. Thus these qEEG ratios seem potentially useful in clinical assessments of children with learning disabilities.

Prognostic value of EEG in different etiological types of coma.

PubMed

Khaburzania, M; Beridze, M

2013-06-01

Study aimed at evaluation of prognostic value of standard EEG in different etiology of coma and the influence of etiological factor on the EEG patterns and coma outcome. Totally 175 coma patients were investigated. Patients were evaluated by Glasgow Coma Scale (GCS), clinically and by 16 channel electroencephalography. Auditory evoked potentials studied by EEG -regime for evoked potentials in patients with vegetative state (VS). Patients divided in 8 groups according to coma etiology. All patients were studied for photoreaction, brainstem reflexes, localization of sound and pain, length of coma state and outcome. Brain injury visualized by conventional CT. Outcome defined as death, VS, recovery with disability and without disability. Disability was rated by Disability Rating Scale (DRS). Recovered patients assessed by Mini Mental State Examination (MMSE) scale. Statistics performed by SPSS-11.0. From 175 coma patients 55 patients died, 23 patients found in VS, 97 patients recovered with and without disability. In all etiological groups of coma the background EEG patterns were established. Correspondence analysis of all investigated factors revealed that sound localization had the significant association with EEG delta and theta rhythms and with recovery from coma state (Chi-sqr. =31.10493; p= 0.000001). Among 23 VS patients 9 patients had the signs of MCS and showed the long latency waves (p300) after binaural stimulation. The high amplitude theta frequencies in frontal and temporal lobes significantly correlated with prolongation of latency of cognitive evoked potentials (r=+0.47; p<0.01). Etiological factor had the significant effect on EEG patterns' association with coma outcome only in hemorrhagic and traumatic coma (chi-sqr.=12.95; p<0.005; chi-sqr.=7.92; p<0.03 respectively). Significant correlations established between the delta and theta EEG patterns and coma outcome. Low amplitude decreased power delta and theta frequencies correlated with SND in survived coma patients (r=+0.21; p<0.001; r=+0.27; p<0.001 respectively). Standard EEG is the useful tool for elucidation of coma patients with a high probability to recover as well as those patients, who are at high risk of SND in case of recovery from coma state.

Exergaming with a pediatric exoskeleton: Facilitating rehabilitation and research in children with cerebral palsy.

PubMed

Bulea, Thomas C; Lerner, Zachary F; Gravunder, Andrew J; Damiano, Diane L

2017-07-01

Effective rehabilitation of children with cerebral palsy (CP) requires intensive task-specific exercise but many in this population lack the motor capabilities to complete the desired training tasks. Providing robotic assistance is a potential solution yet the effects of this assistance are unclear. We combined a novel exoskeleton and exercise video game (exergame) to create a new rehabilitation paradigm for children with CP. We incorporated high density electroencephalography (EEG) to assess cortical activity. Movement to targets in the game was controlled by knee extension while standing. The distance between targets was the same with and without the exoskeleton to isolate the effect of robotic assistance. Our results show that children with CP maintain or increase knee extensor muscle activity during knee extension in the presence of synergistic robotic assistance. Our EEG findings also demonstrate that participants remained engaged in the exercise with robotic assistance. Interestingly we observed a developmental trajectory of sensorimotor mu rhythm in children with CP similar, though delayed, to those reported in typically developing children. While not the goal here, the exoskeleton significantly increased knee extension in 3/6 participants during use. Future work will focus on utilizing the exoskeleton to enhance volitional knee extension capability and in combination with EMG and EEG to study sensorimotor cortex response to progressive exercise in children with CP.

Patients with Rheumatoid Arthritis and Chronic Pain Display Enhanced Alpha Power Density at Rest.

PubMed

Meneses, Francisco M; Queirós, Fernanda C; Montoya, Pedro; Miranda, José G V; Dubois-Mendes, Selena M; Sá, Katia N; Luz-Santos, Cleber; Baptista, Abrahão F

2016-01-01

Patients with chronic pain due to neuropathy or musculoskeletal injury frequently exhibit reduced alpha and increased theta power densities. However, little is known about electrical brain activity and chronic pain in patients with rheumatoid arthritis (RA). For this purpose, we evaluated power densities of spontaneous electroencephalogram (EEG) band frequencies (delta, theta, alpha, and beta) in females with persistent pain due to RA. This was a cross-sectional study of 21 participants with RA and 21 healthy controls (mean age = 47.20; SD = 10.40). EEG was recorded at rest over 5 min with participant's eyes closed. Twenty electrodes were placed over five brain regions (frontal, central, parietal, temporal, and occipital). Significant differences were observed in depression and anxiety with higher scores in RA participants than healthy controls (p = 0.002). Participants with RA exhibited increased average absolute alpha power density in all brain regions when compared to controls [F (1.39) = 6.39, p = 0.016], as well as increased average relative alpha power density [F (1.39) = 5.82, p = 0.021] in all regions, except the frontal region, controlling for depression/anxiety. Absolute theta power density also increased in the frontal, central, and parietal regions for participants with RA when compared to controls [F (1, 39) = 4.51, p = 0.040], controlling for depression/anxiety. Differences were not exhibited on beta and delta absolute and relative power densities. The diffuse increased alpha may suggest a possible neurogenic mechanism for chronic pain in individuals with RA.

The Importance of the Numerical Resolution of the Laplace Equation in the optimization of a Neuronal Stimulation Technique

NASA Astrophysics Data System (ADS)

Faria, Paula

2010-09-01

For the past few years, the potential of transcranial direct current stimulation (tDCS) for the treatment of several pathologies has been investigated. Knowledge of the current density distribution is an important factor in optimizing such applications of tDCS. For this goal, we used the finite element method to solve the Laplace equation in a spherical head model in order to investigate the three dimensional distribution of the current density and the variation of its intensity with depth using different electrodes montages: the traditional one with two sponge electrodes and new electrode montages: with sponge and EEG electrodes and with EEG electrodes varying the numbers of electrodes. The simulation results confirm the effectiveness of the mixed system which may allow the use of tDCS and EEG recording concomitantly and may help to optimize this neuronal stimulation technique. The numerical results were used in a promising application of tDCS in epilepsy.

Towards affordable biomarkers of frontotemporal dementia: A classification study via network's information sharing.

PubMed

Dottori, Martin; Sedeño, Lucas; Martorell Caro, Miguel; Alifano, Florencia; Hesse, Eugenia; Mikulan, Ezequiel; García, Adolfo M; Ruiz-Tagle, Amparo; Lillo, Patricia; Slachevsky, Andrea; Serrano, Cecilia; Fraiman, Daniel; Ibanez, Agustin

2017-06-19

Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost, portability, and growing robustness. Here, we relied on EEG signals and a novel information-sharing method to study resting-state connectivity in patients with behavioral variant frontotemporal dementia (bvFTD) and controls. To evaluate the specificity of our results, we also tested Alzheimer's disease (AD) patients. The classification power of the ensuing connectivity patterns was evaluated through a supervised classification algorithm (support vector machine). In addition, we compared the classification power yielded by (i) functional connectivity, (ii) relevant neuropsychological tests, and (iii) a combination of both. BvFTD patients exhibited a specific pattern of hypoconnectivity in mid-range frontotemporal links, which showed no alterations in AD patients. These functional connectivity alterations in bvFTD were replicated with a low-density EEG setting (20 electrodes). Moreover, while neuropsychological tests yielded acceptable discrimination between bvFTD and controls, the addition of connectivity results improved classification power. Finally, classification between bvFTD and AD patients was better when based on connectivity than on neuropsychological measures. Taken together, such findings underscore the relevance of EEG measures as potential biomarker signatures for clinical settings.

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.

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

Electroencephalography as a tool for evidence-based diagnosis and improved outcomes in children with epilepsy in a resource-poor setting.

PubMed

Lagunju, Ike Oluwa Abiola; Oyinlade, Alexander Opebiyi; Atalabi, Omolola Mojisola; Ogbole, Godwin; Tedimola, Olushola; Famosaya, Abimbola; Ogunniyi, Adesola; Ogunseyinde, Ayotunde Oluremi; Ragin, Ann

2015-01-01

Electroencephalography (EEG) remains the most important investigative modality in the diagnostic evaluation of individuals with epilepsy. Children living with epilepsy in the developing world are faced with challenges of lack of access to appropriate diagnostic evaluation and a high risk of misdiagnosis and inappropriate therapy. We appraised EEG studies in a cohort of Nigerian children with epilepsy seen in a tertiary center in order to evaluate access to and the impact of EEG in the diagnostic evaluation of the cases. Inter-ictal EEG was requested in all cases of pediatric epilepsy seen at the pediatric neurology clinic of the University College Hospital, Ibadan, Nigeria over a period of 18 months. Clinical diagnosis without EEG evaluation was compared with the final diagnosis post- EEG evaluation. A total of 329 EEGs were recorded in 329 children, aged 3 months to 16 years, median 61.0 months. Clinical evaluation pre-EEG classified 69.3% of the epilepsies as generalized. The a posteriori EEG evaluations showed a considerably higher proportion of localization-related epilepsies (33.6%). The final evaluation post EEG showed a 21% reduction in the proportion of cases labeled as generalized epilepsy and a 55% increase in cases of localization-related epilepsy(p<0.001). Here we show that there is a high risk of misdiagnosis and therefore the use of inappropriate therapies in children with epilepsy in the absence of EEG evaluation. The implications of our findings in the resource-poor country scenario are key for reducing the burden of care and cost of epilepsy treatment on both the caregivers and the already overloaded tertiary care services.

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.

Electroencephalogram signatures of loss and recovery of consciousness from propofol

PubMed Central

Purdon, Patrick L.; Pierce, Eric T.; Mukamel, Eran A.; Prerau, Michael J.; Walsh, John L.; Wong, Kin Foon K.; Salazar-Gomez, Andres F.; Harrell, Priscilla G.; Sampson, Aaron L.; Cimenser, Aylin; Ching, ShiNung; Kopell, Nancy J.; Tavares-Stoeckel, Casie; Habeeb, Kathleen; Merhar, Rebecca; Brown, Emery N.

2013-01-01

Unconsciousness is a fundamental component of general anesthesia (GA), but anesthesiologists have no reliable ways to be certain that a patient is unconscious. To develop EEG signatures that track loss and recovery of consciousness under GA, we recorded high-density EEGs in humans during gradual induction of and emergence from unconsciousness with propofol. The subjects executed an auditory task at 4-s intervals consisting of interleaved verbal and click stimuli to identify loss and recovery of consciousness. During induction, subjects lost responsiveness to the less salient clicks before losing responsiveness to the more salient verbal stimuli; during emergence they recovered responsiveness to the verbal stimuli before recovering responsiveness to the clicks. The median frequency and bandwidth of the frontal EEG power tracked the probability of response to the verbal stimuli during the transitions in consciousness. Loss of consciousness was marked simultaneously by an increase in low-frequency EEG power (<1 Hz), the loss of spatially coherent occipital alpha oscillations (8–12 Hz), and the appearance of spatially coherent frontal alpha oscillations. These dynamics reversed with recovery of consciousness. The low-frequency phase modulated alpha amplitude in two distinct patterns. During profound unconsciousness, alpha amplitudes were maximal at low-frequency peaks, whereas during the transition into and out of unconsciousness, alpha amplitudes were maximal at low-frequency nadirs. This latter phase–amplitude relationship predicted recovery of consciousness. Our results provide insights into the mechanisms of propofol-induced unconsciousness, establish EEG signatures of this brain state that track transitions in consciousness precisely, and suggest strategies for monitoring the brain activity of patients receiving GA. PMID:23487781

EEG spectral analysis in primary insomnia: NREM period effects and sex differences.

PubMed

Buysse, Daniel J; Germain, Anne; Hall, Martica L; Moul, Douglas E; Nofzinger, Eric A; Begley, Amy; Ehlers, Cindy L; Thompson, Wesley; Kupfer, David J

2008-12-01

To compare NREM EEG power in primary insomnia (PI) and good sleeper controls (GSC), examining both sex and NREM period effects; to examine relationships between EEG power, clinical characteristics, and self-reports of sleep. Overnight polysomnographic study. Sleep laboratory. PI (n=48; 29 women) and GSC (n=25; 15 women). None. EEG power from 1-50 Hz was computed for artifact-free sleep epochs across four NREM periods. Repeated measures mixed effect models contrasted differences between groups, EEG frequency bands, and NREM periods. EEG power-frequency curves were modeled using regressions with fixed knot splines. Mixed models showed no significant group (PI vs. GSC) differences; marginal sex differences (delta and theta bands); significant differences across NREM periods; and group*sex and group*NREM period interactions, particularly in beta and gamma bands. Modeled power-frequency curves showed no group difference in whole-night NREM, but PI had higher power than GSC from 18-40 Hz in the first NREM period. Among women, PI had higher 16 to 44-Hz power than GSC in the first 3 NREM periods, and higher 3 to 5-Hz power across all NREM periods. PI and GSC men showed no consistent differences in EEG power. High-frequency EEG power was not related to clinical or subjective sleep ratings in PI. Women with PI, but not men, showed increased high-frequency and low-frequency EEG activity during NREM sleep compared to GSC, particularly in early NREM periods. Sex and NREM period may moderate quantitative EEG differences between PI and GSC.

Kinesthetic and vestibular information modulate alpha activity during spatial navigation: a mobile EEG study

PubMed Central

Ehinger, Benedikt V.; Fischer, Petra; Gert, Anna L.; Kaufhold, Lilli; Weber, Felix; Pipa, Gordon; König, Peter

2014-01-01

In everyday life, spatial navigation involving locomotion provides congruent visual, vestibular, and kinesthetic information that need to be integrated. Yet, previous studies on human brain activity during navigation focus on stationary setups, neglecting vestibular and kinesthetic feedback. The aim of our work is to uncover the influence of those sensory modalities on cortical processing. We developed a fully immersive virtual reality setup combined with high-density mobile electroencephalography (EEG). Participants traversed one leg of a triangle, turned on the spot, continued along the second leg, and finally indicated the location of their starting position. Vestibular and kinesthetic information was provided either in combination, as isolated sources of information, or not at all within a 2 × 2 full factorial intra-subjects design. EEG data were processed by clustering independent components, and time-frequency spectrograms were calculated. In parietal, occipital, and temporal clusters, we detected alpha suppression during the turning movement, which is associated with a heightened demand of visuo-attentional processing and closely resembles results reported in previous stationary studies. This decrease is present in all conditions and therefore seems to generalize to more natural settings. Yet, in incongruent conditions, when different sensory modalities did not match, the decrease is significantly stronger. Additionally, in more anterior areas we found that providing only vestibular but no kinesthetic information results in alpha increase. These observations demonstrate that stationary experiments omit important aspects of sensory feedback. Therefore, it is important to develop more natural experimental settings in order to capture a more complete picture of neural correlates of spatial navigation. PMID:24616681

Kinesthetic and vestibular information modulate alpha activity during spatial navigation: a mobile EEG study.

PubMed

Ehinger, Benedikt V; Fischer, Petra; Gert, Anna L; Kaufhold, Lilli; Weber, Felix; Pipa, Gordon; König, Peter

2014-01-01

In everyday life, spatial navigation involving locomotion provides congruent visual, vestibular, and kinesthetic information that need to be integrated. Yet, previous studies on human brain activity during navigation focus on stationary setups, neglecting vestibular and kinesthetic feedback. The aim of our work is to uncover the influence of those sensory modalities on cortical processing. We developed a fully immersive virtual reality setup combined with high-density mobile electroencephalography (EEG). Participants traversed one leg of a triangle, turned on the spot, continued along the second leg, and finally indicated the location of their starting position. Vestibular and kinesthetic information was provided either in combination, as isolated sources of information, or not at all within a 2 × 2 full factorial intra-subjects design. EEG data were processed by clustering independent components, and time-frequency spectrograms were calculated. In parietal, occipital, and temporal clusters, we detected alpha suppression during the turning movement, which is associated with a heightened demand of visuo-attentional processing and closely resembles results reported in previous stationary studies. This decrease is present in all conditions and therefore seems to generalize to more natural settings. Yet, in incongruent conditions, when different sensory modalities did not match, the decrease is significantly stronger. Additionally, in more anterior areas we found that providing only vestibular but no kinesthetic information results in alpha increase. These observations demonstrate that stationary experiments omit important aspects of sensory feedback. Therefore, it is important to develop more natural experimental settings in order to capture a more complete picture of neural correlates of spatial navigation.

Engagement Assessment Using EEG Signals

NASA Technical Reports Server (NTRS)

Li, Feng; Li, Jiang; McKenzie, Frederic; Zhang, Guangfan; Wang, Wei; Pepe, Aaron; Xu, Roger; Schnell, Thomas; Anderson, Nick; Heitkamp, Dean

2012-01-01

In this paper, we present methods to analyze and improve an EEG-based engagement assessment approach, consisting of data preprocessing, feature extraction and engagement state classification. During data preprocessing, spikes, baseline drift and saturation caused by recording devices in EEG signals are identified and eliminated, and a wavelet based method is utilized to remove ocular and muscular artifacts in the EEG recordings. In feature extraction, power spectrum densities with 1 Hz bin are calculated as features, and these features are analyzed using the Fisher score and the one way ANOVA method. In the classification step, a committee classifier is trained based on the extracted features to assess engagement status. Finally, experiment results showed that there exist significant differences in the extracted features among different subjects, and we have implemented a feature normalization procedure to mitigate the differences and significantly improved the engagement assessment performance.

Frontal predominance of a relative increase in sleep delta and theta EEG activity after sleep loss in humans

NASA Technical Reports Server (NTRS)

Cajochen, C.; Foy, R.; Dijk, D. J.; Czeisler, C. A. (Principal Investigator)

1999-01-01

The effect of sleep deprivation (40 h) on topographic and temporal aspects of electroencephalographic (EEG) activity during sleep was investigated by all night spectral analysis in six young volunteers. The sleep-deprivation-induced increase of EEG power density in the delta and theta frequencies (1-7 Hz) during nonREM sleep, assessed along the antero-posterior axis (midline: Fz, Cz, Pz, Oz), was significantly larger in the more frontal derivations (Fz, Cz) than in the more parietal derivations (Pz, Oz). This frequency-specific frontal predominance was already present in the first 30 min of recovery sleep, and dissipated in the course of the 8-h sleep episode. The data demonstrate that the enhancement of slow wave EEG activity during sleep following extended wakefulness is most pronounced in frontal cortical areas.

An electroencephalographic Peak Density Function to detect memorization during the observation of TV commercials.

PubMed

Vecchiato, G; Di Flumeri, G; Maglione, A G; Cherubino, P; Kong, W; Trettel, A; Babiloni, F

2014-01-01

Nowadays, there is a growing interest in measuring the impact of advertisements through the estimation of cerebral reactions. Several techniques and methods are used and discussed in the consumer neuroscience. In such a context, the present paper provides a novel method to estimate the level of memorization occurred in subjects during the observation of TV commercials. In particular, the present work introduce the Peak Density Function (PDF) as an electroencephalographic (EEG) time-varying variable which is correlated with the cerebral events of memorization of TV commercials. The analysis has been performed on the EEG activity recorded on twenty healthy subjects during the exposition to several advertisements. After the EEG recordings, an interview has been performed to obtain the information about the memorized scenes for all the video clips watched by the subjects. Such information has been put in correlation with the occurrence of transient peaks of EEG synchronization in the theta band, by computing the PDF. The present results show that the increase of PDF is positively correlated, scene by scene, (R=0.46, p<;0.01) with the spontaneous recall of subjects. This technology could be of help for marketers to overcome the drawbacks of the standard marketing tools (e.g., interviews, focus groups) when analyzing the impact of advertisements.

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.

Topography of Slow Sigma Power during Sleep is Associated with Processing Speed in Preschool Children

PubMed Central

Doucette, Margaret R.; Kurth, Salome; Chevalier, Nicolas; Munakata, Yuko; LeBourgeois, Monique K.

2015-01-01

Cognitive development is influenced by maturational changes in processing speed, a construct reflecting the rapidity of executing cognitive operations. Although cognitive ability and processing speed are linked to spindles and sigma power in the sleep electroencephalogram (EEG), little is known about such associations in early childhood, a time of major neuronal refinement. We calculated EEG power for slow (10–13 Hz) and fast (13.25–17 Hz) sigma power from all-night high-density electroencephalography (EEG) in a cross-sectional sample of healthy preschool children (n = 10, 4.3 ± 1.0 years). Processing speed was assessed as simple reaction time. On average, reaction time was 1409 ± 251 ms; slow sigma power was 4.0 ± 1.5 μV2; and fast sigma power was 0.9 ± 0.2 μV2. Both slow and fast sigma power predominated over central areas. Only slow sigma power was correlated with processing speed in a large parietal electrode cluster (p < 0.05, r ranging from −0.6 to −0.8), such that greater power predicted faster reaction time. Our findings indicate regional correlates between sigma power and processing speed that are specific to early childhood and provide novel insights into the neurobiological features of the EEG that may underlie developing cognitive abilities. PMID:26556377

Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study.

PubMed

Lee, Poh Foong; Kan, Donica Pei Xin; Croarkin, Paul; Phang, Cheng Kar; Doruk, Deniz

2018-01-01

There is an unmet need for practical and reliable biomarkers for mood disorders in young adults. Identifying the brain activity associated with the early signs of depressive disorders could have important diagnostic and therapeutic implications. In this study we sought to investigate the EEG characteristics in young adults with newly identified depressive symptoms. Based on the initial screening, a total of 100 participants (n = 50 euthymic, n = 50 depressive) underwent 32-channel EEG acquisition. Simple logistic regression and C-statistic were used to explore if EEG power could be used to discriminate between the groups. The strongest EEG predictors of mood using multivariate logistic regression models. Simple logistic regression analysis with subsequent C-statistics revealed that only high-alpha and beta power originating from the left central cortex (C3) have a reliable discriminative value (ROC curve >0.7 (70%)) for differentiating the depressive group from the euthymic group. Multivariate regression analysis showed that the single most significant predictor of group (depressive vs. euthymic) is the high-alpha power over C3 (p = 0.03). The present findings suggest that EEG is a useful tool in the identification of neurophysiological correlates of depressive symptoms in young adults with no previous psychiatric history. Our results could guide future studies investigating the early neurophysiological changes and surrogate outcomes in depression. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Quantitative change of EEG and respiration signals during mindfulness meditation.

PubMed

Ahani, Asieh; Wahbeh, Helane; Nezamfar, Hooman; Miller, Meghan; Erdogmus, Deniz; Oken, Barry

2014-05-14

This study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The physiological signals during meditation and control conditions were analyzed with signal processing. EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis, phase analysis and classification to evaluate an objective marker for meditation. Different frequency bands showed differences in meditation and control conditions. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy (85%) at discriminating between meditation and control conditions than a classifier using the EEG signal only (78%). Support vector machine (SVM) classifier with EEG and respiration feature vector is a viable objective marker for meditation ability. This classifier should be able to quantify different levels of meditation depth and meditation experience in future studies.

Quantitative change of EEG and respiration signals during mindfulness meditation

PubMed Central

2014-01-01

Background This study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The physiological signals during meditation and control conditions were analyzed with signal processing. Methods EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis, phase analysis and classification to evaluate an objective marker for meditation. Results Different frequency bands showed differences in meditation and control conditions. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy (85%) at discriminating between meditation and control conditions than a classifier using the EEG signal only (78%). Conclusion Support vector machine (SVM) classifier with EEG and respiration feature vector is a viable objective marker for meditation ability. This classifier should be able to quantify different levels of meditation depth and meditation experience in future studies. PMID:24939519

A comparative study of electrical potential sensors and Ag/AgCl electrodes for characterising spontaneous and event related electroencephalagram signals.

PubMed

Fatoorechi, M; Parkinson, J; Prance, R J; Prance, H; Seth, A K; Schwartzman, D J

2015-08-15

Electroencephalography (EEG) is still a widely used imaging tool that combines high temporal resolution with a relatively low cost. Ag/AgCl metal electrodes have been the gold standard for non-invasively monitoring electrical brain activity. Although reliable, these electrodes have multiple drawbacks: they suffer from noise, such as offset potential drift, and usability issues, for example, difficult skin preparation and cross-coupling of adjacent electrodes. In order to tackle these issues a prototype Electric Potential Sensor (EPS) device based on an auto-zero operational amplifier was developed and evaluated. The EPS is a novel active ultrahigh impedance capacitively coupled sensor. The absence of 1/f noise makes the EPS ideal for use with signal frequencies of ∼10Hz or less. A comprehensive study was undertaken to compare neural signals recorded by the EPS with a standard commercial EEG system. Quantitatively, highly similar signals were observed between the EPS and EEG sensors for both free running and evoked brain activity with cross correlations of higher than 0.9 between the EPS and a standard benchmark EEG system. These studies comprised measurements of both free running EEG and Event Related Potentials (ERPs) from a commercial EEG system and EPS. The EPS provides a promising alternative with many added benefits compared to standard EEG sensors, including reduced setup time and elimination of sensor cross-coupling. In the future the scalability of the EPS will allow the implementation of a whole head ultra-dense EPS array. Copyright © 2015 Elsevier B.V. All rights reserved.

High-frequency neural activity and human cognition: past, present and possible future of intracranial EEG research

PubMed Central

Lachaux, Jean-Philippe; Axmacher, Nikolai; Mormann, Florian; Halgren, Eric; Crone, Nathan E.

2013-01-01

Human intracranial EEG (iEEG) recordings are primarily performed in epileptic patients for presurgical mapping. When patients perform cognitive tasks, iEEG signals reveal high-frequency neural activities (HFA, between around 40 Hz and 150 Hz) with exquisite anatomical, functional and temporal specificity. Such HFA were originally interpreted in the context of perceptual or motor binding, in line with animal studies on gamma-band (‘40Hz’) neural synchronization. Today, our understanding of HFA has evolved into a more general index of cortical processing: task-induced HFA reveals, with excellent spatial and time resolution, the participation of local neural ensembles in the task-at-hand, and perhaps the neural communication mechanisms allowing them to do so. This review promotes the claim that studying HFA with iEEG provides insights into the neural bases of cognition that cannot be derived as easily from other approaches, such as fMRI. We provide a series of examples supporting that claim, drawn from studies on memory, language and default-mode networks, and successful attempts of real-time functional mapping. These examples are followed by several guidelines for HFA research, intended for new groups interested by this approach. Overall, iEEG research on HFA should play an increasing role in cognitive neuroscience in humans, because it can be explicitly linked to basic research in animals. We conclude by discussing the future evolution of this field, which might expand that role even further, for instance through the use of multi-scale electrodes and the fusion of iEEG with MEG and fMRI. PMID:22750156

Association of autonomic nervous system and EEG scalp potential during playing 2D Grand Turismo 5.

PubMed

Subhani, Ahmad Rauf; Likun, Xia; Saeed Malik, Aamir

2012-01-01

Cerebral activation and autonomic nervous system have importance in studies such as mental stress. The aim of this study is to analyze variations in EEG scalp potential which may influence autonomic activation of heart while playing video games. Ten healthy participants were recruited in this study. Electroencephalogram (EEG) and electrocardiogram (ECG) signals were measured simultaneously during playing video game and rest conditions. Sympathetic and parasympathetic innervations of heart were evaluated from heart rate variability (HRV), derived from the ECG. Scalp potential was measured by the EEG. The results showed a significant upsurge in the value theta Fz/alpha Pz (p<0.001) while playing game. The results also showed tachycardia while playing video game as compared to rest condition (p<0.005). Normalized low frequency power and ratio of low frequency/high frequency power were significantly increased while playing video game and normalized high frequency power sank during video games. Results showed synchronized activity of cerebellum and sympathetic and parasympathetic innervation of heart.

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

PubMed Central

Sadeh, Boaz; Yovel, Galit

2014-01-01

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological effects. Specifically, by creating a transient interference with the normal activity of a target region and measuring changes in an electrophysiological signal, we can establish a causal link between the stimulated brain area or network and the electrophysiological signal that we record. If target brain areas are functionally defined with prior fMRI scan, TMS could be used to link the fMRI activations with evoked potentials recorded. However, conducting such experiments presents significant technical challenges given the high amplitude artifacts introduced into the EEG signal by the magnetic pulse, and the difficulty to successfully target areas that were functionally defined by fMRI. Here we describe a methodology for combining these three common tools: TMS, EEG, and fMRI. We explain how to guide the stimulator's coil to the desired target area using anatomical or functional MRI data, how to record EEG during concurrent TMS, how to design an ERP study suitable for EEG-TMS combination and how to extract reliable ERP from the recorded data. We will provide representative results from a previously published study, in which fMRI-guided TMS was used concurrently with EEG to show that the face-selective N1 and the body-selective N1 component of the ERP are associated with distinct neural networks in extrastriate cortex. This method allows us to combine the high spatial resolution of fMRI with the high temporal resolution of TMS and EEG and therefore obtain a comprehensive understanding of the neural basis of various cognitive processes. PMID:24893706

A high performance sensorimotor beta rhythm-based brain computer interface associated with human natural motor behavior

NASA Astrophysics Data System (ADS)

Bai, Ou; Lin, Peter; Vorbach, Sherry; Floeter, Mary Kay; Hattori, Noriaki; Hallett, Mark

2008-03-01

To explore the reliability of a high performance brain-computer interface (BCI) using non-invasive EEG signals associated with human natural motor behavior does not require extensive training. We propose a new BCI method, where users perform either sustaining or stopping a motor task with time locking to a predefined time window. Nine healthy volunteers, one stroke survivor with right-sided hemiparesis and one patient with amyotrophic lateral sclerosis (ALS) participated in this study. Subjects did not receive BCI training before participating in this study. We investigated tasks of both physical movement and motor imagery. The surface Laplacian derivation was used for enhancing EEG spatial resolution. A model-free threshold setting method was used for the classification of motor intentions. The performance of the proposed BCI was validated by an online sequential binary-cursor-control game for two-dimensional cursor movement. Event-related desynchronization and synchronization were observed when subjects sustained or stopped either motor execution or motor imagery. Feature analysis showed that EEG beta band activity over sensorimotor area provided the largest discrimination. With simple model-free classification of beta band EEG activity from a single electrode (with surface Laplacian derivation), the online classifications of the EEG activity with motor execution/motor imagery were: >90%/~80% for six healthy volunteers, >80%/~80% for the stroke patient and ~90%/~80% for the ALS patient. The EEG activities of the other three healthy volunteers were not classifiable. The sensorimotor beta rhythm of EEG associated with human natural motor behavior can be used for a reliable and high performance BCI for both healthy subjects and patients with neurological disorders. Significance: The proposed new non-invasive BCI method highlights a practical BCI for clinical applications, where the user does not require extensive training.

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation.

PubMed

Zhang, Zutao; Luo, Dianyuan; Rasim, Yagubov; Li, Yanjun; Meng, Guanjun; Xu, Jian; Wang, Chunbai

2016-02-19

In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver's EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver's vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.

Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification.

PubMed

Rifai Chai; Naik, Ganesh R; Sai Ho Ling; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T

2017-07-01

This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation

PubMed Central

Zhang, Zutao; Luo, Dianyuan; Rasim, Yagubov; Li, Yanjun; Meng, Guanjun; Xu, Jian; Wang, Chunbai

2016-01-01

In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver’s EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver’s vigilance level . Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model. PMID:26907278

Ultra-slow mechanical stimulation of olfactory epithelium modulates consciousness by slowing cerebral rhythms in humans.

PubMed

Piarulli, A; Zaccaro, A; Laurino, M; Menicucci, D; De Vito, A; Bruschini, L; Berrettini, S; Bergamasco, M; Laureys, S; Gemignani, A

2018-04-26

The coupling between respiration and neural activity within olfactory areas and hippocampus has recently been unambiguously demonstrated, its neurophysiological basis sustained by the well-assessed mechanical sensitivity of the olfactory epithelium. We herein hypothesize that this coupling reverberates to the whole brain, possibly modulating the subject's behavior and state of consciousness. The olfactory epithelium of 12 healthy subjects was stimulated with periodical odorless air-delivery (frequency 0.05 Hz, 8 s on, 12 off). Cortical electrical activity (High Density-EEG) and perceived state of consciousness have been studied. The stimulation induced i) an enhancement of delta-theta EEG activity over the whole cortex mainly involving the Limbic System and Default Mode Network structures, ii) a reversal of the overall information flow directionality from wake-like postero-anterior to NREM sleep-like antero-posterior, iii) the perception of having experienced an Altered State of Consciousness. These findings could shed further light via a neurophenomenological approach on the links between respiration, cerebral activity and subjective experience, suggesting a plausible neurophysiological basis for interpreting altered states of consciousness induced by respiration-based meditative practices.

Envelope responses in single-trial EEG indicate attended speaker in a “cocktail party”

PubMed Central

Horton, Cort; Srinivasan, Ramesh; D’Zmura, Michael

2014-01-01

Objective Recent studies have shown that auditory cortex better encodes the envelope of attended speech than that of unattended speech during multi-speaker (“cocktail party”) situations. We investigated whether these differences were sufficiently robust within single-trial EEG data to accurately determine where subjects attended. Additionally, we compared this measure to other established EEG markers of attention. Approach High-resolution EEG was recorded while subjects engaged in a two-speaker “cocktail party” task. Cortical responses to speech envelopes were extracted by cross-correlating the envelopes with each EEG channel. We also measured steady-state responses (elicited via high-frequency amplitude modulation of the speech) and alpha-band power, both of which have been sensitive to attention in previous studies. Using linear classifiers, we then examined how well each of these features could be used to predict the subjects’ side of attention at various epoch lengths. Main Results We found that the attended speaker could be determined reliably from the envelope responses calculated from short periods of EEG, with accuracy improving as a function of sample length. Furthermore, envelope responses were far better indicators of attention than changes in either alpha power or steady-state responses. Significance These results suggest that envelope-related signals recorded in EEG data can be used to form robust auditory BCI’s that do not require artificial manipulation (e.g., amplitude modulation) of stimuli to function. PMID:24963838

Changes in decibel scale wavelength properties of EEG with alertness levels while performing sustained attention tasks.

PubMed

Arjunan, Sridhar P; Kumar, Dinesh K; Jung, Tzyy-Ping

2009-01-01

Loss of alertness can have dire consequences for people controlling motorized equipment or for people in professions such as defense. Electroencephalogram (EEG) is known to be related to alertness of the person, but due to high level of noise and low signal strength, the use of EEG for such applications has been considered to be unreliable. This study reports the fractal analysis of EEG and identifies the use of maximum fractal length (MFL) as a feature that is inversely correlated with the alertness of the subject. The results show that MFL (of only single channel of EEG) indicates the loss of alertness of the individual with mean (inverse) correlation coefficient = 0.82.

EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach.

PubMed

Bosl, William J; Tager-Flusberg, Helen; Nelson, Charles A

2018-05-01

Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the need for simple measurements that could be implemented routinely during well-baby checkups. EEG is a relatively easy-to-use, low cost brain measurement tool that is being increasingly explored as a potential clinical tool for monitoring atypical brain development. EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months of age. Nonlinear features were computed from EEG signals and used as input to statistical learning methods. Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. Specificity, sensitivity and PPV were high, exceeding 95% at some ages. Prediction of ADOS calibrated severity scores for all infants in the study using only EEG data taken as early as 3 months of age was strongly correlated with the actual measured scores. This suggests that useful digital biomarkers might be extracted from EEG measurements.

EEG-fMRI evaluation of patients with mesial temporal lobe sclerosis.

PubMed

Avesani, Mirko; Giacopuzzi, Silvia; Bongiovanni, Luigi Giuseppe; Borelli, Paolo; Cerini, Roberto; Pozzi Mucelli, Roberto; Fiaschi, Antonio

2014-02-01

This preliminary study sought more information on blood oxygen level dependent (BOLD) activation, especially contralateral temporal/extratemporal spread, during continuous EEG-fMRI recordings in four patients with mesial temporal sclerosis (MTS). In two patients, EEG showed unilateral focal activity during the EEG-fMRI session concordant with the interictal focus previously identified with standard and video-poly EEG. In the other two patients EEG demonstrated a contralateral diffusion of the irritative focus. In the third patient (with the most drug-resistant form and also extratemporal clinical signs), there was an extratemporal diffusion over frontal regions, ipsilateral to the irritative focus. fMRI analysis confirmed a single activation in the mesial temporal region in two patients whose EEG showed unilateral focal activity, while it demonstrated a bilateral activation in the mesial temporal regions in the other two patients. In the third patient, fMRI demonstrated an activation in the supplementary motxor area. This study confirms the most significant activation with a high firing rate of the irritative focus, but also suggests the importance of using new techniques (such as EEG-fMRI to examine cerebral blood flow) to identify the controlateral limbic activation, and any other extratemporal activations, possible causes of drug resistance in MTS that may require a more precise pre-surgical evaluation with invasive techniques.

EEG-fMRI Evaluation of Patients with Mesial Temporal Lobe Sclerosis

PubMed Central

Avesani, Mirko; Giacopuzzi, Silvia; Bongiovanni, Luigi Giuseppe; Borelli, Paolo; Cerini, Roberto; Pozzi Mucelli, Roberto; Fiaschi, Antonio

2014-01-01

Summary This preliminary study sought more information on blood oxygen level dependent (BOLD) activation, especially contralateral temporal/extratemporal spread, during continuous EEG-fMRI recordings in four patients with mesial temporal sclerosis (MTS). In two patients, EEG showed unilateral focal activity during the EEG-fMRI session concordant with the interictal focus previously identified with standard and video-poly EEG. In the other two patients EEG demonstrated a contralateral diffusion of the irritative focus. In the third patient (with the most drug-resistant form and also extratemporal clinical signs), there was an extratemporal diffusion over frontal regions, ipsilateral to the irritative focus. fMRI analysis confirmed a single activation in the mesial temporal region in two patients whose EEG showed unilateral focal activity, while it demonstrated a bilateral activation in the mesial temporal regions in the other two patients. In the third patient, fMRI demonstrated an activation in the supplementary motxor area. This study confirms the most significant activation with a high firing rate of the irritative focus, but also suggests the importance of using new techniques (such as EEG-fMRI to examine cerebral blood flow) to identify the controlateral limbic activation, and any other extratemporal activations, possible causes of drug resistance in MTS that may require a more precise pre-surgical evaluation with invasive techniques. PMID:24571833

A novel hydrogel electrolyte extender for rapid application of EEG sensors and extended recordings.

PubMed

Kleffner-Canucci, Killian; Luu, Phan; Naleway, John; Tucker, Don M

2012-04-30

Dense-array EEG recordings are now commonplace in research and gaining acceptance in clinical settings. Application of many sensors with traditional electrolytes is time consuming. Saline electrolytes can be used to minimize application time but recording duration is limited due to evaporation. In the present study, we evaluate a NIPAm (N-isopropyl acrylamide:acrylic acid) base electrolyte extender for use with saline electrolytes. Sensor-scalp impedances and EEG data quality acquired with the electrolyte extender are compared with those obtained for saline and an EEG electrolyte commonly used in clinical exams (Elefix). The results show that when used in conjunction with saline, electrode-scalp impedances and data across the EEG spectrum are comparable with those obtained using Elefix EEG paste. When used in conjunction with saline, the electrolyte extender permits rapid application of dense-sensor arrays and stable, high-quality EEG data to be obtained for at least 4.5 h. This is an enabling technology that will make benefits of dense-array EEG recordings practical for clinical applications. Copyright © 2011 Elsevier B.V. All rights reserved.

Assessment of Cognitive Processing by Multiple Sclerosis Patients Using Electroencephalographic Energy Density Analysis

NASA Technical Reports Server (NTRS)

Montgomery, Leslie D.; Ku, Yu-Tsuan E.; Luna, Bernadette; Montgomery, Richard W.; Kliss, Mark (Technical Monitor)

1997-01-01

Recent neuropsychological studies demonstrate that cognitive dysfunction is a common symptom in patients with multiple sclerosis. In many cases the presence of cognitive impairment affects the patient's daily activities to a greater extent than would be found due to their physical disability alone. Cognitive dysfunction can have a significant impact on the quality of life of both the patient and that of their primary caregiver. Two cognitively impaired male MS patients were given a visual discrimination task before and after a one hour cooling period. The subjects were presented a series of either red or blue circles or triangles. One of these combinations, or one fourth of the stimuli, was designated as the "target" presentation. EEG was recorded from 20 scalp electrodes using a Tracor Northern 7500 EEG/ERP system. Oral and ear temperatures were obtained and recorded manually every five minutes during the one hour cooling period. The EEG ERP signatures from each series of stimuli were analyzed in the energy density domain to determine the locus of neural activity at each EEG sampling time. The first subject's ear temperature did not decrease during the cooling period. It was actually elevated approximately 0.05C by the end of the cooling period compared to his mean of control period value. In turn, Subject One's discrimination performance and cortical energy remained essentially the same after body cooling. In contrast, Subject Two's ear temperature decreased approx. 0.8C during his cooling period. Subject Two's ERROR score decreased from 12 during the precooling control period to 2 after cooling. His ENERGY value increased approximately 300%, from a precooling value of approximately 200 to a postcooling value of nearly 600.

Electroencephalographic imaging of higher brain function

NASA Technical Reports Server (NTRS)

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

1999-01-01

High temporal resolution is necessary to resolve the rapidly changing patterns of brain activity that underlie mental function. Electroencephalography (EEG) provides temporal resolution in the millisecond range. However, traditional EEG technology and practice provide insufficient spatial detail to identify relationships between brain electrical events and structures and functions visualized by magnetic resonance imaging or positron emission tomography. Recent advances help to overcome this problem by recording EEGs from more electrodes, by registering EEG data with anatomical images, and by correcting the distortion caused by volume conduction of EEG signals through the skull and scalp. In addition, statistical measurements of sub-second interdependences between EEG time-series recorded from different locations can help to generate hypotheses about the instantaneous functional networks that form between different cortical regions during perception, thought and action. Example applications are presented from studies of language, attention and working memory. Along with its unique ability to monitor brain function as people perform everyday activities in the real world, these advances make modern EEG an invaluable complement to other functional neuroimaging modalities.

Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach.

PubMed

Ahmad, Rana Fayyaz; Malik, Aamir Saeed; Kamel, Nidal; Reza, Faruque; Amin, Hafeez Ullah; Hussain, Muhammad

2017-01-01

Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful. In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes. Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature. The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.

Is the Surface Potential Integral of a Dipole in a Volume Conductor Always Zero? A Cloud Over the Average Reference of EEG and ERP.

PubMed

Yao, Dezhong

2017-03-01

Currently, average reference is one of the most widely adopted references in EEG and ERP studies. The theoretical assumption is the surface potential integral of a volume conductor being zero, thus the average of scalp potential recordings might be an approximation of the theoretically desired zero reference. However, such a zero integral assumption has been proved only for a spherical surface. In this short communication, three counter-examples are given to show that the potential integral over the surface of a dipole in a volume conductor may not be zero. It depends on the shape of the conductor and the orientation of the dipole. This fact on one side means that average reference is not a theoretical 'gold standard' reference, and on the other side reminds us that the practical accuracy of average reference is not only determined by the well-known electrode array density and its coverage but also intrinsically by the head shape. It means that reference selection still is a fundamental problem to be fixed in various EEG and ERP studies.

The Multidimensional Aspects of Sleep Spindles and Their Relationship to Word-Pair Memory Consolidation

PubMed Central

Lustenberger, Caroline; Wehrle, Flavia; Tüshaus, Laura; Achermann, Peter; Huber, Reto

2015-01-01

Study Objectives: Several studies proposed a link between sleep spindles and sleep dependent memory consolidation in declarative learning tasks. In addition to these state-like aspects of sleep spindles, they have also trait-like characteristics, i.e., were related to general cognitive performance, an important distinction that has often been neglected in correlative studies. Furthermore, from the multitude of different sleep spindle measures, often just one specific aspect was analyzed. Thus, we aimed at taking multidimensional aspects of sleep spindles into account when exploring their relationship to word-pair memory consolidation. Design: Each subject underwent 2 study nights with all-night high-density electroencephalographic (EEG) recordings. Sleep spindles were automatically detected in all EEG channels. Subjects were trained and tested on a word-pair learning task in the evening, and retested in the morning to assess sleep related memory consolidation (overnight retention). Trait-like aspects refer to the mean of both nights and state-like aspects were calculated as the difference between night 1 and night 2. Setting: Sleep laboratory. Participants: Twenty healthy male subjects (age: 23.3 ± 2.1 y) Measurements and Results: Overnight retention was negatively correlated with trait-like aspects of fast sleep spindle density and positively with slow spindle density on a global level. In contrast, state-like aspects were observed for integrated slow spindle activity, which was positively related to the differences in overnight retention in specific regions. Conclusion: Our results demonstrate the importance of a multidimensional approach when investigating the relationship between sleep spindles and memory consolidation and thereby provide a more complete picture explaining divergent findings in the literature. Citation: Lustenberger C, Wehrle F, Tüshaus L, Achermann P, Huber R. The multidimensional aspects of sleep spindles and their relationship to word-pair memory consolidation. SLEEP 2015;38(7):1093–1103. PMID:25845686

EEG Event-Related Desynchronization of patients with stroke during motor imagery of hand movement

NASA Astrophysics Data System (ADS)

Tabernig, Carolina B.; Carrere, Lucía C.; Lopez, Camila A.; Ballario, Carlos

2016-04-01

Brain Computer Interfaces (BCI) can be used for therapeutic purposes to improve voluntary motor control that has been affected post stroke. For this purpose, desynchronization of sensorimotor rhythms of the electroencephalographic signal (EEG) can be used. But it is necessary to study what happens in the affected motor cortex of this people. In this article, we analyse EEG recordings of hemiplegic stroke patients to determine if it is possible to detect desynchronization in the affected motor cortex during the imagination of movements of the affected hand. Six patients were included in the study; four evidenced desynchronization in the affected hemisphere, one of them showed no results and the EEG recordings of the last patient presented high noise level. These results suggest that we could use the desynchronization of sensorimotor rhythms of the EEG signal as a BCI paradigm in a rehabilitation programme.

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.

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.

Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI.

PubMed

Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R

2017-04-01

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners. To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering. The first method, reference layer adaptive filtering (RLAF), uses adaptive filtering with reference layer artifact data to optimize artifact subtraction from EEG. In the second method, multi band reference layer adaptive filtering (MBRLAF), adaptive filtering is performed on bandwidth limited sub-bands of the EEG and the reference channels. The results suggests that RLAF outperforms the baseline method, average artifact subtraction, in all settings and also its direct predecessor, reference layer artifact subtraction (RLAS), in lower (<35 Hz) frequency ranges. MBRLAF is computationally more demanding than RLAF, but highly effective in all EEG frequency ranges. Effectivity is determined by visual inspection, as well as root-mean-square voltage reduction and power reduction of EEG provided that physiological EEG components such as occipital EEG alpha power and visual evoked potentials (VEP) are preserved. We demonstrate that both, RLAF and MBRLAF, improve VEP quality. For that, we calculate the mean-squared-distance of single trial VEP to the mean VEP and estimate single trial VEP classification accuracies. We found that the average mean-squared-distance is lowest and the average classification accuracy is highest after MBLAF. RLAF was second best. In conclusion, the results suggests that RLAF and MBRLAF are potentially very effective in improving EEG quality of simultaneous EEG-fMRI. Highlights We present a new and reusable reference layer cap prototype for simultaneous EEG-fMRI We introduce new algorithms for reducing EEG artifacts due to simultaneous fMRI The algorithms combine a reference layer and adaptive filtering Several evaluation criteria suggest superior effectivity in terms of artifact reduction We demonstrate that physiological EEG components are preserved.

Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI

NASA Astrophysics Data System (ADS)

Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R.

2017-04-01

Objective. Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners. Approach. To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering. The first method, reference layer adaptive filtering (RLAF), uses adaptive filtering with reference layer artifact data to optimize artifact subtraction from EEG. In the second method, multi band reference layer adaptive filtering (MBRLAF), adaptive filtering is performed on bandwidth limited sub-bands of the EEG and the reference channels. Main results. The results suggests that RLAF outperforms the baseline method, average artifact subtraction, in all settings and also its direct predecessor, reference layer artifact subtraction (RLAS), in lower (<35 Hz) frequency ranges. MBRLAF is computationally more demanding than RLAF, but highly effective in all EEG frequency ranges. Effectivity is determined by visual inspection, as well as root-mean-square voltage reduction and power reduction of EEG provided that physiological EEG components such as occipital EEG alpha power and visual evoked potentials (VEP) are preserved. We demonstrate that both, RLAF and MBRLAF, improve VEP quality. For that, we calculate the mean-squared-distance of single trial VEP to the mean VEP and estimate single trial VEP classification accuracies. We found that the average mean-squared-distance is lowest and the average classification accuracy is highest after MBLAF. RLAF was second best. Significance. In conclusion, the results suggests that RLAF and MBRLAF are potentially very effective in improving EEG quality of simultaneous EEG-fMRI. Highlights We present a new and reusable reference layer cap prototype for simultaneous EEG-fMRI We introduce new algorithms for reducing EEG artifacts due to simultaneous fMRI The algorithms combine a reference layer and adaptive filtering Several evaluation criteria suggest superior effectivity in terms of artifact reduction We demonstrate that physiological EEG components are preserved

Methodological aspects of EEG and body dynamics measurements during motion

PubMed Central

Reis, Pedro M. R.; Hebenstreit, Felix; Gabsteiger, Florian; von Tscharner, Vinzenz; Lochmann, Matthias

2014-01-01

EEG involves the recording, analysis, and interpretation of voltages recorded on the human scalp which originate from brain gray matter. EEG is one of the most popular methods of studying and understanding the processes that underlie behavior. This is so, because EEG is relatively cheap, easy to wear, light weight and has high temporal resolution. In terms of behavior, this encompasses actions, such as movements that are performed in response to the environment. However, there are methodological difficulties which can occur when recording EEG during movement such as movement artifacts. Thus, most studies about the human brain have examined activations during static conditions. This article attempts to compile and describe relevant methodological solutions that emerged in order to measure body and brain dynamics during motion. These descriptions cover suggestions on how to avoid and reduce motion artifacts, hardware, software and techniques for synchronously recording EEG, EMG, kinematics, kinetics, and eye movements during motion. Additionally, we present various recording systems, EEG electrodes, caps and methods for determinating real/custom electrode positions. In the end we will conclude that it is possible to record and analyze synchronized brain and body dynamics related to movement or exercise tasks. PMID:24715858

Recording Visual Evoked Potentials and Auditory Evoked P300 at 9.4T Static Magnetic Field

PubMed Central

Hahn, David; Boers, Frank; Shah, N. Jon

2013-01-01

Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has shown a number of advantages that make this multimodal technique superior to fMRI alone. The feasibility of recording EEG at ultra-high static magnetic field up to 9.4T was recently demonstrated and promises to be implemented soon in fMRI studies at ultra high magnetic fields. Recording visual evoked potentials are expected to be amongst the most simple for simultaneous EEG/fMRI at ultra-high magnetic field due to the easy assessment of the visual cortex. Auditory evoked P300 measurements are of interest since it is believed that they represent the earliest stage of cognitive processing. In this study, we investigate the feasibility of recording visual evoked potentials and auditory evoked P300 in a 9.4T static magnetic field. For this purpose, EEG data were recorded from 26 healthy volunteers inside a 9.4T MR scanner using a 32-channel MR compatible EEG system. Visual stimulation and auditory oddball paradigm were presented in order to elicit evoked related potentials (ERP). Recordings made outside the scanner were performed using the same stimuli and EEG system for comparison purposes. We were able to retrieve visual P100 and auditory P300 evoked potentials at 9.4T static magnetic field after correction of the ballistocardiogram artefact using independent component analysis. The latencies of the ERPs recorded at 9.4T were not different from those recorded at 0T. The amplitudes of ERPs were higher at 9.4T when compared to recordings at 0T. Nevertheless, it seems that the increased amplitudes of the ERPs are due to the effect of the ultra-high field on the EEG recording system rather than alteration in the intrinsic processes that generate the electrophysiological responses. PMID:23650538

Recording visual evoked potentials and auditory evoked P300 at 9.4T static magnetic field.

PubMed

Arrubla, Jorge; Neuner, Irene; Hahn, David; Boers, Frank; Shah, N Jon

2013-01-01

Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has shown a number of advantages that make this multimodal technique superior to fMRI alone. The feasibility of recording EEG at ultra-high static magnetic field up to 9.4 T was recently demonstrated and promises to be implemented soon in fMRI studies at ultra high magnetic fields. Recording visual evoked potentials are expected to be amongst the most simple for simultaneous EEG/fMRI at ultra-high magnetic field due to the easy assessment of the visual cortex. Auditory evoked P300 measurements are of interest since it is believed that they represent the earliest stage of cognitive processing. In this study, we investigate the feasibility of recording visual evoked potentials and auditory evoked P300 in a 9.4 T static magnetic field. For this purpose, EEG data were recorded from 26 healthy volunteers inside a 9.4 T MR scanner using a 32-channel MR compatible EEG system. Visual stimulation and auditory oddball paradigm were presented in order to elicit evoked related potentials (ERP). Recordings made outside the scanner were performed using the same stimuli and EEG system for comparison purposes. We were able to retrieve visual P100 and auditory P300 evoked potentials at 9.4 T static magnetic field after correction of the ballistocardiogram artefact using independent component analysis. The latencies of the ERPs recorded at 9.4 T were not different from those recorded at 0 T. The amplitudes of ERPs were higher at 9.4 T when compared to recordings at 0 T. Nevertheless, it seems that the increased amplitudes of the ERPs are due to the effect of the ultra-high field on the EEG recording system rather than alteration in the intrinsic processes that generate the electrophysiological responses.

Fast fMRI provides high statistical power in the analysis of epileptic networks.

PubMed

Jacobs, Julia; Stich, Julia; Zahneisen, Benjamin; Assländer, Jakob; Ramantani, Georgia; Schulze-Bonhage, Andreas; Korinthenberg, Rudolph; Hennig, Jürgen; LeVan, Pierre

2014-03-01

EEG-fMRI is a unique method to combine the high temporal resolution of EEG with the high spatial resolution of MRI to study generators of intrinsic brain signals such as sleep grapho-elements or epileptic spikes. While the standard EPI sequence in fMRI experiments has a temporal resolution of around 2.5-3s a newly established fast fMRI sequence called MREG (Magnetic-Resonance-Encephalography) provides a temporal resolution of around 100ms. This technical novelty promises to improve statistics, facilitate correction of physiological artifacts and improve the understanding of epileptic networks in fMRI. The present study compares simultaneous EEG-EPI and EEG-MREG analyzing epileptic spikes to determine the yield of fast MRI in the analysis of intrinsic brain signals. Patients with frequent interictal spikes (>3/20min) underwent EEG-MREG and EEG-EPI (3T, 20min each, voxel size 3×3×3mm, EPI TR=2.61s, MREG TR=0.1s). Timings of the spikes were used in an event-related analysis to generate activation maps of t-statistics. (FMRISTAT, |t|>3.5, cluster size: 7 voxels, p<0.05 corrected). For both sequences, the amplitude and location of significant BOLD activations were compared with the spike topography. 13 patients were recorded and 33 different spike types could be analyzed. Peak T-values were significantly higher in MREG than in EPI (p<0.0001). Positive BOLD effects correlating with the spike topography were found in 8/29 spike types using the EPI and in 22/33 spikes types using the MREG sequence. Negative BOLD responses in the default mode network could be observed in 3/29 spike types with the EPI and in 19/33 with the MREG sequence. With the latter method, BOLD changes were observed even when few spikes occurred during the investigation. Simultaneous EEG-MREG thus is possible with good EEG quality and shows higher sensitivity in regard to the localization of spike-related BOLD responses than EEG-EPI. The development of new methods of analysis for this sequence such as modeling of physiological noise, temporal analysis of the BOLD signal and defining appropriate thresholds is required to fully profit from its high temporal resolution. © 2013.

Cortical characterization of the perception of intelligible and unintelligible speech measured via high-density electroencephalography.

PubMed

Utianski, Rene L; Caviness, John N; Liss, Julie M

2015-01-01

High-density electroencephalography was used to evaluate cortical activity during speech comprehension via a sentence verification task. Twenty-four participants assigned true or false to sentences produced with 3 noise-vocoded channel levels (1--unintelligible, 6--decipherable, 16--intelligible), during simultaneous EEG recording. Participant data were sorted into higher- (HP) and lower-performing (LP) groups. The identification of a late-event related potential for LP listeners in the intelligible condition and in all listeners when challenged with a 6-Ch signal supports the notion that this induced potential may be related to either processing degraded speech, or degraded processing of intelligible speech. Different cortical locations are identified as neural generators responsible for this activity; HP listeners are engaging motor aspects of their language system, utilizing an acoustic-phonetic based strategy to help resolve the sentence, while LP listeners do not. This study presents evidence for neurophysiological indices associated with more or less successful speech comprehension performance across listening conditions. Copyright © 2014 Elsevier Inc. All rights reserved.

Neuroelectrical Correlates of Trustworthiness and Dominance Judgments Related to the Observation of Political Candidates

PubMed Central

Vecchiato, Giovanni; Toppi, Jlenia; Maglione, Anton Giulio; Olejarczyk, Elzbieta; Astolfi, Laura; Mattia, Donatella; Colosimo, Alfredo; Babiloni, Fabio

2014-01-01

The present research investigates the neurophysiological activity elicited by fast observations of faces of real candidates during simulated political elections. We used simultaneous recording of electroencephalographic (EEG) signals as well as galvanic skin response (GSR) and heart rate (HR) as measurements of central and autonomic nervous systems. Twenty healthy subjects were asked to give judgments on dominance, trustworthiness, and a preference of vote related to the politicians' faces. We used high-resolution EEG techniques to map statistical differences of power spectral density (PSD) cortical activity onto a realistic head model as well as partial directed coherence (PDC) and graph theory metrics to estimate the functional connectivity networks and investigate the role of cortical regions of interest (ROIs). Behavioral results revealed that judgment of dominance trait is the most predictive of the outcome of the simulated elections. Statistical comparisons related to PSD and PDC values highlighted an asymmetry in the activation of frontal cortical areas associated with the valence of the judged trait as well as to the probability to cast the vote. Overall, our results highlight the existence of cortical EEG features which are correlated with the prediction of vote and with the judgment of trustworthy and dominant faces. PMID:25214884

[Neurological and neuropsychological comparison between subjects with learning disorder and those suffering from learning difficulties when eeg abnormalities are detected at pediatric age].

PubMed

Borsetti, L; Viberti, B; Ariano, C; Isocrono, A

2015-12-01

The objective of the study is to compare data and investigate the points of overlap between the two clinical conditions. The hypothesis is to observe a similar cognitive and neuropsychological profile in LD children and subjects with electroencephalogram (EEG) abnormalities. The present study consists of a descriptive analysis of 35 children who have been tested for suspected learning disorder (LD). The diagnostic protocol includes a detailed cognitive and neuropsychological evaluation, as well as logopedic and neuropsychomotor assessment. Children carried neurological visit, EEG in waking and encephalic nuclear magnetic resonance (NMR). In this study, anamnestic data and the results of some of the neuropsychological tests were administrated to children and subsequently were analyzed. Depending on EEG report (positive or negative), subjects were split in two subsample: subjects with "pure" LD and subjects who showed significant paroxysmal abnormalities at the EEG. This comparison shows that the profile of the two subsamples matches for many aspects. The only statistically significant differences are the increased impairment of meta-phonological skills and reading speed in children with EEG abnormalities. On the other hand, children with "pure" LD are inclined to manifest more frequently difficulties in highly-modularized processes, such as counting. In conclusion, the substantial overlap of the two profiles causes a reflection about the difficulty in making differential diagnosis in children who show a suspected LD, in absence of an accurate neurophysiological and neuroradiological investigation. The study did not find out useful markers to select subjects who should carry EEG and encephalic NMR. Our team established to keep EEG in waking in the diagnostic protocol, for all children with LD diagnosis. Only in case of abnormalities at the track, we prescribed EEG in sleeping and encephalic NMR.

Beyond the neuropsychology of dreaming: Insights into the neural basis of dreaming with new techniques of sleep recording and analysis.

PubMed

Cipolli, Carlo; Ferrara, Michele; De Gennaro, Luigi; Plazzi, Giuseppe

2017-10-01

Recent advances in electrophysiological [e.g., surface high-density electroencephalographic (hd-EEG) and intracranial recordings], video-polysomnography (video-PSG), transcranial stimulation and neuroimaging techniques allow more in-depth and more accurate investigation of the neural correlates of dreaming in healthy individuals and in patients with brain-damage, neurodegenerative diseases, sleep disorders or parasomnias. Convergent evidence provided by studies using these techniques in healthy subjects has led to a reformulation of several unresolved issues of dream generation and recall [such as the inter- and intra-individual differences in dream recall and the predictivity of specific EEG rhythms, such as theta in rapid eye movement (REM) sleep, for dream recall] within more comprehensive models of human consciousness and its variations across sleep/wake states than the traditional models, which were largely based on the neurophysiology of REM sleep in animals. These studies are casting new light on the neural bases (in particular, the activity of dorsal medial prefrontal cortex regions and hippocampus and amygdala areas) of the inter- and intra-individual differences in dream recall, the temporal location of specific contents or properties (e.g., lucidity) of dream experience and the processing of memories accessed during sleep and incorporated into dream content. Hd-EEG techniques, used on their own or in combination with neuroimaging, appear able to provide further important insights into how the brain generates not only dreaming during sleep but also some dreamlike experiences in waking. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Influence of Finasteride on Mean and Relative Spectral Density of EEG Bands in Rat Model of Thioacetamide-Induced Hepatic Encephalopathy.

PubMed

Mladenović, D; Hrnčić, D; Rašić-Marković, A; Macut, Dj; Stanojlović, O

2016-08-01

Liver failure is associated with a neuropsychiatric syndrome, known as hepatic encephalopathy (HE). Finasteride, inhibitor of neurosteroid synthesis, may improve the course of HE. The aim of our study was to investigate the influence of finasteride on mean and relative power density of EEG bands, determined by spectral analysis, in rat model of thioacetamide-induced HE. Male Wistar rats were divided into groups: (1) control; (2) thioacetamide-treated group, TAA (900 mg/kg); (3) finasteride-treated group, FIN (150 mg/kg); and (4) group treated with finasteride (150 mg/kg) and thioacetamide (900 mg/kg), FIN + TAA. Daily doses of FIN (50 mg/kg) and TAA (300 mg/kg) were administered during 3 subsequent days, and in FIN + TAA group FIN was administered 2 h before every dose of TAA. EEG was recorded 22-24 h after treatment and analyzed by fast Fourier transformation. While TAA did not induce significant changes in the beta band, mean and relative power in this band were significantly higher in FIN + TAA versus control group (p < 0.01). TAA caused a significant decline in mean power in alpha, theta, and delta band, and in FIN + TAA group the mean power in these bands was significantly higher compared with control. While in TAA group relative power was significantly decreased in theta (p < 0.01) and increased in delta band (p < 0.01) versus control, the opposite changes were found in FIN + TAA group: an increase in theta (p < 0.01) and a decrease in delta relative power (p < 0.01). In this study, finasteride pretreatment caused EEG changes that correspond to mild TAA-induced HE.

[A case of MM1+2 Creutzfeldt-Jakob disease with a longitudinal study of EEG and MRI].

PubMed

Katsube, Mizuho; Shiota, Yuri; Harada, Takayuki; Shibata, Hiroshi; Nagai, Atsushi

2013-11-01

We report a case of definite MM1 + 2 sporadic Creutzfeldt-Jakob disease (sCJD). A 66-year-old woman was admitted to our hospital with memory disturbance and disorientation for three months. On admission she presented a progressive cognitive insufficiency. Electroencephalography (EEG) revealed a frontal intermittent rhythmical delta activity (FIRDA) and the brain magnetic resonance imaging (MRI) showed high signal intensities in cerebral cortex on diffusion weighted images (DWI). After four months from the onset, she reached the akinetic mutism state followed by myoclonus. Follow up examination revealed that periodic synchronous discharge (PSD) was found in EEG, and DWI revealed enlargement of high signal intensity lesions in cerebral cortex. At seven months from the onset, PSD and high signal intensities of cortex became unclear with disappearance of myoclonus, and brain white matter lesions were evident on MRI. Serial studies of EEG and MRI revealed that PSD generalized from frontal lobe dominant pattern, while high signal intensity lesions of cortex diffusely increased on DWI. At ten months from the onset patient died. Pathological examination in brain showed moderate and diffuse neuronal cell loss and gliosis in cerebral cortex corresponding with DWI changes. The genotype at codon 129 of the prion protein (PrP) was homozygous methionine (MM) and the type of protease-resistant PrP (PrPres) was the mixed type of 1 and 2 in Western blot analysis. It has been rare to analyze the changes of EEG and MRI in the entire stage and to investigate pathological finding in the case of sCJD-MM1 + 2. A longitudinal examination of EEG and MRI is useful for early diagnosis of CJD. Also we could correlate these findings with clinical and histopathological phenotype.

An EEG (electroencephalogram) recording system with carbon wire electrodes for simultaneous EEG-fMRI (functional magnetic resonance imaging) recording

PubMed Central

Negishi, Michiro; Abildgaard, Mark; Laufer, Ilan; Nixon, Terry; Constable, Robert Todd

2008-01-01

Simultaneous EEG-fMRI (Electroencephalography-functional Magnetic Resonance Imaging) recording provides a means for acquiring high temporal resolution electrophysiological data and high spatial resolution metabolic data of the brain in the same experimental runs. Carbon wire electrodes (not metallic EEG electrodes with carbon wire leads) are suitable for simultaneous EEG-fMRI recording, because they cause less RF (radio-frequency) heating and susceptibility artifacts than metallic electrodes. These characteristics are especially desirable for recording the EEG in high field MRI scanners. Carbon wire electrodes are also comfortable to wear during long recording sessions. However, carbon electrodes have high electrode-electrolyte potentials compared to widely used Ag/AgCl (silver/silver-chloride) electrodes, which may cause slow voltage drifts. This paper introduces a prototype EEG recording system with carbon wire electrodes and a circuit that suppresses the slow voltage drift. The system was tested for the voltage drift, RF heating, susceptibility artifact, and impedance, and was also evaluated in a simultaneous ERP (event-related potential)-fMRI experiment. PMID:18588913

Electric field encephalography for brain activity monitoring.

PubMed

Versek, Craig William; Frasca, Tyler; Zhou, Jianlin; Chowdhury, Kaushik; Sridhar, Srinivas

2018-05-11

Objective - We describe an early-stage prototype of a new wireless electrophysiological sensor system, called NeuroDot, which can measure neuroelectric potentials and fields at the scalp in a new modality called Electric Field Encephalography (EFEG). We aim to establish the physical validity of the EFEG modality, and examine some of its properties and relative merits compared to EEG. Approach - We designed a wireless neuroelectric measurement device based on the Texas Instrument ADS1299 Analog Front End platform and a sensor montage, using custom electrodes, to simultaneously measure EFEG and spatially averaged EEG over a localized patch of the scalp (2cm x 2cm). The signal properties of each modality were compared across tests of noise floor, Berger effect, steady-state Visually Evoked Potential (ssVEP), signal-to-noise ratio (SNR), and others. In order to compare EFEG to EEG modalities in the frequency domain, we use a novel technique to compute spectral power densities and derive narrow-band SNR estimates for ssVEP signals. A simple binary choice brain-computer-interface (BCI) concept based on ssVEP is evaluated. Also, we present examples of high quality recording of transient Visually Evoked Potentials and Fields (tVEPF) that could be used for neurological studies. Main results - We demonstrate the capability of the NeuroDot system to record high quality EEG signals comparable to some recent clinical and research grade systems on the market. We show that the locally-referenced EFEG metric is resistant to certain types of movement artifacts. In some ssVEP based measurements, the EFEG modality shows promising results, demonstrating superior signal to noise ratios than the same recording processed as an analogous EEG signal. We show that by using EFEG based ssVEP SNR estimates to perform a binary classification in a model BCI, the optimal information transfer rate (ITR) can be raised from 15 to 30 bits per minute - though these preliminary results are likely sensitive to inter-subject variations and choice of scalp locations, so require further investigation. Significance - Enhancement of ssVEP SNR using EFEG has the potential to improve visually based BCIs and diagnostic paradigms. The time domain analysis of tVEPF signals shows robust features in the electric field components that might have clinical relevance beyond classical VEP approaches. . © 2018 IOP Publishing Ltd.

A statistically robust EEG re-referencing procedure to mitigate reference effect

PubMed Central

Lepage, Kyle Q.; Kramer, Mark A.; Chu, Catherine J.

2014-01-01

Background The electroencephalogram (EEG) remains the primary tool for diagnosis of abnormal brain activity in clinical neurology and for in vivo recordings of human neurophysiology in neuroscience research. In EEG data acquisition, voltage is measured at positions on the scalp with respect to a reference electrode. When this reference electrode responds to electrical activity or artifact all electrodes are affected. Successful analysis of EEG data often involves re-referencing procedures that modify the recorded traces and seek to minimize the impact of reference electrode activity upon functions of the original EEG recordings. New method We provide a novel, statistically robust procedure that adapts a robust maximum-likelihood type estimator to the problem of reference estimation, reduces the influence of neural activity from the re-referencing operation, and maintains good performance in a wide variety of empirical scenarios. Results The performance of the proposed and existing re-referencing procedures are validated in simulation and with examples of EEG recordings. To facilitate this comparison, channel-to-channel correlations are investigated theoretically and in simulation. Comparison with existing methods The proposed procedure avoids using data contaminated by neural signal and remains unbiased in recording scenarios where physical references, the common average reference (CAR) and the reference estimation standardization technique (REST) are not optimal. Conclusion The proposed procedure is simple, fast, and avoids the potential for substantial bias when analyzing low-density EEG data. PMID:24975291

A Pharmacokinetics-Neural Mass Model (PK-NMM) for the Simulation of EEG Activity during Propofol Anesthesia

PubMed Central

Liang, Zhenhu; Duan, Xuejing; Su, Cui; Voss, Logan; Sleigh, Jamie; Li, Xiaoli

2015-01-01

Modeling the effects of anesthetic drugs on brain activity is very helpful in understanding anesthesia mechanisms. The aim of this study was to set up a combined model to relate actual drug levels to EEG dynamics and behavioral states during propofol-induced anesthesia. We proposed a new combined theoretical model based on a pharmacokinetics (PK) model and a neural mass model (NMM), which we termed PK-NMM—with the aim of simulating electroencephalogram (EEG) activity during propofol-induced general anesthesia. The PK model was used to derive propofol effect-site drug concentrations (C eff) based on the actual drug infusion regimen. The NMM model took C eff as the control parameter to produce simulated EEG-like (sEEG) data. For comparison, we used real prefrontal EEG (rEEG) data of nine volunteers undergoing propofol anesthesia from a previous experiment. To see how well the sEEG could describe the dynamic changes of neural activity during anesthesia, the rEEG data and the sEEG data were compared with respect to: power-frequency plots; nonlinear exponent (permutation entropy (PE)); and bispectral SynchFastSlow (SFS) parameters. We found that the PK-NMM model was able to reproduce anesthesia EEG-like signals based on the estimated drug concentration and patients’ condition. The frequency spectrum indicated that the frequency power peak of the sEEG moved towards the low frequency band as anesthesia deepened. Different anesthetic states could be differentiated by the PE index. The correlation coefficient of PE was 0.80±0.13 (mean±standard deviation) between rEEG and sEEG for all subjects. Additionally, SFS could track the depth of anesthesia and the SFS of rEEG and sEEG were highly correlated with a correlation coefficient of 0.77±0.13. The PK-NMM model could simulate EEG activity and might be a useful tool for understanding the action of propofol on brain activity. PMID:26720495

Behavioural Inhibition System (BIS) sensitivity differentiates EEG theta responses during goal conflict in a continuous monitoring task.

PubMed

Moore, Roger A; Mills, Matthew; Marshman, Paul; Corr, Philip J

2012-08-01

Previous research has revealed that EEG theta oscillations are affected during goal conflict processing. This is consistent with the behavioural inhibition system (BIS) theory of anxiety (Gray & McNaughton, 2000). However, studies have not attempted to relate these BIS-related theta effects to BIS personality measures. Confirmation of such an association would provide further support for BIS theory, especially as it relates to trait differences. EEG was measured (32 electrodes) from extreme groups (low/high trait BIS) engaged in a target detection task. Goal conflicts were introduced throughout the task. Results show that the two groups did not differ in behavioural performance. The major EEG result was that a stepwise discriminant analysis indicated discrimination by 6 variables derived from coherence and power, with 5 of the 6 in the theta range as predicted by BIS theory and one in the beta range. Also, across the whole sample, EEG theta coherence increased at a variety of regions during primary goal conflict and showed a general increase during response execution; EEG theta power, in contrast, was primarily reactive to response execution. This is the first study to reveal a three-way relationship between the induction of goal conflict, the induction of theta power and coherence, and differentiation by psychometrically-defined low/high BIS status. Copyright © 2012 Elsevier B.V. All rights reserved.

Task complexity modulates pilot electroencephalographic activity during real flights.

PubMed

Di Stasi, Leandro L; Diaz-Piedra, Carolina; Suárez, Juan; McCamy, Michael B; Martinez-Conde, Susana; Roca-Dorda, Joaquín; Catena, Andrés

2015-07-01

Most research connecting task performance and neural activity to date has been conducted in laboratory conditions. Thus, field studies remain scarce, especially in extreme conditions such as during real flights. Here, we investigated the effects of flight procedures of varied complexity on the in-flight EEG activity of military helicopter pilots. Flight procedural complexity modulated the EEG power spectrum: highly demanding procedures (i.e., takeoff and landing) were associated with higher EEG power in the higher frequency bands, whereas less demanding procedures (i.e., flight exercises) were associated with lower EEG power over the same frequency bands. These results suggest that EEG recordings may help to evaluate an operator's cognitive performance in challenging real-life scenarios, and thus could aid in the prevention of catastrophic events. © 2015 Society for Psychophysiological Research.

Bristle-sensors—low-cost flexible passive dry EEG electrodes for neurofeedback and BCI applications

NASA Astrophysics Data System (ADS)

Grozea, Cristian; Voinescu, Catalin D.; Fazli, Siamac

2011-04-01

In this paper, we present a new, low-cost dry electrode for EEG that is made of flexible metal-coated polymer bristles. We examine various standard EEG paradigms, such as capturing occipital alpha rhythms, testing for event-related potentials in an auditory oddball paradigm and performing a sensory motor rhythm-based event-related (de-) synchronization paradigm to validate the performance of the novel electrodes in terms of signal quality. Our findings suggest that the dry electrodes that we developed result in high-quality EEG recordings and are thus suitable for a wide range of EEG studies and BCI applications. Furthermore, due to the flexibility of the novel electrodes, greater comfort is achieved in some subjects, this being essential for long-term use.

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.

Improving mental task classification by adding high frequency band information.

PubMed

Zhang, Li; He, Wei; He, Chuanhong; Wang, Ping

2010-02-01

Features extracted from delta, theta, alpha, beta and gamma bands spanning low frequency range are commonly used to classify scalp-recorded electroencephalogram (EEG) for designing brain-computer interface (BCI) and higher frequencies are often neglected as noise. In this paper, we implemented an experimental validation to demonstrate that high frequency components could provide helpful information for improving the performance of the mental task based BCI. Electromyography (EMG) and electrooculography (EOG) artifacts were removed by using blind source separation (BSS) techniques. Frequency band powers and asymmetry ratios from the high frequency band (40-100 Hz) together with those from the lower frequency bands were used to represent EEG features. Finally, Fisher discriminant analysis (FDA) combining with Mahalanobis distance were used as the classifier. In this study, four types of classifications were performed using EEG signals recorded from four subjects during five mental tasks. We obtained significantly higher classification accuracy by adding the high frequency band features compared to using the low frequency bands alone, which demonstrated that the information in high frequency components from scalp-recorded EEG is valuable for the mental task based BCI.

Comparison of Amplitude-Integrated EEG and Conventional EEG in a Cohort of Premature Infants.

PubMed

Meledin, Irina; Abu Tailakh, Muhammad; Gilat, Shlomo; Yogev, Hagai; Golan, Agneta; Novack, Victor; Shany, Eilon

2017-03-01

To compare amplitude-integrated EEG (aEEG) and conventional EEG (EEG) activity in premature neonates. Biweekly aEEG and EEG were simultaneously recorded in a cohort of infants born less than 34 weeks gestation. aEEG recordings were visually assessed for lower and upper border amplitude and bandwidth. EEG recordings were compressed for visual evaluation of continuity and assessed using a signal processing software for interburst intervals (IBI) and frequencies' amplitude. Ten-minute segments of aEEG and EEG indices were compared using regression analysis. A total of 189 recordings from 67 infants were made, from which 1697 aEEG/EEG pairs of 10-minute segments were assessed. Good concordance was found for visual assessment of continuity between the 2 methods. EEG IBI, alpha and theta frequencies' amplitudes were negatively correlated to the aEEG lower border while conceptional age (CA) was positively correlated to aEEG lower border ( P < .001). IBI and all frequencies' amplitude were positively correlated to the upper aEEG border ( P ≤ .001). CA was negatively correlated to aEEG span while IBI, alpha, beta, and theta frequencies' amplitude were positively correlated to the aEEG span. Important information is retained and integrated in the transformation of premature neonatal EEG to aEEG. aEEG recordings in high-risk premature neonates reflect reliably EEG background information related to continuity and amplitude.

Global field synchronization in gamma range of the sleep EEG tracks sleep depth: Artifact introduced by a rectangular analysis window.

PubMed

Rusterholz, Thomas; Achermann, Peter; Dürr, Roland; Koenig, Thomas; Tarokh, Leila

2017-06-01

Investigating functional connectivity between brain networks has become an area of interest in neuroscience. Several methods for investigating connectivity have recently been developed, however, these techniques need to be applied with care. We demonstrate that global field synchronization (GFS), a global measure of phase alignment in the EEG as a function of frequency, must be applied considering signal processing principles in order to yield valid results. Multichannel EEG (27 derivations) was analyzed for GFS based on the complex spectrum derived by the fast Fourier transform (FFT). We examined the effect of window functions on GFS, in particular of non-rectangular windows. Applying a rectangular window when calculating the FFT revealed high GFS values for high frequencies (>15Hz) that were highly correlated (r=0.9) with spectral power in the lower frequency range (0.75-4.5Hz) and tracked the depth of sleep. This turned out to be spurious synchronization. With a non-rectangular window (Tukey or Hanning window) these high frequency synchronization vanished. Both, GFS and power density spectra significantly differed for rectangular and non-rectangular windows. Previous papers using GFS typically did not specify the applied window and may have used a rectangular window function. However, the demonstrated impact of the window function raises the question of the validity of some previous findings at higher frequencies. We demonstrated that it is crucial to apply an appropriate window function for determining synchronization measures based on a spectral approach to avoid spurious synchronization in the beta/gamma range. Copyright © 2017 Elsevier B.V. All rights reserved.

Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness

NASA Astrophysics Data System (ADS)

Pichiorri, F.; De Vico Fallani, F.; Cincotti, F.; Babiloni, F.; Molinari, M.; Kleih, S. C.; Neuper, C.; Kübler, A.; Mattia, D.

2011-04-01

The main purpose of electroencephalography (EEG)-based brain-computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how sensorimotor rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with transcranial magnetic stimulation (TMS) and high-density EEG. Motor imagery (MI)-based BCI training in naïve participants led to a significant increase in motor cortical excitability, as revealed by post-training TMS mapping of the hand muscle's cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the functional brain networks constructed using a connectivity matrix between scalp electrodes revealed a significant decrease in the global efficiency index for the higher-beta frequency range (22-29 Hz), indicating that the brain network changes its topology with practice of hand grasping MI. Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.

Investigating dynamical information transfer in the brain following a TMS pulse: Insights from structural architecture.

PubMed

Amico, Enrico; Van Mierlo, Pieter; Marinazzo, Daniele; Laureys, Steven

2015-01-01

Transcranial magnetic stimulation (TMS) has been used for more than 20 years to investigate connectivity and plasticity in the human cortex. By combining TMS with high-density electroencephalography (hd-EEG), one can stimulate any cortical area and measure the effects produced by this perturbation in the rest of the cerebral cortex. The purpose of this paper is to investigate changes of information flow in the brain after TMS from a functional and structural perspective, using multimodal modeling of source reconstructed TMS/hd-EEG recordings and DTI tractography. We prove how brain dynamics induced by TMS is constrained and driven by its structure, at different spatial and temporal scales, especially when considering cross-frequency interactions. These results shed light on the function-structure organization of the brain network at the global level, and on the huge variety of information contained in it.

EFFECTIVE INDICES FOR MONITORING MENTAL WORKLOAD WHILE PERFORMING MULTIPLE TASKS.

PubMed

Hsu, Bin-Wei; Wang, Mao-Jiun J; Chen, Chi-Yuan; Chen, Fang

2015-08-01

This study identified several physiological indices that can accurately monitor mental workload while participants performed multiple tasks with the strategy of maintaining stable performance and maximizing accuracy. Thirty male participants completed three 10-min. simulated multitasks: MATB (Multi-Attribute Task Battery) with three workload levels. Twenty-five commonly used mental workload measures were collected, including heart rate, 12 HRV (heart rate variability), 10 EEG (electroencephalography) indices (α, β, θ, α/θ, θ/β from O1-O2 and F4-C4), and two subjective measures. Analyses of index sensitivity showed that two EEG indices, θ and α/θ (F4-C4), one time-domain HRV-SDNN (standard deviation of inter-beat intervals), and four frequency-domain HRV: VLF (very low frequency), LF (low frequency), %HF (percentage of high frequency), and LF/HF were sensitive to differentiate high workload. EEG α/θ (F4-C4) and LF/HF were most effective for monitoring high mental workload. LF/HF showed the highest correlations with other physiological indices. EEG α/θ (F4-C4) showed strong correlations with subjective measures across different mental workload levels. Operation strategy would affect the sensitivity of EEG α (F4-C4) and HF.

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.

Enhancing detection of steady-state visual evoked potentials using individual training data.

PubMed

Wang, Yijun; Nakanishi, Masaki; Wang, Yu-Te; Jung, Tzyy-Ping

2014-01-01

Although the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has improved gradually in the past decades, it still does not meet the requirement of a high communication speed in many applications. A major challenge is the interference of spontaneous background EEG activities in discriminating SSVEPs. An SSVEP BCI using frequency coding typically does not have a calibration procedure since the frequency of SSVEPs can be recognized by power spectrum density analysis (PSDA). However, the detection rate can be deteriorated by the spontaneous EEG activities within the same frequency range because phase information of SSVEPs is ignored in frequency detection. To address this problem, this study proposed to incorporate individual SSVEP training data into canonical correlation analysis (CCA) to improve the frequency detection of SSVEPs. An eight-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment was used for performance evaluation. Compared to the standard CCA method, the proposed method obtained significantly improved detection accuracy (95.2% vs. 88.4%, p<0.05) and information transfer rates (ITR) (104.6 bits/min vs. 89.1 bits/min, p<0.05). The results suggest that the employment of individual SSVEP training data can significantly improve the detection rate and thereby facilitate the implementation of a high-speed BCI.

Noninvasive Electrical Neuroimaging of the Human Brain during Mobile Tasks including Walking and Running

DTIC Science & Technology

2012-01-01

not trivial, and the increase is not without drawbacks . For high-density EEG systems, data processing can take a significant amount of time, even...existing wireless transmission systems. Given these drawbacks , a question naturally arises: how many electrodes are needed for MoBI? The answer will...state motor output. Neuroimage 36, 785-792. Kuo, A.D., Donelan, J.M., Ruina, A., 2005. Energetic consequences of walking like an inverted pendulum

EEG biometric identification: a thorough exploration of the time-frequency domain

NASA Astrophysics Data System (ADS)

DelPozo-Banos, Marcos; Travieso, Carlos M.; Weidemann, Christoph T.; Alonso, Jesús B.

2015-10-01

Objective. Although interest in using electroencephalogram (EEG) activity for subject identification has grown in recent years, the state of the art still lacks a comprehensive exploration of the discriminant information within it. This work aims to fill this gap, and in particular, it focuses on the time-frequency representation of the EEG. Approach. We executed qualitative and quantitative analyses of six publicly available data sets following a sequential experimentation approach. This approach was divided in three blocks analysing the configuration of the power spectrum density, the representation of the data and the properties of the discriminant information. A total of ten experiments were applied. Main results. Results show that EEG information below 40 Hz is unique enough to discriminate across subjects (a maximum of 100 subjects were evaluated here), regardless of the recorded cognitive task or the sensor location. Moreover, the discriminative power of rhythms follows a W-like shape between 1 and 40 Hz, with the central peak located at the posterior rhythm (around 10 Hz). This information is maximized with segments of around 2 s, and it proved to be moderately constant across montages and time. Significance. Therefore, we characterize how EEG activity differs across individuals and detail the optimal conditions to detect subject-specific information. This work helps to clarify the results of previous studies and to solve some unanswered questions. Ultimately, it will serve as guide for the design of future biometric systems.

Classification of independent components of EEG into multiple artifact classes.

PubMed

Frølich, Laura; Andersen, Tobias S; Mørup, Morten

2015-01-01

In this study, we aim to automatically identify multiple artifact types in EEG. We used multinomial regression to classify independent components of EEG data, selecting from 65 spatial, spectral, and temporal features of independent components using forward selection. The classifier identified neural and five nonneural types of components. Between subjects within studies, high classification performances were obtained. Between studies, however, classification was more difficult. For neural versus nonneural classifications, performance was on par with previous results obtained by others. We found that automatic separation of multiple artifact classes is possible with a small feature set. Our method can reduce manual workload and allow for the selective removal of artifact classes. Identifying artifacts during EEG recording may be used to instruct subjects to refrain from activity causing them. Copyright © 2014 Society for Psychophysiological Research.

Chaos-chaos transition of left hemisphere EEGs during standard tasks of Waterloo-Stanford Group Scale of hypnotic susceptibility.

PubMed

Yargholi, Elahe'; Nasrabadi, Ali Motie

2015-01-01

A recent study, recurrence quantification analysis of EEG signals during standard tasks of Waterloo-Stanford Group Scale of hypnotic susceptibility investigated recurrence quantifiers (RQs) of hypnotic electroencephalograph (EEG) signals recorded after hypnotic induction while subjects were doing standard tasks of Waterloo-Stanford Group Scale (WSGS) of hypnotic susceptibility to distinguish subjects of different hypnotizability levels. Following the same analysis, the current study determines the capability of different RQs to distinguish subjects of low, medium and high hypnotizability level and studies the influence of hypnotizability level on underlying dynamic of tasks. Besides, EEG channels were sorted according to the number of their RQs, which differed significantly among subjects of different hypnotizability levels. Another valuable result was determination of major brain regions in observing significant differences in various task types (ideomotors, hallucination, challenge and memory).

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.

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.

Brain Dynamics: Methodological Issues and Applications in Psychiatric and Neurologic Diseases

NASA Astrophysics Data System (ADS)

Pezard, Laurent

The human brain is a complex dynamical system generating the EEG signal. Numerical methods developed to study complex physical dynamics have been used to characterize EEG since the mid-eighties. This endeavor raised several issues related to the specificity of EEG. Firstly, theoretical and methodological studies should address the major differences between the dynamics of the human brain and physical systems. Secondly, this approach of EEG signal should prove to be relevant for dealing with physiological or clinical problems. A set of studies performed in our group is presented here within the context of these two problematic aspects. After the discussion of methodological drawbacks, we review numerical simulations related to the high dimension and spatial extension of brain dynamics. Experimental studies in neurologic and psychiatric disease are then presented. We conclude that if it is now clear that brain dynamics changes in relation with clinical situations, methodological problems remain largely unsolved.

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

Current source density analysis of the hippocampal theta rhythm: associated sustained potentials and candidate synaptic generators.

PubMed

Brankack, J; Stewart, M; Fox, S E

1993-07-02

Single-electrode depth profiles of the hippocampal EEG were made in urethane-anesthetized rats and rats trained in an alternating running/drinking task. Current source density (CSD) was computed from the voltage as a function of depth. A problem inherent to AC-coupled profiles was eliminated by incorporating sustained potential components of the EEG. 'AC' profiles force phasic current sinks to alternate with current sources at each lamina, changing the magnitude and even the sign of the computed membrane current. It was possible to include DC potentials in the profiles from anesthetized rats by using glass micropipettes for recording. A method of 'subtracting' profiles of the non-theta EEG from theta profiles was developed as an approach to including sustained potentials in recordings from freely-moving animals implanted with platinum electrodes. 'DC' profiles are superior to 'AC' profiles for analysis of EEG activity because 'DC'-CSD values can be considered correct in sign and more closely represent the actual membrane current magnitudes. Since hippocampal inputs are laminated, CSD analysis leads to straightforward predictions of the afferents involved. Theta-related activity in afferents from entorhinal neurons, hippocampal interneurons and ipsi- and contralateral hippocampal pyramids all appear to contribute to sources and sinks in CA1 and the dentate area. The largest theta-related generator was a sink at the fissure, having both phasic and tonic components. This sink may reflect activity in afferents from the lateral entorhinal cortex. The phase of the dentate mid-molecular sink suggests that medial entorhinal afferents drive the theta-related granule and pyramidal cell firing. The sustained components may be simply due to different average rates of firing during theta rhythm than during non-theta EEG in afferents whose firing rates are also phasically modulated.

Sensor Level Functional Connectivity Topography Comparison Between Different References Based EEG and MEG.

PubMed

Huang, Yunzhi; Zhang, Junpeng; Cui, Yuan; Yang, Gang; Liu, Qi; Yin, Guangfu

2018-01-01

Sensor-level functional connectivity topography (sFCT) contributes significantly to our understanding of brain networks. sFCT can be constructed using either electroencephalography (EEG) or magnetoencephalography (MEG). Here, we compared sFCT within the EEG modality and between EEG and MEG modalities. We first used simulations to look at how different EEG references-including the Reference Electrode Standardization Technique (REST), average reference (AR), linked mastoids (LM), and left mastoid references (LR)-affect EEG-based sFCT. The results showed that REST decreased the reference effects on scalp EEG recordings, making REST-based sFCT closer to the ground truth (sFCT based on ideal recordings). For the inter-modality simulation comparisons, we compared each type of EEG-sFCT with MEG-sFCT using three metrics to quantize the differences: Relative Error (RE), Overlap Rate (OR), and Hamming Distance (HD). When two sFCTs are similar, RE and HD are low, while OR is high. Results showed that among all reference schemes, EEG-and MEG-sFCT were most similar when the EEG was REST-based and the EEG and MEG were recorded simultaneously. Next, we analyzed simultaneously recorded MEG and EEG data from publicly available face-recognition experiments using a similar procedure as in the simulations. The results showed (1) if MEG-sFCT is the standard, REST-and LM-based sFCT provided results closer to this standard in the terms of HD; (2) REST-based sFCT and MEG-sFCT had the highest similarity in terms of RE; (3) REST-based sFCT had the most overlapping edges with MEG-sFCT in terms of OR. This study thus provides new insights into the effect of different reference schemes on sFCT and the similarity between MEG and EEG in terms of sFCT.

Automated EEG entropy measurements in coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state

PubMed Central

Gosseries, Olivia; Schnakers, Caroline; Ledoux, Didier; Vanhaudenhuyse, Audrey; Bruno, Marie-Aurélie; Demertzi, Athéna; Noirhomme, Quentin; Lehembre, Rémy; Damas, Pierre; Goldman, Serge; Peeters, Erika; Moonen, Gustave; Laureys, Steven

Summary Monitoring the level of consciousness in brain-injured patients with disorders of consciousness is crucial as it provides diagnostic and prognostic information. Behavioral assessment remains the gold standard for assessing consciousness but previous studies have shown a high rate of misdiagnosis. This study aimed to investigate the usefulness of electroencephalography (EEG) entropy measurements in differentiating unconscious (coma or vegetative) from minimally conscious patients. Left fronto-temporal EEG recordings (10-minute resting state epochs) were prospectively obtained in 56 patients and 16 age-matched healthy volunteers. Patients were assessed in the acute (≤1 month post-injury; n=29) or chronic (>1 month post-injury; n=27) stage. The etiology was traumatic in 23 patients. Automated online EEG entropy calculations (providing an arbitrary value ranging from 0 to 91) were compared with behavioral assessments (Coma Recovery Scale-Revised) and outcome. EEG entropy correlated with Coma Recovery Scale total scores (r=0.49). Mean EEG entropy values were higher in minimally conscious (73±19; mean and standard deviation) than in vegetative/unresponsive wakefulness syndrome patients (45±28). Receiver operating characteristic analysis revealed an entropy cut-off value of 52 differentiating acute unconscious from minimally conscious patients (sensitivity 89% and specificity 90%). In chronic patients, entropy measurements offered no reliable diagnostic information. EEG entropy measurements did not allow prediction of outcome. User-independent time-frequency balanced spectral EEG entropy measurements seem to constitute an interesting diagnostic – albeit not prognostic – tool for assessing neural network complexity in disorders of consciousness in the acute setting. Future studies are needed before using this tool in routine clinical practice, and these should seek to improve automated EEG quantification paradigms in order to reduce the remaining false negative and false positive findings. PMID:21693085

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.

EEG-fMRI Bayesian framework for neural activity estimation: a simulation study

NASA Astrophysics Data System (ADS)

Croce, Pierpaolo; Basti, Alessio; Marzetti, Laura; Zappasodi, Filippo; Del Gratta, Cosimo

2016-12-01

Objective. Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. Approach. We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF). Main results. First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements. Significance. The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics.

EEG-fMRI Bayesian framework for neural activity estimation: a simulation study.

PubMed

Croce, Pierpaolo; Basti, Alessio; Marzetti, Laura; Zappasodi, Filippo; Gratta, Cosimo Del

2016-12-01

Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF). First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements. The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics.

EEG and ERP profiles in the high alcohol preferring (HAP) and low alcohol preferring (LAP) mice: relationship to ethanol preference.

PubMed

Slawecki, Craig J; Grahame, Nicholas J; Roth, Jennifer; Katner, Simon N; Ehlers, C L

2003-01-31

Neurophysiological measures, such as decreased P300 amplitude and altered EEG alpha activity, have been associated with increased alcoholism risk. The purpose of the present study was to extend the assessment of the neurophysiological indices associated with alcohol consumption to a recently developed mouse model of high ethanol consumption, the first replicate line of high alcohol preferring (HAP-1) and low alcohol preferring (LAP-1) mice. Male HAP-1, LAP-1, and HS mice from the Institute for Behavioral Genetics at the University of Colorado Health Science Center (i.e., HS/Ibg mice) were implanted with cortical electrodes. EEG activity, and event related potentials (ERPs) were then examined. Following electrophysiological assessment, ethanol preference was assessed to examine the relationship between neurophysiological indices and ethanol consumption. EEG analyses revealed that HAPs and HS/Ibgs had greater peak frequency in the 2-4-Hz band and lower peak frequency in the 6-8- and 1-50-Hz bands of the cortical EEG compared to LAPs. Compared to HAPs, LAPs and HS/Ibgs had decreased peak EEG frequency in the 8-16-Hz band. Decreased parietal cortical power from 8 to 50 Hz was associated with high initial ethanol preference in HAP mice. In regards to ERPs, P1 amplitude was greater in HAPs compared to both LAPs and HS/Ibgs and the P3 latency in LAPs was decreased compared to both HAPs and HS/Ibgs. As expected, HAPs consumed more ethanol and had higher ethanol preference than LAPs and HS/Ibgs. There were no significant differences in ethanol intake or preference between HS/Ibgs and LAPs. These data indicate that selective breeding of the HAP and LAP lines has resulted in the divergence of EEG and ERP phenotypes. The differences observed suggest that increased cortical P1 amplitude and altered cortical EEG activity in the 8-50-Hz frequency range may be neurophysiological 'risk factors' associated with high ethanol consumption in mice. Decreased P3 latency in LAPs compared to HAPs and HS/Ibgs mice may be a 'protective factor'.

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.

Iron Deficiency (ID) at Both Birth and 9 Months Predicts Right Frontal EEG Asymmetry in Infancy

PubMed Central

Armony-Sivan, Rinat; Zhu, Bingquan; Clark, Katy M.; Richards, Blair; Ji, Chai; Kaciroti, Niko; Shao, Jie

2016-01-01

This study considered effects of timing and duration of iron deficiency (ID) on frontal EEG asymmetry in infancy. In healthy term Chinese infants, EEG was recorded at 9 months in three experimental conditions: baseline, peek-a-boo, and stranger approach. Eighty infants provided data for all conditions. Prenatal ID was defined as low cord ferritin or high ZPP/H. Postnatal ID was defined as ≥ two abnormal iron measures at 9 months. Study groups were pre- and postnatal ID, prenatal ID only, postnatal ID only, and not ID. GLM repeated measure analysis showed a main effect for iron group. The pre- and postnatal ID group had negative asymmetry scores, reflecting right frontal EEG asymmetry (mean ±SE: −.18 ±.07) versus prenatal ID only (.00 ±.04), postnatal ID only (.03 ±.04), and not ID (.02 ±.04). Thus, ID at both birth and 9 months was associated with right frontal EEG asymmetry, a neural correlate of behavioral withdrawal and negative emotions. PMID:26668100

On analysis of electroencephalogram by multiresolution-based energetic approach

NASA Astrophysics Data System (ADS)

Sevindir, Hulya Kodal; Yazici, Cuneyt; Siddiqi, A. H.; Aslan, Zafer

2013-10-01

Epilepsy is a common brain disorder where the normal neuronal activity gets affected. Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. The main application of EEG is in the case of epilepsy. On a standard EEG some abnormalities indicate epileptic activity. EEG signals like many biomedical signals are highly non-stationary by their nature. For the investigation of biomedical signals, in particular EEG signals, wavelet analysis have found prominent position in the study for their ability to analyze such signals. Wavelet transform is capable of separating the signal energy among different frequency scales and a good compromise between temporal and frequency resolution is obtained. The present study is an attempt for better understanding of the mechanism causing the epileptic disorder and accurate prediction of occurrence of seizures. In the present paper following Magosso's work [12], we identify typical patterns of energy redistribution before and during the seizure using multiresolution wavelet analysis on Kocaeli University's Medical School's data.

Delayed Early Primary Visual Pathway Development in Premature Infants: High Density Electrophysiological Evidence

PubMed Central

Tremblay, Emmanuel; Vannasing, Phetsamone; Roy, Marie-Sylvie; Lefebvre, Francine; Kombate, Damelan; Lassonde, Maryse; Lepore, Franco; McKerral, Michelle; Gallagher, Anne

2014-01-01

In the past decades, multiple studies have been interested in developmental patterns of the visual system in healthy infants. During the first year of life, differential maturational changes have been observed between the Magnocellular (P) and the Parvocellular (P) visual pathways. However, few studies investigated P and M system development in infants born prematurely. The aim of the present study was to characterize P and M system maturational differences between healthy preterm and fullterm infants through a critical period of visual maturation: the first year of life. Using a cross-sectional design, high-density electroencephalogram (EEG) was recorded in 31 healthy preterms and 41 fullterm infants of 3, 6, or 12 months (corrected age for premature babies). Three visual stimulations varying in contrast and spatial frequency were presented to stimulate preferentially the M pathway, the P pathway, or both systems simultaneously during EEG recordings. Results from early visual evoked potentials in response to the stimulation that activates simultaneously both systems revealed longer N1 latencies and smaller P1 amplitudes in preterm infants compared to fullterms. Moreover, preterms showed longer N1 and P1 latencies in response to stimuli assessing the M pathway at 3 months. No differences between preterms and fullterms were found when using the preferential P system stimulation. In order to identify the cerebral generator of each visual response, distributed source analyses were computed in 12-month-old infants using LORETA. Source analysis demonstrated an activation of the parietal dorsal region in fullterm infants, in response to the preferential M pathway, which was not seen in the preterms. Overall, these findings suggest that the Magnocellular pathway development is affected in premature infants. Although our VEP results suggest that premature children overcome, at least partially, the visual developmental delay with time, source analyses reveal abnormal brain activation of the Magnocellular pathway at 12 months of age. PMID:25268226

Resected Brain Tissue, Seizure Onset Zone and Quantitative EEG Measures: Towards Prediction of Post-Surgical Seizure Control

PubMed Central

Andrzejak, Ralph G.; Hauf, Martinus; Pollo, Claudio; Müller, Markus; Weisstanner, Christian; Wiest, Roland; Schindler, Kaspar

2015-01-01

Background Epilepsy surgery is a potentially curative treatment option for pharmacoresistent patients. If non-invasive methods alone do not allow to delineate the epileptogenic brain areas the surgical candidates undergo long-term monitoring with intracranial EEG. Visual EEG analysis is then used to identify the seizure onset zone for targeted resection as a standard procedure. Methods Despite of its great potential to assess the epileptogenicty of brain tissue, quantitative EEG analysis has not yet found its way into routine clinical practice. To demonstrate that quantitative EEG may yield clinically highly relevant information we retrospectively investigated how post-operative seizure control is associated with four selected EEG measures evaluated in the resected brain tissue and the seizure onset zone. Importantly, the exact spatial location of the intracranial electrodes was determined by coregistration of pre-operative MRI and post-implantation CT and coregistration with post-resection MRI was used to delineate the extent of tissue resection. Using data-driven thresholding, quantitative EEG results were separated into normally contributing and salient channels. Results In patients with favorable post-surgical seizure control a significantly larger fraction of salient channels in three of the four quantitative EEG measures was resected than in patients with unfavorable outcome in terms of seizure control (median over the whole peri-ictal recordings). The same statistics revealed no association with post-operative seizure control when EEG channels contributing to the seizure onset zone were studied. Conclusions We conclude that quantitative EEG measures provide clinically relevant and objective markers of target tissue, which may be used to optimize epilepsy surgery. The finding that differentiation between favorable and unfavorable outcome was better for the fraction of salient values in the resected brain tissue than in the seizure onset zone is consistent with growing evidence that spatially extended networks might be more relevant for seizure generation, evolution and termination than a single highly localized brain region (i.e. a “focus”) where seizures start. PMID:26513359

Exploring the time-frequency content of high frequency oscillations for automated identification of seizure onset zone in epilepsy.

PubMed

Liu, Su; Sha, Zhiyi; Sencer, Altay; Aydoseli, Aydin; Bebek, Nerse; Abosch, Aviva; Henry, Thomas; Gurses, Candan; Ince, Nuri Firat

2016-04-01

High frequency oscillations (HFOs) in intracranial electroencephalography (iEEG) recordings are considered as promising clinical biomarkers of epileptogenic regions in the brain. The aim of this study is to improve and automatize the detection of HFOs by exploring the time-frequency content of iEEG and to investigate the seizure onset zone (SOZ) detection accuracy during the sleep, awake and pre-ictal states in patients with epilepsy, for the purpose of assisting the localization of SOZ in clinical practice. Ten-minute iEEG segments were defined during different states in eight patients with refractory epilepsy. A three-stage algorithm was implemented to detect HFOs in these segments. First, an amplitude based initial detection threshold was used to generate a large pool of HFO candidates. Then distinguishing features were extracted from the time and time-frequency domain of the raw iEEG and used with a Gaussian mixture model clustering to isolate HFO events from other activities. The spatial distribution of HFO clusters was correlated with the seizure onset channels identified by neurologists in seven patient with good surgical outcome. The overlapping rates of localized channels and seizure onset locations were high in all states. The best result was obtained using the iEEG data during sleep, achieving a sensitivity of 81%, and a specificity of 96%. The channels with maximum number of HFOs identified epileptogenic areas where the seizures occurred more frequently. The current study was conducted using iEEG data collected in realistic clinical conditions without channel pre-exclusion. HFOs were investigated with novel features extracted from the entire frequency band, and were correlated with SOZ in different states. The results indicate that automatic HFO detection with unsupervised clustering methods exploring the time-frequency content of raw iEEG can be efficiently used to identify the epileptogenic zone with an accurate and efficient manner.

Resection of ictal high-frequency oscillations leads to favorable surgical outcome in pediatric epilepsy

PubMed Central

Fujiwara, Hisako; Greiner, Hansel M.; Lee, Ki Hyeong; Holland-Bouley, Katherine D.; Seo, Joo Hee; Arthur, Todd; Mangano, Francesco T.; Leach, James L.; Rose, Douglas F.

2012-01-01

Summary Purpose Intracranial electroencephalography (EEG) is performed as part of an epilepsy surgery evaluation when noninvasive tests are incongruent or the putative seizure-onset zone is near eloquent cortex. Determining the seizure-onset zone using intracranial EEG has been conventionally based on identification of specific ictal patterns with visual inspection. High-frequency oscillations (HFOs, >80 Hz) have been recognized recently as highly correlated with the epileptogenic zone. However, HFOs can be difficult to detect because of their low amplitude. Therefore, the prevalence of ictal HFOs and their role in localization of epileptogenic zone on intracranial EEG are unknown. Methods We identified 48 patients who underwent surgical treatment after the surgical evaluation with intracranial EEG, and 44 patients met criteria for this retrospective study. Results were not used in surgical decision making. Intracranial EEG recordings were collected with a sampling rate of 2,000 Hz. Recordings were first inspected visually to determine ictal onset and then analyzed further with time-frequency analysis. Forty-one (93%) of 44 patients had ictal HFOs determined with time-frequency analysis of intracranial EEG. Key Findings Twenty-two (54%) of the 41 patients with ictal HFOs had complete resection of HFO regions, regardless of frequency bands. Complete resection of HFOs (n = 22) resulted in a seizure-free outcome in 18 (82%) of 22 patients, significantly higher than the seizure-free outcome with incomplete HFO resection (4/19, 21%). Significance Our study shows that ictal HFOs are commonly found with intracranial EEG in our population largely of children with cortical dysplasia, and have localizing value. The use of ictal HFOs may add more promising information compared to interictal HFOs because of the evidence of ictal propagation and followed by clinical aspect of seizures. Complete resection of HFOs is a favorable prognostic indicator for surgical outcome. PMID:22905734

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.

Simultaneous scalp electroencephalography (EEG), electromyography (EMG), and whole-body segmental inertial recording for multi-modal neural decoding.

PubMed

Bulea, Thomas C; Kilicarslan, Atilla; Ozdemir, Recep; Paloski, William H; Contreras-Vidal, Jose L

2013-07-26

Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG.

Reproducibility of the spectral components of the electroencephalogram during driver fatigue.

PubMed

Lal, Saroj K L; Craig, Ashley

2005-02-01

To date, no study has tested the reproducibility of EEG changes that occur during driver fatigue. For the EEG changes to be useful in the development of a fatigue countermeasure device the EEG response during each onset period of fatigue in individuals needs to be reproducible. It should be noted that fatigue during driving is not a continuous process but consists of successive episodes of 'microsleeps' where the subject may go in and out of a fatigue state. The aim of the present study was to investigate the reproducibility of fatigue during driving in both professional and non-professional drivers. Thirty five non-professional drivers and twenty professional drivers were tested during two separate sessions of a driver simulator task. EEG, EOG and behavioural measurements of fatigue were obtained during the driving task. The results showed high reproducibility for the delta and theta bands (r>0.95) in both groups of drivers. The results are discussed in light of implications for future studies and for the development of an EEG based fatigue countermeasure device.

Cannabinoid antagonist SLV326 induces convulsive seizures and changes in the interictal EEG in rats

PubMed Central

de Bruin, Natasja; Heijink, Liesbeth; Kruse, Chris; Vinogradova, Lyudmila; Lüttjohann, Annika; van Luijtelaar, Gilles; van Rijn, Clementina M.

2017-01-01

Cannabinoid CB1 antagonists have been investigated for possible treatment of e.g. obesity-related disorders. However, clinical application was halted due to their symptoms of anxiety and depression. In addition to these adverse effects, we have shown earlier that chronic treatment with the CB1 antagonist rimonabant may induce EEG-confirmed convulsive seizures. In a regulatory repeat-dose toxicity study violent episodes of “muscle spasms” were observed in Wistar rats, daily dosed with the CB1 receptor antagonist SLV326 during 5 months. The aim of the present follow-up study was to investigate whether these violent movements were of an epileptic origin. In selected SLV326-treated and control animals, EEG and behavior were monitored for 24 hours. 25% of SLV326 treated animals showed 1 to 21 EEG-confirmed generalized convulsive seizures, whereas controls were seizure-free. The behavioral seizures were typical for a limbic origin. Moreover, interictal spikes were found in 38% of treated animals. The frequency spectrum of the interictal EEG of the treated rats showed a lower theta peak frequency, as well as lower gamma power compared to the controls. These frequency changes were state-dependent: they were only found during high locomotor activity. It is concluded that long term blockade of the endogenous cannabinoid system can provoke limbic seizures in otherwise healthy rats. Additionally, SLV326 alters the frequency spectrum of the EEG when rats are highly active, suggesting effects on complex behavior and cognition. PMID:28151935

Amplitude Integrated Electroencephalography Compared With Conventional Video EEG for Neonatal Seizure Detection: A Diagnostic Accuracy Study.

PubMed

Rakshasbhuvankar, Abhijeet; Rao, Shripada; Palumbo, Linda; Ghosh, Soumya; Nagarajan, Lakshmi

2017-08-01

This diagnostic accuracy study compared the accuracy of seizure detection by amplitude-integrated electroencephalography with the criterion standard conventional video EEG in term and near-term infants at risk of seizures. Simultaneous recording of amplitude-integrated EEG (2-channel amplitude-integrated EEG with raw trace) and video EEG was done for 24 hours for each infant. Amplitude-integrated EEG was interpreted by a neonatologist; video EEG was interpreted by a neurologist independently. Thirty-five infants were included in the analysis. In the 7 infants with seizures on video EEG, there were 169 seizure episodes on video EEG, of which only 57 were identified by amplitude-integrated EEG. Amplitude-integrated EEG had a sensitivity of 33.7% for individual seizure detection. Amplitude-integrated EEG had an 86% sensitivity for detection of babies with seizures; however, it was nonspecific, in that 50% of infants with seizures detected by amplitude-integrated EEG did not have true seizures by video EEG. In conclusion, our study suggests that amplitude-integrated EEG is a poor screening tool for neonatal seizures.

Characterization of EEG signals revealing covert cognition in the injured brain.

PubMed

Curley, William H; Forgacs, Peter B; Voss, Henning U; Conte, Mary M; Schiff, Nicholas D

2018-05-01

See Boly and Laureys (doi:10.1093/brain/awy080) for a scientific commentary on this article.Patients with severe brain injury are difficult to assess and frequently subject to misdiagnosis. 'Cognitive motor dissociation' is a term used to describe a subset of such patients with preserved cognition as detected with neuroimaging methods but not evident in behavioural assessments. Unlike the locked-in state, cognitive motor dissociation after severe brain injury is prominently marked by concomitant injuries across the cerebrum in addition to limited or no motoric function. In the present study, we sought to characterize the EEG signals used as indicators of cognition in patients with disorders of consciousness and examine their reliability for potential future use to re-establish communication. We compared EEG-based assessments to the results of using similar methods with functional MRI. Using power spectral density analysis to detect EEG evidence of task performance (Two Group Test, P ≤ 0.05, with false discovery rate correction), we found evidence of the capacity to follow commands in 21 of 28 patients with severe brain injury and all 15 healthy individuals studied. We found substantial variability in the temporal and spatial characteristics of significant EEG signals among the patients in contrast to only modest variation in these domains across healthy controls; the majority of healthy controls showed suppression of either 8-12 Hz 'alpha' or 13-40 Hz 'beta' power during task performance, or both. Nine of the 21 patients with EEG evidence of command-following also demonstrated functional MRI evidence of command-following. Nine of the patients with command-following capacity demonstrated by EEG showed no behavioural evidence of a communication channel as detected by a standardized behavioural assessment, the Coma Recovery Scale - Revised. We further examined the potential contributions of fluctuations in arousal that appeared to co-vary with some patients' ability to reliably generate EEG signals in response to command. Five of nine patients with statistically indeterminate responses to one task tested showed a positive response after accounting for variations in overall background state (as visualized in the qualitative shape of the power spectrum) and grouping of trial runs with similar background state characteristics. Our findings reveal signal variations of EEG responses in patients with severe brain injuries and provide insight into the underlying physiology of cognitive motor dissociation. These results can help guide future efforts aimed at re-establishment of communication in such patients who will need customization for brain-computer interfaces.

Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis.

PubMed

Somers, Ben; Bertrand, Alexander

2016-12-01

Chronic, 24/7 EEG monitoring requires the use of highly miniaturized EEG modules, which only measure a few EEG channels over a small area. For improved spatial coverage, a wireless EEG sensor network (WESN) can be deployed, consisting of multiple EEG modules, which interact through short-distance wireless communication. In this paper, we aim to remove eye blink artifacts in each EEG channel of a WESN by optimally exploiting the correlation between EEG signals from different modules, under stringent communication bandwidth constraints. We apply a distributed canonical correlation analysis (CCA-)based algorithm, in which each module only transmits an optimal linear combination of its local EEG channels to the other modules. The method is validated on both synthetic and real EEG data sets, with emulated wireless transmissions. While strongly reducing the amount of data that is shared between nodes, we demonstrate that the algorithm achieves the same eye blink artifact removal performance as the equivalent centralized CCA algorithm, which is at least as good as other state-of-the-art multi-channel algorithms that require a transmission of all channels. Due to their potential for extreme miniaturization, WESNs are viewed as an enabling technology for chronic EEG monitoring. However, multi-channel analysis is hampered in WESNs due to the high energy cost for wireless communication. This paper shows that multi-channel eye blink artifact removal is possible with a significantly reduced wireless communication between EEG modules.

Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis

NASA Astrophysics Data System (ADS)

Somers, Ben; Bertrand, Alexander

2016-12-01

Objective. Chronic, 24/7 EEG monitoring requires the use of highly miniaturized EEG modules, which only measure a few EEG channels over a small area. For improved spatial coverage, a wireless EEG sensor network (WESN) can be deployed, consisting of multiple EEG modules, which interact through short-distance wireless communication. In this paper, we aim to remove eye blink artifacts in each EEG channel of a WESN by optimally exploiting the correlation between EEG signals from different modules, under stringent communication bandwidth constraints. Approach. We apply a distributed canonical correlation analysis (CCA-)based algorithm, in which each module only transmits an optimal linear combination of its local EEG channels to the other modules. The method is validated on both synthetic and real EEG data sets, with emulated wireless transmissions. Main results. While strongly reducing the amount of data that is shared between nodes, we demonstrate that the algorithm achieves the same eye blink artifact removal performance as the equivalent centralized CCA algorithm, which is at least as good as other state-of-the-art multi-channel algorithms that require a transmission of all channels. Significance. Due to their potential for extreme miniaturization, WESNs are viewed as an enabling technology for chronic EEG monitoring. However, multi-channel analysis is hampered in WESNs due to the high energy cost for wireless communication. This paper shows that multi-channel eye blink artifact removal is possible with a significantly reduced wireless communication between EEG modules.

EEG sleep in Cushing's disease and Cushing's syndrome: comparison with patients with major depressive disorder.

PubMed

Shipley, J E; Schteingart, D E; Tandon, R; Pande, A C; Grunhaus, L; Haskett, R F; Starkman, M N

1992-07-15

Because patients with Cushing' syndrome (CS) and Major depressive disorder (MDD) share features of hypercortisolism and the depressive syndrome, we compared electro-encephalographic (EEG) sleep in patients with pituitary-ACTH-dependent Cushing's syndrome (Cushing's disease, CD), patients with ACTH-independent Cushing's syndrome (AICS), patients with major depressive disorder (MDD), and normal subjects. There were substantial similarities in the abnormal polysomnography profiles of patients with CD, AICS, and MDD. All three patient groups demonstrated poorer sleep continuity, shortened rapid eye movement (REM) latency, and increased first REM period density compared with normal subjects. In addition, AICS patients and MDD patients had elevated REM activity and density. These findings are discussed in terms of models of pathophysiology that relate abnormalities in sleep, mood, and hypothalamic-pituitary-adrenal function.

Holistic approach for automated background EEG assessment in asphyxiated full-term infants

NASA Astrophysics Data System (ADS)

Matic, Vladimir; Cherian, Perumpillichira J.; Koolen, Ninah; Naulaers, Gunnar; Swarte, Renate M.; Govaert, Paul; Van Huffel, Sabine; De Vos, Maarten

2014-12-01

Objective. To develop an automated algorithm to quantify background EEG abnormalities in full-term neonates with hypoxic ischemic encephalopathy. Approach. The algorithm classifies 1 h of continuous neonatal EEG (cEEG) into a mild, moderate or severe background abnormality grade. These classes are well established in the literature and a clinical neurophysiologist labeled 272 1 h cEEG epochs selected from 34 neonates. The algorithm is based on adaptive EEG segmentation and mapping of the segments into the so-called segments’ feature space. Three features are suggested and further processing is obtained using a discretized three-dimensional distribution of the segments’ features represented as a 3-way data tensor. Further classification has been achieved using recently developed tensor decomposition/classification methods that reduce the size of the model and extract a significant and discriminative set of features. Main results. Effective parameterization of cEEG data has been achieved resulting in high classification accuracy (89%) to grade background EEG abnormalities. Significance. For the first time, the algorithm for the background EEG assessment has been validated on an extensive dataset which contained major artifacts and epileptic seizures. The demonstrated high robustness, while processing real-case EEGs, suggests that the algorithm can be used as an assistive tool to monitor the severity of hypoxic insults in newborns.

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

Graph theory in brain-to-brain connectivity: A simulation study and an application to an EEG hyperscanning experiment.

PubMed

Toppi, J; Ciaramidaro, A; Vogel, P; Mattia, D; Babiloni, F; Siniatchkin, M; Astolfi, L

2015-08-01

Hyperscanning consists in the simultaneous recording of hemodynamic or neuroelectrical signals from two or more subjects acting in a social context. Well-established methodologies for connectivity estimation have already been adapted to hyperscanning purposes. The extension of graph theory approach to multi-subjects case is still a challenging issue. In the present work we aim to test the ability of the currently used graph theory global indices in describing the properties of a network given by two interacting subjects. The testing was conducted first on surrogate brain-to-brain networks reproducing typical social scenarios and then on real EEG hyperscanning data recorded during a Joint Action task. The results of the simulation study highlighted the ability of all the investigated indexes in modulating their values according to the level of interaction between subjects. However, only global efficiency and path length indexes demonstrated to be sensitive to an asymmetry in the communication between the two subjects. Such results were, then, confirmed by the application on real EEG data. Global efficiency modulated, in fact, their values according to the inter-brain density, assuming higher values in the social condition with respect to the non-social condition.

EEG-distributed inverse solutions for a spherical head model

NASA Astrophysics Data System (ADS)

Riera, J. J.; Fuentes, M. E.; Valdés, P. A.; Ohárriz, Y.

1998-08-01

The theoretical study of the minimum norm solution to the MEG inverse problem has been carried out in previous papers for the particular case of spherical symmetry. However, a similar study for the EEG is remarkably more difficult due to the very complicated nature of the expression relating the voltage differences on the scalp to the primary current density (PCD) even for this simple symmetry. This paper introduces the use of the electric lead field (ELF) on the dyadic formalism in the spherical coordinate system to overcome such a drawback using an expansion of the ELF in terms of longitudinal and orthogonal vector fields. This approach allows us to represent EEG Fourier coefficients on a 2-sphere in terms of a current multipole expansion. The choice of a suitable basis for the Hilbert space of the PCDs on the brain region allows the current multipole moments to be related by spatial transfer functions to the PCD spectral coefficients. Properties of the most used distributed inverse solutions are explored on the basis of these results. Also, a part of the ELF null space is completely characterized and those spherical components of the PCD which are possible silent candidates are discussed.

Filtration of human EEG recordings from physiological artifacts with empirical mode method

NASA Astrophysics Data System (ADS)

Grubov, Vadim V.; Runnova, Anastasiya E.; Khramova, Marina V.

2017-03-01

In the paper we propose the new method for dealing with noise and physiological artifacts in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We consider noises and physiological artifacts on EEG as specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from eye-moving artifacts and show high efficiency of the method.

QEEG Spectral and Coherence Assessment of Autistic Children in Three Different Experimental Conditions

ERIC Educational Resources Information Center

Machado, Calixto; Estévez, Mario; Leisman, Gerry; Melillo, Robert; Rodríguez, Rafael; DeFina, Phillip; Hernández, Adrián; Pérez-Nellar, Jesús; Naranjo, Rolando; Chinchilla, Mauricio; Garófalo, Nicolás; Vargas, José; Beltrán, Carlos

2015-01-01

We studied autistics by quantitative EEG spectral and coherence analysis during three experimental conditions: basal, watching a cartoon with audio (V-A), and with muted audio band (VwA). Significant reductions were found for the absolute power spectral density (PSD) in the central region for delta and theta, and in the posterior region for sigma…

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.

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.

Early Oxygen-Utilization and Brain Activity in Preterm Infants

PubMed Central

de Vries, Linda S.; Groenendaal, Floris; Toet, Mona C.; Lemmers, Petra M. A.; Vosse van de, Renè E.; van Bel, Frank; Benders, Manon J. N. L.

2015-01-01

The combined monitoring of oxygen supply and delivery using Near-InfraRed spectroscopy (NIRS) and cerebral activity using amplitude-integrated EEG (aEEG) could yield new insights into brain metabolism and detect potentially vulnerable conditions soon after birth. The relationship between NIRS and quantitative aEEG/EEG parameters has not yet been investigated. Our aim was to study the association between oxygen utilization during the first 6 h after birth and simultaneously continuously monitored brain activity measured by aEEG/EEG. Forty-four hemodynamically stable babies with a GA < 28 weeks, with good quality NIRS and aEEG/EEG data available and who did not receive morphine were included in the study. aEEG and NIRS monitoring started at NICU admission. The relation between regional cerebral oxygen saturation (rScO2) and cerebral fractional tissue oxygen extraction (cFTOE), and quantitative measurements of brain activity such as number of spontaneous activity transients (SAT) per minute (SAT rate), the interval in seconds (i.e. time) between SATs (ISI) and the minimum amplitude of the EEG in μV (min aEEG) were evaluated. rScO2 was negatively associated with SAT rate (β=-3.45 [CI=-5.76- -1.15], p=0.004) and positively associated with ISI (β=1.45 [CI=0.44-2.45], p=0.006). cFTOE was positively associated with SAT rate (β=0.034 [CI=0.009-0.059], p=0.008) and negatively associated with ISI (β=-0.015 [CI=-0.026- -0.004], p=0.007). Oxygen delivery and utilization, as indicated by rScO2 and cFTOE, are directly related to functional brain activity, expressed by SAT rate and ISI during the first hours after birth, showing an increase in oxygen extraction in preterm infants with increased early electro-cerebral activity. NIRS monitored oxygenation may be a useful biomarker of brain vulnerability in high-risk infants. PMID:25965343

Real-time mental arithmetic task recognition from EEG signals.

PubMed

Wang, Qiang; Sourina, Olga

2013-03-01

Electroencephalography (EEG)-based monitoring the state of the user's brain functioning and giving her/him the visual/audio/tactile feedback is called neurofeedback technique, and it could allow the user to train the corresponding brain functions. It could provide an alternative way of treatment for some psychological disorders such as attention deficit hyperactivity disorder (ADHD), where concentration function deficit exists, autism spectrum disorder (ASD), or dyscalculia where the difficulty in learning and comprehending the arithmetic exists. In this paper, a novel method for multifractal analysis of EEG signals named generalized Higuchi fractal dimension spectrum (GHFDS) was proposed and applied in mental arithmetic task recognition from EEG signals. Other features such as power spectrum density (PSD), autoregressive model (AR), and statistical features were analyzed as well. The usage of the proposed fractal dimension spectrum of EEG signal in combination with other features improved the mental arithmetic task recognition accuracy in both multi-channel and one-channel subject-dependent algorithms up to 97.87% and 84.15% correspondingly. Based on the channel ranking, four channels were chosen which gave the accuracy up to 97.11%. Reliable real-time neurofeedback system could be implemented based on the algorithms proposed in this paper.

Prediction of rhythmic and periodic EEG patterns and seizures on continuous EEG with early epileptiform discharges.

PubMed

Koren, J; Herta, J; Draschtak, S; Pötzl, G; Pirker, S; Fürbass, F; Hartmann, M; Kluge, T; Baumgartner, C

2015-08-01

Continuous EEG (cEEG) is necessary to document nonconvulsive seizures (NCS), nonconvulsive status epilepticus (NCSE), as well as rhythmic and periodic EEG patterns of 'ictal-interictal uncertainty' (RPPIIU) including periodic discharges, rhythmic delta activity, and spike-and-wave complexes in neurological intensive care patients. However, cEEG is associated with significant recording and analysis efforts. Therefore, predictors from short-term routine EEG with a reasonably high yield are urgently needed in order to select patients for evaluation with cEEG. The aim of this study was to assess the prognostic significance of early epileptiform discharges (i.e., within the first 30 min of EEG recording) on the following: (1) incidence of ictal EEG patterns and RPPIIU on subsequent cEEG, (2) occurrence of acute convulsive seizures during the ICU stay, and (3) functional outcome after 6 months of follow-up. We conducted a separate analysis of the first 30 min and the remaining segments of prospective cEEG recordings according to the ACNS Standardized Critical Care EEG Terminology as well as NCS criteria and review of clinical data of 32 neurological critical care patients. In 17 patients with epileptiform discharges within the first 30 min of EEG (group 1), electrographic seizures were observed in 23.5% (n = 4), rhythmic or periodic EEG patterns of 'ictal-interictal uncertainty' in 64.7% (n = 11), and neither electrographic seizures nor RPPIIU in 11.8% (n = 2). In 15 patients with no epileptiform discharges in the first 30 min of EEG (group 2), no electrographic seizures were recorded on subsequent cEEG, RPPIIU were seen in 26.7% (n = 4), and neither electrographic seizures nor RPPIIU in 73.3% (n = 11). The incidence of EEG patterns on cEEG was significantly different between the two groups (p = 0.008). Patients with early epileptiform discharges developed acute seizures more frequently than patients without early epileptiform discharges (p = 0.009). Finally, functional outcome six months after discharge was significantly worse in patients with early epileptiform discharges (p=0.01). Epileptiform discharges within the first 30 min of EEG recording are predictive for the occurrence of ictal EEG patterns and for RPPIIU on subsequent cEEG, for acute convulsive seizures during the ICU stay, and for a worse functional outcome after 6 months of follow-up. This article is part of a Special Issue entitled Status Epilepticus. Copyright © 2015 Elsevier Inc. All rights reserved.

High Definition Transcranial Direct Current Stimulation Induces Both Acute and Persistent Changes in Broadband Cortical Synchronization: a Simultaneous tDCS-EEG Study

PubMed Central

Roy, Abhrajeet; Baxter, Bryan

2014-01-01

The goal of this study was to develop methods for simultaneously acquiring electrophysiological data during high definition transcranial direct current stimulation (tDCS) using high resolution electroencephalography (EEG). Previous studies have pointed to the after effects of tDCS on both motor and cognitive performance, and there appears to be potential for using tDCS in a variety of clinical applications. However, little is known about the real-time effects of tDCS on rhythmic cortical activity in humans due to the technical challenges of simultaneously obtaining electrophysiological data during ongoing stimulation. Furthermore, the mechanisms of action of tDCS in humans are not well understood. We have conducted a simultaneous tDCS-EEG study in a group of healthy human subjects. Significant acute and persistent changes in spontaneous neural activity and event related synchronization (ERS) were observed during and after the application of high definition tDCS over the left sensorimotor cortex. Both anodal and cathodal stimulation resulted in acute global changes in broadband cortical activity which were significantly different than the changes observed in response to sham stimulation. For the group of 8 subjects studied, broadband individual changes in spontaneous activity during stimulation were apparent both locally and globally. In addition, we found that high definition tDCS of the left sensorimotor cortex can induce significant ipsilateral and contralateral changes in event related desynchronization (ERD) and ERS during motor imagination following the end of the stimulation period. Overall, our results demonstrate the feasibility of acquiring high resolution EEG during high definition tDCS and provide evidence that tDCS in humans directly modulates rhythmic cortical synchronization during and after its administration. PMID:24956615

Resting and reactive frontal brain electrical activity (EEG) among a non-clinical sample of socially anxious adults: Does concurrent depressive mood matter?

PubMed Central

Beaton, Elliott A; Schmidt, Louis A; Ashbaugh, Andrea R; Santesso, Diane L; Antony, Martin M; McCabe, Randi E

2008-01-01

A number of studies have noted that the pattern of resting frontal brain electrical activity (EEG) is related to individual differences in affective style in healthy infants, children, and adults and some clinical populations when symptoms are reduced or in remission. We measured self-reported trait shyness and sociability, concurrent depressive mood, and frontal brain electrical activity (EEG) at rest and in anticipation of a speech task in a non-clinical sample of healthy young adults selected for high and low social anxiety. Although the patterns of resting and reactive frontal EEG asymmetry did not distinguish among individual differences in social anxiety, the pattern of resting frontal EEG asymmetry was related to trait shyness after controlling for concurrent depressive mood. Individuals who reported a higher degree of shyness were likely to exhibit greater relative right frontal EEG activity at rest. However, trait shyness was not related to frontal EEG asymmetry measured during the speech-preparation task, even after controlling for concurrent depressive mood. These findings replicate and extend prior work on resting frontal EEG asymmetry and individual differences in affective style in adults. Findings also highlight the importance of considering concurrent emotional states of participants when examining psychophysiological correlates of personality. PMID:18728822

Like/dislike analysis using EEG: determination of most discriminative channels and frequencies.

PubMed

Yılmaz, Bülent; Korkmaz, Sümeyye; Arslan, Dilek Betül; Güngör, Evrim; Asyalı, Musa H

2014-02-01

In this study, we have analyzed electroencephalography (EEG) signals to investigate the following issues, (i) which frequencies and EEG channels could be relatively better indicators of preference (like or dislike decisions) of consumer products, (ii) timing characteristic of "like" decisions during such mental processes. For this purpose, we have obtained multichannel EEG recordings from 15 subjects, during total of 16 epochs of 10 s long, while they were presented with some shoe photographs. When they liked a specific shoe, they pressed on a button and marked the time of this activity and the particular epoch was labeled as a LIKE case. No button press meant that the subject did not like the particular shoe that was displayed and corresponding epoch designated as a DISLIKE case. After preprocessing, power spectral density (PSD) of EEG data was estimated at different frequencies (4, 5, …, 40 Hz) using the Burg method, for each epoch corresponding to one shoe presentation. Each subject's data consisted of normalized PSD values (NPVs) from all LIKE and DISLIKE cases/epochs coming from all 19 EEG channels. In order to determine the most discriminative frequencies and channels, we have utilized logistic regression, where LIKE/DISLIKE status was used as a categorical (binary) response variable and corresponding NPVs were the continuously valued input variables or predictors. We observed that when all the NPVs (total of 37) are used as predictors, the regression problem was becoming ill-posed due to large number of predictors (compared to the number of samples) and high correlation among predictors. To circumvent this issue, we have divided the frequency band into low frequency (LF) 4-19 Hz and high frequency (HF) 20-40 Hz bands and analyzed the influence of the NPV in these bands separately. Then, using the p-values that indicate how significantly estimated predictor weights are different than zero, we have determined the NPVs and channels that are more influential in determining the outcome, i.e., like/dislike decision. In the LF band, 4 and 5 Hz were found to be the most discriminative frequencies (MDFs). In the HF band, none of the frequencies seemed offer significant information. When both male and female data was used, in the LF band, a frontal channel on the left (F7-A1) and a temporal channel on the right (T6-A2) were found to be the most discriminative channels (MDCs). In the HF band, MDCs were central (Cz-A1) and occipital on the left (O1-A1) channels. The results of like timings suggest that male and female behavior for this set of stimulant images were similar. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Dreaming and the default network: A review, synthesis, and counterintuitive research proposal.

PubMed

Domhoff, G William; Fox, Kieran C R

2015-05-01

This article argues that the default network, augmented by secondary visual and sensorimotor cortices, is the likely neural correlate of dreaming. This hypothesis is based on a synthesis of work on dream content, the findings on the contents and neural correlates of mind-wandering, and the results from EEG and neuroimaging studies of REM sleep. Relying on studies in the 1970s that serendipitously discovered episodes of dreaming during waking mind-wandering, this article presents the seemingly counterintuitive hypothesis that the neural correlates for dreaming could be further specified in the process of carrying out EEG/fMRI studies of mind-wandering and default network activity. This hypothesis could be tested by asking participants for experiential reports during moments of differentially high levels of default network activation, as indicated by mixed EEG/fMRI criteria. Evidence from earlier EEG/fMRI studies of mind-wandering and from laboratory studies of dreaming during the sleep-onset process is used to support the argument. Copyright © 2015 Elsevier Inc. All rights reserved.

A method for detecting nonlinear determinism in normal and epileptic brain EEG signals.

PubMed

Meghdadi, Amir H; Fazel-Rezai, Reza; Aghakhani, Yahya

2007-01-01

A robust method of detecting determinism for short time series is proposed and applied to both healthy and epileptic EEG signals. The method provides a robust measure of determinism through characterizing the trajectories of the signal components which are obtained through singular value decomposition. Robustness of the method is shown by calculating proposed index of determinism at different levels of white and colored noise added to a simulated chaotic signal. The method is shown to be able to detect determinism at considerably high levels of additive noise. The method is then applied to both intracranial and scalp EEG recordings collected in different data sets for healthy and epileptic brain signals. The results show that for all of the studied EEG data sets there is enough evidence of determinism. The determinism is more significant for intracranial EEG recordings particularly during seizure activity.

Adaptive Filtration of Physiological Artifacts in EEG Signals in Humans Using Empirical Mode Decomposition

NASA Astrophysics Data System (ADS)

Grubov, V. V.; Runnova, A. E.; Hramov, A. E.

2018-05-01

A new method for adaptive filtration of experimental EEG signals in humans and for removal of different physiological artifacts has been proposed. The algorithm of the method includes empirical mode decomposition of EEG, determination of the number of empirical modes that are considered, analysis of the empirical modes and search for modes that contains artifacts, removal of these modes, and reconstruction of the EEG signal. The method was tested on experimental human EEG signals and demonstrated high efficiency in the removal of different types of physiological EEG artifacts.

[Effect of high altitude hypoxia on the human EEG].

PubMed

Daniiarov, S B; Vilenskaia, E M

1980-01-01

The paper presents the results of the comparative study of the EEG at alpine altitudes (Tuya -- Ashu pass, 3200 m) and at low altitudes (City of Frunze, 760 m above the sea level). The dynamics of EEG changes at different stages of adaptation to hypoxia is also traced. The obtained data show that the alpine hypoxia produces a considerable intensification of the excitation processes in the cerebral cortex. Different sensitivity to the oxigen shortage has been found in the frontal-temporal parts of the right and the left hemispheres.

Experimental Sleep Restriction Facilitates Pain and Electrically Induced Cortical Responses.

PubMed

Matre, Dagfinn; Hu, Li; Viken, Leif A; Hjelle, Ingri B; Wigemyr, Monica; Knardahl, Stein; Sand, Trond; Nilsen, Kristian Bernhard

2015-10-01

Sleep restriction (SR) has been hypothesized to sensitize the pain system. The current study determined whether experimental sleep restriction had an effect on experimentally induced pain and pain-elicited electroencephalographic (EEG) responses. A paired crossover study. Pain testing was performed after 2 nights of 50% SR and after 2 nights with habitual sleep (HS). Laboratory experiment at research center. Self-reported healthy volunteers (n = 21, age range: 18-31 y). Brief high-density electrical stimuli to the forearm skin produced pinprick-like pain. Subjective pain ratings increased after SR, but only in response to the highest stimulus intensity (P = 0.018). SR increased the magnitude of the pain-elicited EEG response analyzed in the time-frequency domain (P = 0.021). Habituation across blocks did not differ between HS and SR. Event-related desynchronization (ERD) was reduced after SR (P = 0.039). Pressure pain threshold of the trapezius muscle region also decreased after SR (P = 0.017). Sleep restriction (SR) increased the sensitivity to pressure pain and to electrically induced pain of moderate, but not low, intensity. The increased electrical pain could not be explained by a difference in habituation. Increased response magnitude is possibly related to reduced processing within the somatosensory cortex after partial SR. © 2015 Associated Professional Sleep Societies, LLC.

Heart rate calculation from ensemble brain wave using wavelet and Teager-Kaiser energy operator.

PubMed

Srinivasan, Jayaraman; Adithya, V

2015-01-01

Electroencephalogram (EEG) signal artifacts are caused by various factors, such as, Electro-oculogram (EOG), Electromyogram (EMG), Electrocardiogram (ECG), movement artifact and line interference. The relatively high electrical energy cardiac activity causes EEG artifacts. In EEG signal processing the general approach is to remove the ECG signal. In this paper, we introduce an automated method to extract the ECG signal from EEG using wavelet and Teager-Kaiser energy operator for R-peak enhancement and detection. From the detected R-peaks the heart rate (HR) is calculated for clinical diagnosis. To check the efficiency of our method, we compare the HR calculated from ECG signal recorded in synchronous with EEG. The proposed method yields a mean error of 1.4% for the heart rate and 1.7% for mean R-R interval. The result illustrates that, proposed method can be used for ECG extraction from single channel EEG and used in clinical diagnosis like estimation for stress analysis, fatigue, and sleep stages classification studies as a multi-model system. In addition, this method eliminates the dependence of additional synchronous ECG in extraction of ECG from EEG signal process.

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.

[EEG-correlates of pilots' functional condition in simulated flight dynamics].

PubMed

Kiroy, V N; Aslanyan, E V; Bakhtin, O M; Minyaeva, N R; Lazurenko, D M

2015-01-01

The spectral characteristics of the EEG recorded on two professional pilots in the simulator TU-154 aircraft in flight dynamics, including takeoff, landing and horizontal flight (in particular during difficult conditions) were analyzed. EEG recording was made with frequency band 0.1-70 Hz continuously from 15 electrodes. The EEG recordings were evaluated using analysis of variance and discriminant analysis. Statistical significant of the identified differences and the influence of the main factors and their interactions were evaluated using Greenhouse - Gaiser corrections. It was shown that the spectral characteristics of the EEG are highly informative features of the state of the pilots, reflecting the different flight phases. High validity ofthe differences including individual characteristic, indicates their non-random nature and the possibility of constructing a system of pilots' state control during all phases of flight, based on EEG features.

Correlation between EEG abnormalities and symptoms of autism spectrum disorder (ASD).

PubMed

Yasuhara, Akihiro

2010-11-01

Children with ASD often suffer from epilepsy and paroxysmal EEG abnormality. Purposes of this study are the confirmation of incidence of epileptic seizures and EEG abnormalities in children with autism using a high performance digital EEG, to examine the nature of EEG abnormalities such as locus or modality, and to determine if the development of children with ASD, who have experienced developmental delay, improves when their epilepsy has been treated and maintained under control. A total of 1014 autistic children that have been treated and followed-up for more than 3 years at Yasuhara Children's Clinic in Osaka, Japan, were included in this study. Each participant's EEG had been recorded approximately every 6 months under sleep conditions. Epilepsy was diagnosed in 37% (375/1014) of the study participants. Almost all patients diagnosed with epilepsy presented with symptomatic epilepsy. The data showed that the participants with lower IQ had a higher incidence of epileptic seizures. Epileptic EEG discharges occurred in 85.8% (870/1014) of the patients. There was also a very high incidence of spike discharges in participants whose intellectual quotient was very low or low. Epileptic seizure waves most frequently developed from the frontal lobe (65.6%), including the front pole (Fp1 and Fp2), frontal part (F3, F4, F7 and F8) and central part (C3, Cz and C4). The occurrence rate of spike discharges in other locations, including temporal lobe (T3, T4, T5, T6), parietal lobe (P3, Pz, P4), occipital lobe (O1, O2) and multifocal spikes was less than 10%. These results support the notion that there is a relationship between ASD and dysfunction of the mirror neuron system. The management of seizure waves in children diagnosed with ASD may result in improves function and reduction of autistic symptoms. Copyright © 2010 Elsevier B.V. All rights reserved.

Differences between state entropy and bispectral index during analysis of identical electroencephalogram signals: a comparison with two randomised anaesthetic techniques.

PubMed

Pilge, Stefanie; Kreuzer, Matthias; Karatchiviev, Veliko; Kochs, Eberhard F; Malcharek, Michael; Schneider, Gerhard

2015-05-01

It is claimed that bispectral index (BIS) and state entropy reflect an identical clinical spectrum, the hypnotic component of anaesthesia. So far, it is not known to what extent different devices display similar index values while processing identical electroencephalogram (EEG) signals. To compare BIS and state entropy during analysis of identical EEG data. Inspection of raw EEG input to detect potential causes of erroneous index calculation. Offline re-analysis of EEG data from a randomised, single-centre controlled trial using the Entropy Module and an Aspect A-2000 monitor. Klinikum rechts der Isar, Technische Universität München, Munich. Forty adult patients undergoing elective surgery under general anaesthesia. Blocked randomisation of 20 patients per anaesthetic group (sevoflurane/remifentanil or propofol/remifentanil). Isolated forearm technique for differentiation between consciousness and unconsciousness. Prediction probability (PK) of state entropy to discriminate consciousness from unconsciousness. Correlation and agreement between state entropy and BIS from deep to light hypnosis. Analysis of raw EEG compared with index values that are in conflict with clinical examination, with frequency measures (frequency bands/Spectral Edge Frequency 95) and visual inspection for physiological EEG patterns (e.g. beta or delta arousal), pathophysiological features such as high-frequency signals (electromyogram/high-frequency EEG or eye fluttering/saccades), different types of electro-oculogram or epileptiform EEG and technical artefacts. PK of state entropy was 0.80 and of BIS 0.84; correlation coefficient of state entropy with BIS 0.78. Nine percent BIS and 14% state entropy values disagreed with clinical examination. Highest incidence of disagreement occurred after state transitions, in particular for state entropy after loss of consciousness during sevoflurane anaesthesia. EEG sequences which led to false 'conscious' index values often showed high-frequency signals and eye blinks. High-frequency EEG/electromyogram signals were pooled because a separation into EEG and fast electro-oculogram, for example eye fluttering or saccades, on the basis of a single EEG channel may not be very reliable. These signals led to higher Spectral Edge Frequency 95 and ratio of relative beta and gamma band power than EEG signals, indicating adequate unconscious classification. The frequency of other artefacts that were assignable, for example technical artefacts, movement artefacts, was negligible and they were excluded from analysis. High-frequency signals and eye blinks may account for index values that falsely indicate consciousness. Compared with BIS, state entropy showed more false classifications of the clinical state at transition between consciousness and unconsciousness.

Correlates of a single cortical action potential in the epidural EEG

PubMed Central

Teleńczuk, Bartosz; Baker, Stuart N; Kempter, Richard; Curio, Gabriel

2015-01-01

To identify the correlates of a single cortical action potential in surface EEG, we recorded simultaneously epidural EEG and single-unit activity in the primary somatosensory cortex of awake macaque monkeys. By averaging over EEG segments coincident with more than hundred thousand single spikes, we found short-lived (≈ 0.5 ms) triphasic EEG deflections dominated by high-frequency components > 800 Hz. The peak-to-peak amplitude of the grand-averaged spike correlate was 80 nV, which matched theoretical predictions, while single-neuron amplitudes ranged from 12 to 966 nV. Combining these estimates with post-stimulus-time histograms of single-unit responses to median-nerve stimulation allowed us to predict the shape of the evoked epidural EEG response and to estimate the number of contributing neurons. These findings establish spiking activity of cortical neurons as a primary building block of high-frequency epidural EEG, which thus can serve as a quantitative macroscopic marker of neuronal spikes. PMID:25554430

Feasibility of an intracranial EEG-fMRI protocol at 3T: risk assessment and image quality.

PubMed

Boucousis, Shannon M; Beers, Craig A; Cunningham, Cameron J B; Gaxiola-Valdez, Ismael; Pittman, Daniel J; Goodyear, Bradley G; Federico, Paolo

2012-11-15

Integrating intracranial EEG (iEEG) with functional MRI (iEEG-fMRI) may help elucidate mechanisms underlying the generation of seizures. However, the introduction of iEEG electrodes in the MR environment has inherent risk and data quality implications that require consideration prior to clinical use. Previous studies of subdural and depth electrodes have confirmed low risk under specific circumstances at 1.5T and 3T. However, no studies have assessed risk and image quality related to the feasibility of a full iEEG-fMRI protocol. To this end, commercially available platinum subdural grid/strip electrodes (4×5 grid or 1×8 strip) and 4 or 6-contact depth electrodes were secured to the surface of a custom-made phantom mimicking the conductivity of the human brain. Electrode displacement, temperature increase of electrodes and surrounding phantom material, and voltage fluctuations in electrode contacts were measured in a GE Discovery MR750 3T MR scanner during a variety of imaging sequences, typical of an iEEG-fMRI protocol. An electrode grid was also used to quantify the spatial extent of susceptibility artifact. The spatial extent of susceptibility artifact in the presence of an electrode was also assessed for typical imaging parameters that maximize BOLD sensitivity at 3T (TR=1500 ms; TE=30 ms; slice thickness=4mm; matrix=64×64; field-of-view=24 cm). Under standard conditions, all electrodes exhibited no measurable displacement and no clinically significant temperature increase (<1°C) during scans employed in a typical iEEG-fMRI experiment, including 60 min of continuous fMRI. However, high SAR sequences, such as fast spin-echo (FSE), produced significant heating in almost all scenarios (>2.0°C) that in some cases exceeded 10°C. Induced voltages in the frequency range that could elicit neuronal stimulation (<10 kHz) were well below the threshold of 100 mV. fMRI signal intensity was significantly reduced within 20mm of the electrodes for the imaging parameters used in this study. Thus, for the conditions tested, a full iEEG-fMRI protocol poses a low risk at 3T; however, fMRI sensitivity may be reduced immediately adjacent to the electrodes. In addition, high SAR sequences must be avoided. Copyright © 2012 Elsevier Inc. All rights reserved.

Sensor Level Functional Connectivity Topography Comparison Between Different References Based EEG and MEG

PubMed Central

Huang, Yunzhi; Zhang, Junpeng; Cui, Yuan; Yang, Gang; Liu, Qi; Yin, Guangfu

2018-01-01

Sensor-level functional connectivity topography (sFCT) contributes significantly to our understanding of brain networks. sFCT can be constructed using either electroencephalography (EEG) or magnetoencephalography (MEG). Here, we compared sFCT within the EEG modality and between EEG and MEG modalities. We first used simulations to look at how different EEG references—including the Reference Electrode Standardization Technique (REST), average reference (AR), linked mastoids (LM), and left mastoid references (LR)—affect EEG-based sFCT. The results showed that REST decreased the reference effects on scalp EEG recordings, making REST-based sFCT closer to the ground truth (sFCT based on ideal recordings). For the inter-modality simulation comparisons, we compared each type of EEG-sFCT with MEG-sFCT using three metrics to quantize the differences: Relative Error (RE), Overlap Rate (OR), and Hamming Distance (HD). When two sFCTs are similar, RE and HD are low, while OR is high. Results showed that among all reference schemes, EEG-and MEG-sFCT were most similar when the EEG was REST-based and the EEG and MEG were recorded simultaneously. Next, we analyzed simultaneously recorded MEG and EEG data from publicly available face-recognition experiments using a similar procedure as in the simulations. The results showed (1) if MEG-sFCT is the standard, REST—and LM-based sFCT provided results closer to this standard in the terms of HD; (2) REST-based sFCT and MEG-sFCT had the highest similarity in terms of RE; (3) REST-based sFCT had the most overlapping edges with MEG-sFCT in terms of OR. This study thus provides new insights into the effect of different reference schemes on sFCT and the similarity between MEG and EEG in terms of sFCT. PMID:29867395

A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking

PubMed Central

Han, Jiuqi; Zhao, Yuwei; Sun, Hongji; Chen, Jiayun; Ke, Ang; Xu, Gesen; Zhang, Hualiang; Zhou, Jin; Wang, Changyong

2018-01-01

Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG) classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high computational complexity, non-convergent procedure and narrow expansibility. In this paper, to remedy these drawbacks, we propose a fast, open EEG classification framework centralized by EEG feature compression, low-dimensional representation, and convergent iterative channel ranking. First, to reduce the complexity, we use data clustering to compress the EEG features channel-wise, packing the high-dimensional EEG signal, and endowing them with numerical signatures. Second, to provide easy access to alternative superior methods, we structurally represent each EEG trial in a feature vector with its corresponding numerical signature. Thus, the recorded signals of many trials shrink to a low-dimensional structural matrix compatible with most pattern recognition methods. Third, a series of effective iterative feature selection approaches with theoretical convergence is introduced to rank the EEG channels and remove redundant ones, further accelerating the EEG classification process and ensuring its stability. Finally, a classical linear discriminant analysis (LDA) model is employed to classify a single EEG trial with selected channels. Experimental results on two real world brain-computer interface (BCI) competition datasets demonstrate the promising performance of the proposed framework over state-of-the-art methods. PMID:29713262

Systems, Subjects, Sessions: To What Extent Do These Factors Influence EEG Data?

PubMed

Melnik, Andrew; Legkov, Petr; Izdebski, Krzysztof; Kärcher, Silke M; Hairston, W David; Ferris, Daniel P; König, Peter

2017-01-01

Lab-based electroencephalography (EEG) techniques have matured over decades of research and can produce high-quality scientific data. It is often assumed that the specific choice of EEG system has limited impact on the data and does not add variance to the results. However, many low cost and mobile EEG systems are now available, and there is some doubt as to the how EEG data vary across these newer systems. We sought to determine how variance across systems compares to variance across subjects or repeated sessions. We tested four EEG systems: two standard research-grade systems, one system designed for mobile use with dry electrodes, and an affordable mobile system with a lower channel count. We recorded four subjects three times with each of the four EEG systems. This setup allowed us to assess the influence of all three factors on the variance of data. Subjects performed a battery of six short standard EEG paradigms based on event-related potentials (ERPs) and steady-state visually evoked potential (SSVEP). Results demonstrated that subjects account for 32% of the variance, systems for 9% of the variance, and repeated sessions for each subject-system combination for 1% of the variance. In most lab-based EEG research, the number of subjects per study typically ranges from 10 to 20, and error of uncertainty in estimates of the mean (like ERP) will improve by the square root of the number of subjects. As a result, the variance due to EEG system (9%) is of the same order of magnitude as variance due to subjects (32%/sqrt(16) = 8%) with a pool of 16 subjects. The two standard research-grade EEG systems had no significantly different means from each other across all paradigms. However, the two other EEG systems demonstrated different mean values from one or both of the two standard research-grade EEG systems in at least half of the paradigms. In addition to providing specific estimates of the variability across EEG systems, subjects, and repeated sessions, we also propose a benchmark to evaluate new mobile EEG systems by means of ERP responses.

Systems, Subjects, Sessions: To What Extent Do These Factors Influence EEG Data?

PubMed Central

Melnik, Andrew; Legkov, Petr; Izdebski, Krzysztof; Kärcher, Silke M.; Hairston, W. David; Ferris, Daniel P.; König, Peter

2017-01-01

Lab-based electroencephalography (EEG) techniques have matured over decades of research and can produce high-quality scientific data. It is often assumed that the specific choice of EEG system has limited impact on the data and does not add variance to the results. However, many low cost and mobile EEG systems are now available, and there is some doubt as to the how EEG data vary across these newer systems. We sought to determine how variance across systems compares to variance across subjects or repeated sessions. We tested four EEG systems: two standard research-grade systems, one system designed for mobile use with dry electrodes, and an affordable mobile system with a lower channel count. We recorded four subjects three times with each of the four EEG systems. This setup allowed us to assess the influence of all three factors on the variance of data. Subjects performed a battery of six short standard EEG paradigms based on event-related potentials (ERPs) and steady-state visually evoked potential (SSVEP). Results demonstrated that subjects account for 32% of the variance, systems for 9% of the variance, and repeated sessions for each subject-system combination for 1% of the variance. In most lab-based EEG research, the number of subjects per study typically ranges from 10 to 20, and error of uncertainty in estimates of the mean (like ERP) will improve by the square root of the number of subjects. As a result, the variance due to EEG system (9%) is of the same order of magnitude as variance due to subjects (32%/sqrt(16) = 8%) with a pool of 16 subjects. The two standard research-grade EEG systems had no significantly different means from each other across all paradigms. However, the two other EEG systems demonstrated different mean values from one or both of the two standard research-grade EEG systems in at least half of the paradigms. In addition to providing specific estimates of the variability across EEG systems, subjects, and repeated sessions, we also propose a benchmark to evaluate new mobile EEG systems by means of ERP responses. PMID:28424600

Design of a 32-Channel EEG System for Brain Control Interface Applications

PubMed Central

Wang, Ching-Sung

2012-01-01

This study integrates the hardware circuit design and the development support of the software interface to achieve a 32-channel EEG system for BCI applications. Since the EEG signals of human bodies are generally very weak, in addition to preventing noise interference, it also requires avoiding the waveform distortion as well as waveform offset and so on; therefore, the design of a preamplifier with high common-mode rejection ratio and high signal-to-noise ratio is very important. Moreover, the friction between the electrode pads and the skin as well as the design of dual power supply will generate DC bias which affects the measurement signals. For this reason, this study specially designs an improved single-power AC-coupled circuit, which effectively reduces the DC bias and improves the error caused by the effects of part errors. At the same time, the digital way is applied to design the adjustable amplification and filter function, which can design for different EEG frequency bands. For the analog circuit, a frequency band will be taken out through the filtering circuit and then the digital filtering design will be used to adjust the extracted frequency band for the target frequency band, combining with MATLAB to design man-machine interface for displaying brain wave. Finally the measured signals are compared to the traditional 32-channel EEG signals. In addition to meeting the IFCN standards, the system design also conducted measurement verification in the standard EEG isolation room in order to demonstrate the accuracy and reliability of this system design. PMID:22778545

Design of a 32-channel EEG system for brain control interface applications.

PubMed

Wang, Ching-Sung

2012-01-01

This study integrates the hardware circuit design and the development support of the software interface to achieve a 32-channel EEG system for BCI applications. Since the EEG signals of human bodies are generally very weak, in addition to preventing noise interference, it also requires avoiding the waveform distortion as well as waveform offset and so on; therefore, the design of a preamplifier with high common-mode rejection ratio and high signal-to-noise ratio is very important. Moreover, the friction between the electrode pads and the skin as well as the design of dual power supply will generate DC bias which affects the measurement signals. For this reason, this study specially designs an improved single-power AC-coupled circuit, which effectively reduces the DC bias and improves the error caused by the effects of part errors. At the same time, the digital way is applied to design the adjustable amplification and filter function, which can design for different EEG frequency bands. For the analog circuit, a frequency band will be taken out through the filtering circuit and then the digital filtering design will be used to adjust the extracted frequency band for the target frequency band, combining with MATLAB to design man-machine interface for displaying brain wave. Finally the measured signals are compared to the traditional 32-channel EEG signals. In addition to meeting the IFCN standards, the system design also conducted measurement verification in the standard EEG isolation room in order to demonstrate the accuracy and reliability of this system design.

EEG-based classification of imaginary left and right foot movements using beta rebound.

PubMed

Hashimoto, Yasunari; Ushiba, Junichi

2013-11-01

The purpose of this study was to investigate cortical lateralization of event-related (de)synchronization during left and right foot motor imagery tasks and to determine classification accuracy of the two imaginary movements in a brain-computer interface (BCI) paradigm. We recorded 31-channel scalp electroencephalograms (EEGs) from nine healthy subjects during brisk imagery tasks of left and right foot movements. EEG was analyzed with time-frequency maps and topographies, and the accuracy rate of classification between left and right foot movements was calculated. Beta rebound at the end of imagination (increase of EEG beta rhythm amplitude) was identified from the two EEGs derived from the right-shift and left-shift bipolar pairs at the vertex. This process enabled discrimination between right or left foot imagery at a high accuracy rate (maximum 81.6% in single trial analysis). These data suggest that foot motor imagery has potential to elicit left-right differences in EEG, while BCI using the unilateral foot imagery can achieve high classification accuracy, similar to ordinary BCI, based on hand motor imagery. By combining conventional discrimination techniques, the left-right discrimination of unilateral foot motor imagery provides a novel BCI system that could control a foot neuroprosthesis or a robotic foot. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

[Mexidol in treatment of children with generalized epilepsy and febrile seizures].

PubMed

Natriashvili, G; Natriashvili, S; Kapanadze, N

2005-05-01

The aim of our study was to estimate the role of Mexidol in ceasing of epileptic fits and improving electroencephalographic (EEG) pathological patterns in children. 120 patients with generalized epilepsy (from 4 to 16 years old) were investigated. All patients were treated by Depakin chrono 30 mg/kg. Children were divided into 2 groups: 1st--study group consisted of 60 children with combined treatment with Depakin and Mexidol (5 mg/kg). In the control group (60 children) treatment was performed only by Depakin. 100 children with the first episode of febrile seizures (from 6 months to 4 years old) were investigated. 50 children composed the study group with monotheraphy by Mexidol and 50 patients--the control group, without any treatment. The EEG examination was done by computer EEG Topography "Brain Surveyor Saico". Using Depakin in combination with Mexidol in the study group of patients with generalized epilepsy, improvement of clinical picture of disease and normalization of EEG patterns in 93% of cases has been observed. In the study group of patients with febrile seizures, normalization of EEG pathological patterns was observed in 82% cases and in 18% its improvement was seen. The relapse of seizures at high temperature was observed in 3 patients. In control group EEG patterns were improved only in 20%, in 48% no positive effect was observed and in 41% the worsening of EEG findings was seen. The relapse of febrile seizures was observed in 26 cases. Mexidol titrated to the target doze of 5mg/kg may be effective in combination with Depakin for treatment of patients with generalized epilepsy and as monotherapy in patients with first episode of febrile seizures.

New Flexible Silicone-Based EEG Dry Sensor Material Compositions Exhibiting Improvements in Lifespan, Conductivity, and Reliability

PubMed Central

Yu, Yi-Hsin; Chen, Shih-Hsun; Chang, Che-Lun; Lin, Chin-Teng; Hairston, W. David; Mrozek, Randy A.

2016-01-01

This study investigates alternative material compositions for flexible silicone-based dry electroencephalography (EEG) electrodes to improve the performance lifespan while maintaining high-fidelity transmission of EEG signals. Electrode materials were fabricated with varying concentrations of silver-coated silica and silver flakes to evaluate their electrical, mechanical, and EEG transmission performance. Scanning electron microscope (SEM) analysis of the initial electrode development identified some weak points in the sensors’ construction, including particle pull-out and ablation of the silver coating on the silica filler. The newly-developed sensor materials achieved significant improvement in EEG measurements while maintaining the advantages of previous silicone-based electrodes, including flexibility and non-toxicity. The experimental results indicated that the proposed electrodes maintained suitable performance even after exposure to temperature fluctuations, 85% relative humidity, and enhanced corrosion conditions demonstrating improvements in the environmental stability. Fabricated flat (forehead) and acicular (hairy sites) electrodes composed of the optimum identified formulation exhibited low impedance and reliable EEG measurement; some initial human experiments demonstrate the feasibility of using these silicone-based electrodes for typical lab data collection applications. PMID:27809260

Assessment of the QT interval in the electroencephalography (EEG) of children with syncope, epilepsy, and attention-deficit hyperactivity disorder (ADHD).

PubMed

Jha, Om P; Khurana, Divya S; Carvalho, Karen S; Melvin, Joseph J; Legido, Agustin; O'Riordan, Anna C; Valencia, Ignacio

2010-03-01

The interpretation of QT interval is often neglected during electroencephalography (EEG) reading. We compared the incidence of prolonged QT interval, as seen in the electrocardiography (ECG) recording lead of the EEG, in children presenting with seizure, syncope, or attention-deficit hyperactivity disorder (ADHD). Abnormal QT was defined as >460 ms. The incidence of prolonged QT in the seizure, syncope, and ADHD groups was 1/50 (2%), 7/50 (14%), and 2/50 (4%), respectively (P = .036, chi-square). The mean +/- SD of QT were 405 +/- 34, 424 +/- 39, and 414 +/- 36, respectively (P = .035, analysis of variance [ANOVA], syncope group, compared with seizure group). The incidence of prolonged QT as measured in the EEG was unexpectedly high in children presenting with seizure, syncope, or ADHD. These data support the concept that QT evaluation should be emphasized during routine EEG reading, as it may aid in identifying cases of undiagnosed cardiac conduction abnormalities. Prospective studies comparing EEG-ECG tracings with 12-lead ECG are warranted.

New Flexible Silicone-Based EEG Dry Sensor Material Compositions Exhibiting Improvements in Lifespan, Conductivity, and Reliability.

PubMed

Yu, Yi-Hsin; Chen, Shih-Hsun; Chang, Che-Lun; Lin, Chin-Teng; Hairston, W David; Mrozek, Randy A

2016-10-31

This study investigates alternative material compositions for flexible silicone-based dry electroencephalography (EEG) electrodes to improve the performance lifespan while maintaining high-fidelity transmission of EEG signals. Electrode materials were fabricated with varying concentrations of silver-coated silica and silver flakes to evaluate their electrical, mechanical, and EEG transmission performance. Scanning electron microscope (SEM) analysis of the initial electrode development identified some weak points in the sensors' construction, including particle pull-out and ablation of the silver coating on the silica filler. The newly-developed sensor materials achieved significant improvement in EEG measurements while maintaining the advantages of previous silicone-based electrodes, including flexibility and non-toxicity. The experimental results indicated that the proposed electrodes maintained suitable performance even after exposure to temperature fluctuations, 85% relative humidity, and enhanced corrosion conditions demonstrating improvements in the environmental stability. Fabricated flat (forehead) and acicular (hairy sites) electrodes composed of the optimum identified formulation exhibited low impedance and reliable EEG measurement; some initial human experiments demonstrate the feasibility of using these silicone-based electrodes for typical lab data collection applications.

The EEG as an index of neuromodulator balance in memory and mental illness.

PubMed

Vakalopoulos, Costa

2014-01-01

There is a strong correlation between signature EEG frequency patterns and the relative levels of distinct neuromodulators. These associations become particularly evident during the sleep-wake cycle. The monoamine-acetylcholine balance hypothesis is a theory of neurophysiological markers of the EEG and a detailed description of the findings that support this proposal are presented in this paper. According to this model alpha rhythm reflects the relative predominance of cholinergic muscarinic signals and delta rhythm that of monoaminergic receptor effects. Both high voltage synchronized rhythms are likely mediated by inhibitory Gαi/o-mediated transduction of inhibitory interneurons. Cognitively, alpha and delta EEG measures are proposed to indicate automatic and flexible strategies, respectively. Sleep is associated with marked changes in relative neuromodulator levels corresponding to EEG markers of distinct stages. Sleep studies on memory consolidation present some of the strongest evidence yet for the respective roles of monoaminergic and cholinergic projections in declarative and non-declarative memory processes, a key theoretical premise for understanding the data. Affective dysregulation is reflected in altered EEG patterns during sleep.

Change in physiological signals during mindfulness meditation

PubMed Central

Ahani, Asieh; Wahbeh, Helane; Miller, Meghan; Nezamfar, Hooman; Erdogmus, Deniz; Oken, Barry

2014-01-01

Mindfulness meditation (MM) is an inward mental practice, in which a resting but alert state of mind is maintained. MM intervention was performed for a population of older people with high stress levels. This study assessed signal processing methodologies of electroencephalographic (EEG) and respiration signals during meditation and control condition to aid in quantification of the meditative state. EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis and support vector machine classification to evaluate an objective marker for meditation. We observed meditation and control condition differences in the alpha, beta and theta frequency bands. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy at discriminating between meditation and control conditions than one using the EEG signal only. EEG and respiration based classifier is a viable objective marker for meditation ability. Future studies should quantify different levels of meditation depth and meditation experience using this classifier. Development of an objective physiological meditation marker will allow the mind-body medicine field to advance by strengthening rigor of methods. PMID:24748422

Decoding human swallowing via electroencephalography: a state-of-the-art review

PubMed Central

Jestrović, Iva; Coyle, James L.

2015-01-01

Swallowing and swallowing disorders have garnered continuing interest over the past several decades. Electroencephalography (EEG) is an inexpensive and non-invasive procedure with very high temporal resolution which enables analysis of short and fast swallowing events, as well as an analysis of the organizational and behavioral aspects of cortical motor preparation, swallowing execution and swallowing regulation. EEG is a powerful technique which can be used alone or in combination with other techniques for monitoring swallowing, detection of swallowing motor imagery for diagnostic or biofeedback purposes, or to modulate and measure the effects of swallowing rehabilitation. This paper provides a review of the existing literature which has deployed EEG in the investigation of oropharyngeal swallowing, smell, taste and texture related to swallowing, cortical pre-motor activation in swallowing, and swallowing motor imagery detection. Furthermore, this paper provides a brief review of the different modalities of brain imaging techniques used to study swallowing brain activities, as well as the EEG components of interest for studies on swallowing and on swallowing motor imagery. Lastly, this paper provides directions for future swallowing investigations using EEG. PMID:26372528

Clozapine-induced EEG abnormalities and clinical response to clozapine.

PubMed

Risby, E D; Epstein, C M; Jewart, R D; Nguyen, B V; Morgan, W N; Risch, S C; Thrivikraman, K V; Lewine, R L

1995-01-01

The authors hypothesized that patients who develop gross EEG abnormalities during clozapine treatment would have a less favorable outcome than patients who did not develop abnormal EEGs. The clinical EEGs and the Brief Psychiatric Rating Scale (BPRS) scores of 12 patients with schizophrenia and 4 patients with schizoaffective disorder were compared before and during treatment with clozapine. Eight patients developed significant EEG abnormalities on clozapine; 1 showed worsening of an abnormal pre-clozapine EEG; none of these subjects had clinical seizures. BPRS scores improved significantly in the group of patients who developed abnormal EEGs but not in the group who did not. Findings are consistent with previous reports of a high incidence of clozapine-induced EEG abnormalities and a positive association between these abnormalities and clinical improvement.

Artifact removal from EEG data with empirical mode decomposition

NASA Astrophysics Data System (ADS)

Grubov, Vadim V.; Runnova, Anastasiya E.; Efremova, Tatyana Yu.; Hramov, Alexander E.

2017-03-01

In the paper we propose the novel method for dealing with the physiological artifacts caused by intensive activity of facial and neck muscles and other movements in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We introduce the mathematical algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from movement artifacts and show high efficiency of the method.

Improved cognitive morning performance in healthy older adults following blue-enriched light exposure on the previous evening.

PubMed

Scheuermaier, Karine; Münch, Mirjam; Ronda, Joseph M; Duffy, Jeanne F

2018-04-21

Exposure to light can have acute alerting and circadian phase-shifting effects. This study investigated the effects of evening exposure to blue-enriched polychromatic white (BEL) vs. polychromatic white light (WL) on sleep inertia dissipation the following morning in older adults. Ten healthy older adults (average age = 63.3 yrs; 6F) participated in a 13-day study comprising three baseline days, an initial circadian phase assessment, four days with 2-h evening light exposures, a post light exposure circadian phase assessment and three recovery days. Participants were randomized to either BEL or WL of the same irradiance for the four evening light exposures. On the next mornings at 2, 12, 22 and 32 min after each wake time, the participants completed a 90-s digit-symbol substitution test (DSST) to assess working memory, and objective alertness was assessed using a wake EEG recording. DSST and power density from the wake EEG recordings were compared between the two groups. DSST performance improved with time awake (p < 0.0001) and across study days in both light exposure groups (p < 0.0001). There was no main effect of group, although we observed a significant day x group interaction (p = 0.0004), whereby participants exposed to BEL performed significantly better on the first two mornings after light exposures than participants in WL (post-hoc, p < 0.05). On those days, the BEL group showed higher EEG activity in some of the frequency bins in the sigma and beta range (p < 0.05) on the wake EEG. Exposure to blue-enriched white light in the evening significantly improved DSST performance the following morning when compared to polychromatic white light. This was associated with a higher level of objective alertness on the wake EEG, but not with changes in sleep or circadian timing. Copyright © 2018 Elsevier B.V. All rights reserved.

Comparison of quantitative EEG characteristics of quiet and active sleep in newborns.

PubMed

Paul, Karel; Krajca, Vladimír; Roth, Zdenek; Melichar, Jan; Petránek, Svojmil

2003-11-01

The aim of the present study was to verify whether the proposed method of computer-supported EEG analysis is able to differentiate the EEG activity in quiet sleep (QS) from that in active sleep (AS) in newborns. A quantitative description of the neonatal EEG may contribute to a more exact evaluation of the functional state of the brain, as well as to a refinement of diagnostics of brain dysfunction manifesting itself frequently as 'dysrhythmia' or 'dysmaturity'. Twenty-one healthy newborns (10 full-term and 11 pre-term) were examined polygraphically (EEG-eight channels, respiration, ECG, EOG and EMG) in the course of sleep. From each EEG record, two 5-min samples (one from QS and one from AS) were subject to an off-line computerized analysis. The obtained data were averaged with respect to the sleep state and to the conceptional age. The number of variables was reduced by means of factor analysis. All factors identified by factor analysis were highly significantly influenced by sleep states in both developmental periods. Likewise, a comparison of the measured variables between QS and AS revealed many statistically significant differences. The variables describing (a) the number and length of quasi-stationary segments, (b) voltage and (c) power in delta and theta bands contributed to the greatest degree to the differentiation of EEGs between both sleep states. The presented method of the computerized EEG analysis which has good discriminative potential is adequately sensitive and describes the neonatal EEG with convenient accuracy.

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

PubMed Central

Bulea, Thomas C.; Kilicarslan, Atilla; Ozdemir, Recep; Paloski, William H.; Contreras-Vidal, Jose L.

2013-01-01

Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG. PMID:23912203

Human cortical activity related to unilateral movements. A high resolution EEG study.

PubMed

Urbano, A; Babiloni, C; Onorati, P; Babiloni, F

1996-12-20

In the present study a modern high resolution electroencephalography (EEG) technique was used to investigate the dynamic functional topography of human cortical activity related to simple unilateral internally triggered finger movements. The sensorimotor area (M1-S1) contralateral to the movement as well as the supplementary motor area (SMA) and to a lesser extent the ipsilateral M1-S1 were active during the preparation and execution of these movements. These findings suggest that both hemispheres may cooperate in both planning and production of simple unilateral volitional acts.

EEG spectral power density profiles during NREM sleep for gaboxadol and zolpidem in patients with primary insomnia.

PubMed

Lundahl, Jonas; Deacon, Steve; Maurice, Damien; Staner, Luc

2012-08-01

There is significant interest in the functional significance and the therapeutic value of slow-wave sleep (SWS)-enhancing drugs. A prerequisite for studies of the functional differences is characterization of the electroencephalography (EEG) spectra following treatment in relevant patients. We evaluate for the first time gaboxadol and zolpidem treatments in insomniac patients using power spectra analysis. We carried out two randomized, double-blind, crossover studies. Study 1, 38 patients received gaboxadol 10 mg and 20 mg and zolpidem 10 mg; study 2, 23 patients received gaboxadol 5 mg and 15 mg. Treatments were administered during two nights and compared with placebo. Gaboxadol 10, 15 and 20 mg enhanced slow-wave activity (SWA) and theta power. In 1 Hz bins gaboxadol 10 and 20 mg enhanced power up to 9 Hz. In study 2, 15 mg gaboxadol showed a similar effect pattern. Zolpidem suppressed theta and alpha power, and increased sigma power, with no effect on SWA. In the 1 Hz bins zolpidem suppressed power between 5-10 Hz. Gaboxadol dose-dependently increased SWA and theta power in insomniac patients. In contrast, zolpidem did not affect SWA, reduced theta and alpha activity and enhanced sigma power. EEG spectral power differences may be consequences of the different mechanisms of action for zolpidem and the SWS-enhancing agent, gaboxadol.

Comparison of simultaneously recorded [H2(15)O]-PET and LORETA during cognitive and pharmacological activation.

PubMed

Gamma, Alex; Lehmann, Dietrich; Frei, Edi; Iwata, Kazuki; Pascual-Marqui, Roberto D; Vollenweider, Franz X

2004-06-01

The complementary strengths and weaknesses of established functional brain imaging methods (high spatial, low temporal resolution) and EEG-based techniques (low spatial, high temporal resolution) make their combined use a promising avenue for studying brain processes at a more fine-grained level. However, this strategy requires a better understanding of the relationship between hemodynamic/metabolic and neuroelectric measures of brain activity. We investigated possible correspondences between cerebral blood flow (CBF) as measured by [H2O]-PET and intracerebral electric activity computed by Low Resolution Brain Electromagnetic Tomography (LORETA) from scalp-recorded multichannel EEG in healthy human subjects during cognitive and pharmacological stimulation. The two imaging modalities were compared by descriptive, correlational, and variance analyses, the latter carried out using statistical parametric mapping (SPM99). Descriptive visual comparison showed a partial overlap between the sets of active brain regions detected by the two modalities. A number of exclusively positive correlations of neuroelectric activity with regional CBF were found across the whole EEG frequency range, including slow wave activity, the latter finding being in contrast to most previous studies conducted in patients. Analysis of variance revealed an extensive lack of statistically significant correspondences between brain activity changes as measured by PET vs. EEG-LORETA. In general, correspondences, to the extent they were found, were dependent on experimental condition, brain region, and EEG frequency. Copyright 2004 Wiley-Liss, Inc.

A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings.

PubMed

Tamburro, Gabriella; Fiedler, Patrique; Stone, David; Haueisen, Jens; Comani, Silvia

2018-01-01

EEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts. Our fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity ( p ). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR. The best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs). Apart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings.

A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings

PubMed Central

Tamburro, Gabriella; Fiedler, Patrique; Stone, David; Haueisen, Jens

2018-01-01

Background EEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts. Methods Our fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity (p). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR. Results The best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs). Discussion Apart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings. PMID:29492336

Quantitative EEG during REM and NREM sleep in combat-exposed veterans with and without Posttraumatic Stress Disorder

PubMed Central

Cohen, Daniel J.; Begley, Amy; Alman, Jennie J.; Cashmere, J. David; Pietrone, Regina N.; Seres, Robert J.; Germain, Anne

2012-01-01

Summary Sleep disturbances are a hallmark feature of posttraumatic stress disorder (PTSD), and associated with poor clinical outcomes. Few studies have examined sleep quantitative electroencephalography (qEEG), a technique able to detect subtle differences polysomnography does not capture. We hypothesized greater high-frequency qEEG would reflect “hyperarousal” in in combat veterans with PTSD (n=16) compared to veterans without PTSD (n=13). EEG power in traditional EEG frequency bands was computed for artifact-free sleep epochs across an entire night. Correlations were performed between qEEG and ratings of PTSD symptoms and combat exposure. The groups did not differ significantly in whole night qEEG measures for either REM or NREM. Non-significant medium effect sizes suggest less REM beta (opposite to our hypothesis), less REM and NREM sigma, and more NREM gamma in combat veterans with PTSD. Positive correlations were found between combat exposure and NREM beta (PTSD group only), and REM and NREM sigma (non-PTSD group only). Results did not support global hyperarousal in PTSD as indexed by increased beta qEEG activity. The correlation of sigma activity with combat exposure in those without PTSD, and the non-significant trend towards less sigma activity during both REM and NREM sleep in combat veterans with PTSD suggests that differential information processing during sleep may characterize combat-exposed military veterans with and without PTSD. PMID:22845675

Pharmaco-EEG: A Study of Individualized Medicine in Clinical Practice.

PubMed

Swatzyna, Ronald J; Kozlowski, Gerald P; Tarnow, Jay D

2015-07-01

Pharmaco-electroencephalography (Pharmaco-EEG) studies using clinical EEG and quantitative EEG (qEEG) technologies have existed for more than 4 decades. This is a promising area that could improve psychotropic intervention using neurological data. One of the objectives in our clinical practice has been to collect EEG and quantitative EEG (qEEG) data. In the past 5 years, we have identified a subset of refractory cases (n = 386) found to contain commonalities of a small number of electrophysiological features in the following diagnostic categories: mood, anxiety, autistic spectrum, and attention deficit disorders, Four abnormalities were noted in the majority of medication failure cases and these abnormalities did not appear to significantly align with their diagnoses. Those were the following: encephalopathy, focal slowing, beta spindles, and transient discharges. To analyze the relationship noted, they were tested for association with the assigned diagnoses. Fisher's exact test and binary logistics regression found very little (6%) association between particular EEG/qEEG abnormalities and diagnoses. Findings from studies of this type suggest that EEG/qEEG provides individualized understanding of pharmacotherapy failures and has the potential to improve medication selection. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Automatic EEG-assisted retrospective motion correction for fMRI (aE-REMCOR).

PubMed

Wong, Chung-Ki; Zotev, Vadim; Misaki, Masaya; Phillips, Raquel; Luo, Qingfei; Bodurka, Jerzy

2016-04-01

Head motions during functional magnetic resonance imaging (fMRI) impair fMRI data quality and introduce systematic artifacts that can affect interpretation of fMRI results. Electroencephalography (EEG) recordings performed simultaneously with fMRI provide high-temporal-resolution information about ongoing brain activity as well as head movements. Recently, an EEG-assisted retrospective motion correction (E-REMCOR) method was introduced. E-REMCOR utilizes EEG motion artifacts to correct the effects of head movements in simultaneously acquired fMRI data on a slice-by-slice basis. While E-REMCOR is an efficient motion correction approach, it involves an independent component analysis (ICA) of the EEG data and identification of motion-related ICs. Here we report an automated implementation of E-REMCOR, referred to as aE-REMCOR, which we developed to facilitate the application of E-REMCOR in large-scale EEG-fMRI studies. The aE-REMCOR algorithm, implemented in MATLAB, enables an automated preprocessing of the EEG data, an ICA decomposition, and, importantly, an automatic identification of motion-related ICs. aE-REMCOR has been used to perform retrospective motion correction for 305 fMRI datasets from 16 subjects, who participated in EEG-fMRI experiments conducted on a 3T MRI scanner. Performance of aE-REMCOR has been evaluated based on improvement in temporal signal-to-noise ratio (TSNR) of the fMRI data, as well as correction efficiency defined in terms of spike reduction in fMRI motion parameters. The results show that aE-REMCOR is capable of substantially reducing head motion artifacts in fMRI data. In particular, when there are significant rapid head movements during the scan, a large TSNR improvement and high correction efficiency can be achieved. Depending on a subject's motion, an average TSNR improvement over the brain upon the application of aE-REMCOR can be as high as 27%, with top ten percent of the TSNR improvement values exceeding 55%. The average correction efficiency over the 305 fMRI scans is 18% and the largest achieved efficiency is 71%. The utility of aE-REMCOR on the resting state fMRI connectivity of the default mode network is also examined. The motion-induced position-dependent error in the DMN connectivity analysis is shown to be reduced when aE-REMCOR is utilized. These results demonstrate that aE-REMCOR can be conveniently and efficiently used to improve fMRI motion correction in large clinical EEG-fMRI studies. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Using Electroencephalography for Treatment Guidance in Major Depressive Disorder.

PubMed

Wade, Elizabeth C; Iosifescu, Dan V

2016-09-01

Given the high prevalence of treatment-resistant depression and the long delays in finding effective treatments via trial and error, valid biomarkers of treatment outcome with the ability to guide treatment selection represent one of the most important unmet needs in mood disorders. A large body of research has investigated, for this purpose, biomarkers derived from electroencephalography (EEG), using resting state EEG or evoked potentials. Most studies have focused on specific EEG features (or combinations thereof), whereas more recently machine-learning approaches have been used to define the EEG features with the best predictive abilities without a priori hypotheses. While reviewing these different approaches, we have focused on the predictor characteristics and the quality of the supporting evidence. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

A new EEG measure using the 1D cluster variation method

NASA Astrophysics Data System (ADS)

Maren, Alianna J.; Szu, Harold H.

2015-05-01

A new information measure, drawing on the 1-D Cluster Variation Method (CVM), describes local pattern distributions (nearest-neighbor and next-nearest neighbor) in a binary 1-D vector in terms of a single interaction enthalpy parameter h for the specific case where the fractions of elements in each of two states are the same (x1=x2=0.5). An example application of this method would be for EEG interpretation in Brain-Computer Interfaces (BCIs), especially in the frontier of invariant biometrics based on distinctive and invariant individual responses to stimuli containing an image of a person with whom there is a strong affiliative response (e.g., to a person's grandmother). This measure is obtained by mapping EEG observed configuration variables (z1, z2, z3 for next-nearest neighbor triplets) to h using the analytic function giving h in terms of these variables at equilibrium. This mapping results in a small phase space region of resulting h values, which characterizes local pattern distributions in the source data. The 1-D vector with equal fractions of units in each of the two states can be obtained using the method for transforming natural images into a binarized equi-probability ensemble (Saremi & Sejnowski, 2014; Stephens et al., 2013). An intrinsically 2-D data configuration can be mapped to 1-D using the 1-D Peano-Hilbert space-filling curve, which has demonstrated a 20 dB lower baseline using the method compared with other approaches (cf. SPIE ICA etc. by Hsu & Szu, 2014). This CVM-based method has multiple potential applications; one near-term one is optimizing classification of the EEG signals from a COTS 1-D BCI baseball hat. This can result in a convenient 3-D lab-tethered EEG, configured in a 1-D CVM equiprobable binary vector, and potentially useful for Smartphone wireless display. Longer-range applications include interpreting neural assembly activations via high-density implanted soft, cellular-scale electrodes.

A resource for assessing information processing in the developing brain using EEG and eye tracking

PubMed Central

Langer, Nicolas; Ho, Erica J.; Alexander, Lindsay M.; Xu, Helen Y.; Jozanovic, Renee K.; Henin, Simon; Petroni, Agustin; Cohen, Samantha; Marcelle, Enitan T.; Parra, Lucas C.; Milham, Michael P.; Kelly, Simon P.

2017-01-01

We present a dataset combining electrophysiology and eye tracking intended as a resource for the investigation of information processing in the developing brain. The dataset includes high-density task-based and task-free EEG, eye tracking, and cognitive and behavioral data collected from 126 individuals (ages: 6–44). The task battery spans both the simple/complex and passive/active dimensions to cover a range of approaches prevalent in modern cognitive neuroscience. The active task paradigms facilitate principled deconstruction of core components of task performance in the developing brain, whereas the passive paradigms permit the examination of intrinsic functional network activity during varying amounts of external stimulation. Alongside these neurophysiological data, we include an abbreviated cognitive test battery and questionnaire-based measures of psychiatric functioning. We hope that this dataset will lead to the development of novel assays of neural processes fundamental to information processing, which can be used to index healthy brain development as well as detect pathologic processes. PMID:28398357

A resource for assessing information processing in the developing brain using EEG and eye tracking.

PubMed

Langer, Nicolas; Ho, Erica J; Alexander, Lindsay M; Xu, Helen Y; Jozanovic, Renee K; Henin, Simon; Petroni, Agustin; Cohen, Samantha; Marcelle, Enitan T; Parra, Lucas C; Milham, Michael P; Kelly, Simon P

2017-04-11

We present a dataset combining electrophysiology and eye tracking intended as a resource for the investigation of information processing in the developing brain. The dataset includes high-density task-based and task-free EEG, eye tracking, and cognitive and behavioral data collected from 126 individuals (ages: 6-44). The task battery spans both the simple/complex and passive/active dimensions to cover a range of approaches prevalent in modern cognitive neuroscience. The active task paradigms facilitate principled deconstruction of core components of task performance in the developing brain, whereas the passive paradigms permit the examination of intrinsic functional network activity during varying amounts of external stimulation. Alongside these neurophysiological data, we include an abbreviated cognitive test battery and questionnaire-based measures of psychiatric functioning. We hope that this dataset will lead to the development of novel assays of neural processes fundamental to information processing, which can be used to index healthy brain development as well as detect pathologic processes.

Deep learning for EEG-Based preference classification

NASA Astrophysics Data System (ADS)

Teo, Jason; Hou, Chew Lin; Mountstephens, James

2017-10-01

Electroencephalogram (EEG)-based emotion classification is rapidly becoming one of the most intensely studied areas of brain-computer interfacing (BCI). The ability to passively identify yet accurately correlate brainwaves with our immediate emotions opens up truly meaningful and previously unattainable human-computer interactions such as in forensic neuroscience, rehabilitative medicine, affective entertainment and neuro-marketing. One particularly useful yet rarely explored areas of EEG-based emotion classification is preference recognition [1], which is simply the detection of like versus dislike. Within the limited investigations into preference classification, all reported studies were based on musically-induced stimuli except for a single study which used 2D images. The main objective of this study is to apply deep learning, which has been shown to produce state-of-the-art results in diverse hard problems such as in computer vision, natural language processing and audio recognition, to 3D object preference classification over a larger group of test subjects. A cohort of 16 users was shown 60 bracelet-like objects as rotating visual stimuli on a computer display while their preferences and EEGs were recorded. After training a variety of machine learning approaches which included deep neural networks, we then attempted to classify the users' preferences for the 3D visual stimuli based on their EEGs. Here, we show that that deep learning outperforms a variety of other machine learning classifiers for this EEG-based preference classification task particularly in a highly challenging dataset with large inter- and intra-subject variability.

Interactions between different EEG frequency bands and their effect on alpha-fMRI correlations.

PubMed

de Munck, J C; Gonçalves, S I; Mammoliti, R; Heethaar, R M; Lopes da Silva, F H

2009-08-01

In EEG/fMRI correlation studies it is common to consider the fMRI BOLD as filtered version of the EEG alpha power. Here the question is addressed whether other EEG frequency components may affect the correlation between alpha and BOLD. This was done comparing the statistical parametric maps (SPMs) of three different filter models wherein either the free or the standard hemodynamic response functions (HRF) were used in combination with the full spectral bandwidth of the EEG. EEG and fMRI were co-registered in a 30 min resting state condition in 15 healthy young subjects. Power variations in the delta, theta, alpha, beta and gamma bands were extracted from the EEG and used as regressors in a general linear model. Statistical parametric maps (SPMs) were computed using three different filter models, wherein either the free or the standard hemodynamic response functions (HRF) were used in combination with the full spectral bandwidth of the EEG. Results show that the SPMs of different EEG frequency bands, when significant, are very similar to that of the alpha rhythm. This is true in particular for the beta band, despite the fact that the alpha harmonics were discarded. It is shown that inclusion of EEG frequency bands as confounder in the fMRI-alpha correlation model has a large effect on the resulting SPM, in particular when for each frequency band the HRF is extracted from the data. We conclude that power fluctuations of different EEG frequency bands are mutually highly correlated, and that a multi frequency model is required to extract the SPM of the frequency of interest from EEG/fMRI data. When no constraints are put on the shapes of the HRFs of the nuisance frequencies, the correlation model looses so much statistical power that no correlations can be detected.

Women need more propofol than men during EEG-monitored total intravenous anaesthesia / Frauen benötigen mehr Propofol als Männer während EEG-überwachter total-intravenöser Anästhesie.

PubMed

Haensch, Klaus; Schultz, Arthur; Krauss, Terence; Grouven, Ulrich; Schultz, Barbara

2009-04-01

Gender-related differences in the pharmacology of drugs used in anaesthesiology have been reported by different authors. The aim of this study was to compare propofol dosages in a greater number of male and female patients who had received electroencephalogram (EEG) monitoring to maintain a defined depth of anaesthesia. Data from an EEG-controlled study were analysed with regard to gender differences in the consumption of the short-acting hypnotic propofol during maintenance of total intravenous anaesthesia and with regard to recovery times. The 656 patients (239 male, 417 female) were 15 to 97 years old, underwent different surgical procedures, and received propofol in combination with remifentanil, a short-acting opioid. During the steady-state of anaesthesia the EEG stage D(2)/E(0), which corresponds to deep hypnosis, was the target level (EEG monitor: Narcotrend). Propofol dosages were calculated as mg/kg body weight/h and as mg/kg lean body mass/h. Significantly higher propofol dosages were observed in female patients compared to male patients, especially with lean body mass as a reference parameter. The dosages were characterised by a high interindividual variability. The time from stop of propofol until extubation was significantly shorter in women than in men. The propofol dosage for maintenance of anaesthesia at the EEG level D(2)/E(0) decreased with increasing age.

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

Evaluation of Electroencephalography Source Localization Algorithms with Multiple Cortical Sources.

PubMed

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

2016-01-01

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. 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. 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.

Intelligence measures and stage 2 sleep in typically-developing and autistic children.

PubMed

Tessier, Sophie; Lambert, Andréane; Chicoine, Marjolaine; Scherzer, Peter; Soulières, Isabelle; Godbout, Roger

2015-07-01

The relationship between intelligence measures and 2 EEG measures of non-rapid eye movement sleep, sleep spindles and Sigma activity, was examined in 13 typically-developing (TD) and 13 autistic children with normal IQ and no complaints of poor sleep. Sleep spindles and Sigma EEG activity were computed for frontal (Fp1, Fp2) and central (C3, C4) recording sites. Time in stage 2 sleep and IQ was similar in both groups. Autistic children presented less spindles at Fp2 compared to the TD children. TD children showed negative correlation between verbal IQ and sleep spindle density at Fp2. In the autistic group, verbal and full-scale IQ scores correlated negatively with C3 sleep spindle density. The duration of sleep spindles at Fp1 was shorter in the autistic group than in the TD children. The duration of sleep spindles at C4 was positively correlated with verbal IQ only in the TD group. Fast Sigma EEG activity (13.25-15.75 Hz) was lower at C3 and C4 in autistic children compared to the TD children, particularly in the latter part of the night. Only the TD group showed positive correlation between performance IQ and latter part of the night fast Sigma activity at C4. These results are consistent with a relationship between EEG activity during sleep and cognitive processing in children. The difference between TD and autistic children could derive from dissimilar cortical organization and information processing in these 2 groups. Copyright © 2015. Published by Elsevier B.V.

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

On the identification of sleep stages in mouse electroencephalography time-series.

PubMed

Lampert, Thomas; Plano, Andrea; Austin, Jim; Platt, Bettina

2015-05-15

The automatic identification of sleep stages in electroencephalography (EEG) time-series is a long desired goal for researchers concerned with the study of sleep disorders. This paper presents advances towards achieving this goal, with particular application to EEG time-series recorded from mice. Approaches in the literature apply supervised learning classifiers, however, these do not reach the performance levels required for use within a laboratory. In this paper, detection reliability is increased, most notably in the case of REM stage identification, by naturally decomposing the problem and applying a support vector machine (SVM) based classifier to each of the EEG channels. Their outputs are integrated within a multiple classifier system. Furthermore, there exists no general consensus on the ideal choice of parameter values in such systems. Therefore, an investigation into the effects upon the classification performance is presented by varying parameters such as the epoch length; features size; number of training samples; and the method for calculating the power spectral density estimate. Finally, the results of these investigations are brought together to demonstrate the performance of the proposed classification algorithm in two cases: intra-animal classification and inter-animal classification. It is shown that, within a dataset of 10 EEG recordings, and using less than 1% of an EEG as training data, a mean classification errors of Awake 6.45%, NREM 5.82%, and REM 6.65% (with standard deviations less than 0.6%) are achieved in intra-animal analysis and, when using the equivalent of 7% of one EEG as training data, Awake 10.19%, NREM 7.75%, and REM 17.43% are achieved in inter-animal analysis (with mean standard deviations of 6.42%, 2.89%, and 9.69% respectively). A software package implementing the proposed approach will be made available through Cybula Ltd. Copyright © 2015 Elsevier B.V. All rights reserved.

Evaluation of a Piezoelectric System as an Alternative to Electroencephalogram/ Electromyogram Recordings in Mouse Sleep Studies

PubMed Central

Mang, Géraldine M.; Nicod, Jérôme; Emmenegger, Yann; Donohue, Kevin D.; O'Hara, Bruce F.; Franken, Paul

2014-01-01

Study Objectives: Traditionally, sleep studies in mammals are performed using electroencephalogram/electromyogram (EEG/EMG) recordings to determine sleep-wake state. In laboratory animals, this requires surgery and recovery time and causes discomfort to the animal. In this study, we evaluated the performance of an alternative, noninvasive approach utilizing piezoelectric films to determine sleep and wakefulness in mice by simultaneous EEG/EMG recordings. The piezoelectric films detect the animal's movements with high sensitivity and the regularity of the piezo output signal, related to the regular breathing movements characteristic of sleep, serves to automatically determine sleep. Although the system is commercially available (Signal Solutions LLC, Lexington, KY), this is the first statistical validation of various aspects of sleep. Design: EEG/EMG and piezo signals were recorded simultaneously during 48 h. Setting: Mouse sleep laboratory. Participants: Nine male and nine female CFW outbred mice. Interventions: EEG/EMG surgery. Measurements and Results: The results showed a high correspondence between EEG/EMG-determined and piezo-determined total sleep time and the distribution of sleep over a 48-h baseline recording with 18 mice. Moreover, the piezo system was capable of assessing sleep quality (i.e., sleep consolidation) and interesting observations at transitions to and from rapid eye movement sleep were made that could be exploited in the future to also distinguish the two sleep states. Conclusions: The piezo system proved to be a reliable alternative to electroencephalogram/electromyogram recording in the mouse and will be useful for first-pass, large-scale sleep screens for genetic or pharmacological studies. Citation: Mang GM, Nicod J, Emmenegger Y, Donohue KD, O'Hara BF, Franken P. Evaluation of a piezoelectric system as an alternative to electroencephalogram/electromyogram recordings in mouse sleep studies. SLEEP 2014;37(8):1383-1392. PMID:25083019

Scale-specific effects: A report on multiscale analysis of acupunctured EEG in entropy and power

NASA Astrophysics Data System (ADS)

Song, Zhenxi; Deng, Bin; Wei, Xile; Cai, Lihui; Yu, Haitao; Wang, Jiang; Wang, Ruofan; Chen, Yingyuan

2018-02-01

Investigating acupuncture effects contributes to improving clinical application and understanding neuronal dynamics under external stimulation. In this report, we recorded electroencephalography (EEG) signals evoked by acupuncture at ST36 acupoint with three stimulus frequencies of 50, 100 and 200 times per minutes, and selected non-acupuncture EEGs as the control group. Multiscale analyses were introduced to investigate the possible acupuncture effects on complexity and power in multiscale level. Using multiscale weighted-permutation entropy, we found the significant effects on increased complexity degree in EEG signals induced by acupuncture. The comparison of three stimulation manipulations showed that 100 times/min generated most obvious effects, and affected most cortical regions. By estimating average power spectral density, we found decreased power induced by acupuncture. The joint distribution of entropy and power indicated an inverse correlation, and this relationship was weakened by acupuncture effects, especially under the manipulation of 100 times/min frequency. Above findings are more evident and stable in large scales than small scales, which suggests that multiscale analysis allows evaluating significant effects in specific scale and enables to probe the inherent characteristics underlying physiological signals.

Electroencephalogram-based indices applied to dogs' depth of anaesthesia monitoring.

PubMed

Brás, S; Georgakis, A; Ribeiro, L; Ferreira, D A; Silva, A; Antunes, L; Nunes, C S

2014-12-01

Hypnotic drug administration causes alterations in the electroencephalogram (EEG) in a dose-dependent manner. These changes cannot be identified easily in the raw EEG, therefore EEG based indices were adopted for assessing depth of anaesthesia (DoA). This study examines several indices for estimating dogs' DoA. Data (EEG, clinical end-points) were collected from 8 dogs anaesthetized with propofol. EEG was initially collected without propofol. Then, 100 ml h⁻¹ (1000 mg h⁻¹) of propofol 1% infusion rate was administered until a deep anaesthetic stage was reached. The infusion rate was temporarily increased to 200 ml h⁻¹ (2000 mg h⁻¹) to achieve 80% of burst suppression. The index performance was accessed by correlation coefficient with the propofol concentrations, and prediction probability with the anaesthetic clinical end-points. The temporal entropy and the averaged instantaneous frequency were the best indices because they exhibit: (a) strong correlations with propofol concentrations, (b) high probabilities of predicting anaesthesia clinical end-points. Copyright © 2014 Elsevier Ltd. All rights reserved.

A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study.

PubMed

Duffy, Frank H; Als, Heidelise

2012-06-26

The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI) studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG) coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact. Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD); 571 children were neuro-typical controls (C). After artifact management, principal components analysis (PCA) identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984). Discriminant function analysis (DFA) determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls. Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (P < 0.0001). Ten randomly generated split half replications demonstrated high-average classification success (C, 88.5%; ASD, 86.0%). Still higher success was obtained in the more restricted age sub-samples using the jackknifing technique: 2- to 4-year-olds (C, 90.6%; ASD, 98.1%); 4- to 6-year-olds (C, 90.9%; ASD 99.1%); and 6- to 12-year-olds (C, 98.7%; ASD, 93.9%). Coherence loadings demonstrated reduced short-distance and reduced, as well as increased, long-distance coherences for the ASD-groups, when compared to the controls. Average spectral loading per factor was wide (10.1 Hz). Classification success suggests a stable coherence loading pattern that differentiates ASD- from C-group subjects. This might constitute an EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range of factor loadings may suggest over-damped neural networks.

Sleep Dysfunction and EEG Alterations in Mice Overexpressing Alpha-Synuclein

PubMed Central

McDowell, Kimberly A.; Shin, David; Roos, Kenneth P.; Chesselet, Marie-Françoise

2018-01-01

Background: Sleep disruptions occur early and frequently in Parkinson’s disease (PD). PD patients also show a slowing of resting state activity. Alpha-synuclein is causally linked to PD and accumulates in sleep-related brain regions. While sleep problems occur in over 75% of PD patients and severely impact the quality of life of patients and caregivers, their study is limited by a paucity of adequate animal models. Objective: The objective of this study was to determine whether overexpression of wildtype alpha-synuclein could lead to alterations in sleep patterns reminiscent of those observed in PD by measuring sleep/wake activity with rigorous quantitative methods in a well-characterized genetic mouse model. Methods: At 10 months of age, mice expressing human wildtype alpha-synuclein under the Thy-1 promoter (Thy1-aSyn) and wildtype littermates underwent the subcutaneous implantation of a telemetry device (Data Sciences International) for the recording of electromyograms (EMG) and electroencephalograms (EEG) in freely moving animals. Surgeries and data collection were performed without knowledge of mouse genotype. Results: Thy1-aSyn mice showed increased non-rapid eye movement sleep during their quiescent phase, increased active wake during their active phase, and decreased rapid eye movement sleep over a 24-h period, as well as a shift in the density of their EEG power spectra toward lower frequencies with a significant decrease in gamma power during wakefulness. Conclusions: Alpha-synuclein overexpression in mice produces sleep disruptions and altered oscillatory EEG activity reminiscent of PD, and this model provides a novel platform to assess mechanisms and therapeutic strategies for sleep dysfunction in PD. PMID:24867919

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.

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.

Clinical review: Continuous and simplified electroencephalography to monitor brain recovery after cardiac arrest

PubMed Central

2013-01-01

There has been a dramatic change in hospital care of cardiac arrest survivors in recent years, including the use of target temperature management (hypothermia). Clinical signs of recovery or deterioration, which previously could be observed, are now concealed by sedation, analgesia, and muscle paralysis. Seizures are common after cardiac arrest, but few centers can offer high-quality electroencephalography (EEG) monitoring around the clock. This is due primarily to its complexity and lack of resources but also to uncertainty regarding the clinical value of monitoring EEG and of treating post-ischemic electrographic seizures. Thanks to technical advances in recent years, EEG monitoring has become more available. Large amounts of EEG data can be linked within a hospital or between neighboring hospitals for expert opinion. Continuous EEG (cEEG) monitoring provides dynamic information and can be used to assess the evolution of EEG patterns and to detect seizures. cEEG can be made more simple by reducing the number of electrodes and by adding trend analysis to the original EEG curves. In our version of simplified cEEG, we combine a reduced montage, displaying two channels of the original EEG, with amplitude-integrated EEG trend curves (aEEG). This is a convenient method to monitor cerebral function in comatose patients after cardiac arrest but has yet to be validated against the gold standard, a multichannel cEEG. We recently proposed a simplified system for interpreting EEG rhythms after cardiac arrest, defining four major EEG patterns. In this topical review, we will discuss cEEG to monitor brain function after cardiac arrest in general and how a simplified cEEG, with a reduced number of electrodes and trend analysis, may facilitate and improve care. PMID:23876221

Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively

PubMed Central

Shimamoto, Shoichi; Waldman, Zachary J.; Orosz, Iren; Song, Inkyung; Bragin, Anatol; Fried, Itzhak; Engel, Jerome; Staba, Richard; Sharan, Ashwini; Wu, Chengyuan; Sperling, Michael R.; Weiss, Shennan A.

2018-01-01

Objective To develop and validate a detector that identifies ripple (80–200 Hz) events in intracranial EEG (iEEG) recordings in a referential montage and utilizes independent component analysis (ICA) to eliminate or reduce high-frequency artifact contamination. Also, investigate the correspondence of detected ripples and the seizure onset zone (SOZ). Methods iEEG recordings from 16 patients were first band-pass filtered (80–600 Hz) and Infomax ICA was next applied to derive the first independent component (IC1). IC1 was subsequently pruned, and an artifact index was derived to reduce the identification of high-frequency events introduced by the reference electrode signal. A Hilbert detector identified ripple events in the processed iEEG recordings using amplitude and duration criteria. The identified ripple events were further classified and characterized as true or false ripple on spikes, or ripples on oscillations by utilizing a topographical analysis to their time-frequency plot, and confirmed by visual inspection. Results The signal to noise ratio was improved by pruning IC1. The precision of the detector for ripple events was 91.27 ± 4.3%, and the sensitivity of the detector was 79.4 ± 3.0% (N = 16 patients, 5842 ripple events). The sensitivity and precision of the detector was equivalent in iEEG recordings obtained during sleep or intra-operatively. Across all the patients, true ripple on spike rates and also the rates of false ripple on spikes, that were generated due to filter ringing, classified the seizure onset zone (SOZ) with an area under the receiver operating curve (AUROC) of >76%. The magnitude and spectral content of true ripple on spikes generated in the SOZ was distinct as compared with the ripples generated in the NSOZ (p < .001). Conclusions Utilizing ICA to analyze iEEG recordings in referential montage provides many benefits to the study of high-frequency oscillations. The ripple rates and properties defined using this approach may accurately delineate the seizure onset zone. Significance Strategies to improve the spatial resolution of intracranial EEG and reduce artifact can help improve the clinical utility of HFO biomarkers. PMID:29113719

Long-Term Clinical and Electroencephalography (EEG) Consequences of Idiopathic Partial Epilepsies.

PubMed

Dörtcan, Nimet; Tekin Guveli, Betul; Dervent, Aysin

2016-05-03

BACKGROUND Idiopathic partial epilepsies of childhood (IPE) affect a considerable proportion of children. Three main electroclinical syndromes of IPE are the Benign Childhood Epilepsy with Centro-temporal Spikes (BECTS), Panayiotopoulos Syndrome (PS), and Childhood Epilepsy with Occipital Paroxysms (CEOP). In this study we investigated the long-term prognosis of patients with IPE and discussed the semiological and electroencephalography (EEG) data in terms of syndromic characteristics. MATERIAL AND METHODS This study included a group of consecutive patients with IPE who had been followed since 1990. Demographic and clinical variables were investigated. Patients were divided into 3 groups - A: Cases suitable for a single IPE (BECTS, PS and CEOP); B: cases with intermediate characteristics within IPEs; and C: cases with both IPE and IGE characteristics. Long-term data regarding the individual seizure types and EEG findings were re-evaluated. RESULTS A total of 61 patients were included in the study. Mean follow-up duration was 7.8 ± 4.50 years. The mean age at onset of seizures was 7.7 years. There were 40 patients in group A 40, 14 in group B, and 7 in group C. Seizure and EEG characteristics were also explored independently from the syndromic approach. Incidence of autonomic seizures is considerably high at 2-5 years and incidence of oromotor seizures is high at age 9-11 years. The EEG is most abnormal at 6-8 years. The vast majority (86%) of epileptic activity (EA) with parietooccipital is present at 2-5 years, whereas EA with fronto-temporal or multiple sites become more abundant between ages 6 and 11. CONCLUSIONS Results of the present study provide support for the age-related characteristics of the seizures and EEGs in IPE syndromes. Acknowledgement of those phenomena may improve the management of IPEs and give a better estimate of the future consequences.

Somatosensory-evoked spikes on electroencephalography (EEG): longitudinal clinical and EEG aspects in 313 children.

PubMed

Fonseca, Lineu Corrêa; Tedrus, Gloria M A S

2012-01-01

Somatosensory-evoked spikes (ESp) are high-voltage potentials registered on the EEG, which accompany each of the percussions on the feet or hands. The objective of this research was to study the longitudinal clinical and EEG aspects of children with ESp. A total of 313 children, 53.7% male, showing ESp on the EEG and with an average initial age of 6.82 (range from 2 to 14 years) were followed for a mean period of 35.7 months. In the initial evaluation, 118 (37.7%) had a history of nonfebrile epileptic seizures (ES). Epileptiform activity (EA) was observed on the EEG in 61% and showed a significantly greater occurrence in children with ES than in those without (P = .000). Of the 118 showing seizures from the start, 53 (44.9%) continued to have seizures; of the 195 without seizures at the start, only 13 (6.67%) developed them. Thus, only 66 (21.1%) children showed ES during the follow-up. ESp disappeared in 237 (75.7%) cases and EA in 221 (70.6%). In the children with ES, it was found that the presence of EA on the first EEG did not indicate continuation of the ES throughout the remaining period, while the 13 children who presented their first ES in a later period showed a greater occurrence of EA on the initial EEG than those who did not develop ES (P = .001). Evidence of brain injury was observed in 43 (13.7%) children and was associated with a greater continuity of the ES during the study (P = .018). ESp, EA, and ES tend to disappear, suggesting an age-dependent phenomenon. The finding of ESp, particularly in the absence of any evidence of brain injury, indicates a low association with ES and benign outcome.

Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes.

PubMed

Matthews, R; Turner, P J; McDonald, N J; Ermolaev, K; Manus, T; Shelby, R A; Steindorf, M

2008-01-01

This paper describes a compact, lightweight and ultra-low power ambulatory wireless EEG system based upon QUASAR's innovative noninvasive bioelectric sensor technologies. The sensors operate through hair without skin preparation or conductive gels. Mechanical isolation built into the harness permits the recording of high quality EEG data during ambulation. Advanced algorithms developed for this system permit real time classification of workload during subject motion. Measurements made using the EEG system during ambulation are presented, including results for real time classification of subject workload.

Characterization of dynamic changes of current source localization based on spatiotemporal fMRI constrained EEG source imaging

NASA Astrophysics Data System (ADS)

Nguyen, Thinh; Potter, Thomas; Grossman, Robert; Zhang, Yingchun

2018-06-01

Objective. Neuroimaging has been employed as a promising approach to advance our understanding of brain networks in both basic and clinical neuroscience. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) represent two neuroimaging modalities with complementary features; EEG has high temporal resolution and low spatial resolution while fMRI has high spatial resolution and low temporal resolution. Multimodal EEG inverse methods have attempted to capitalize on these properties but have been subjected to localization error. The dynamic brain transition network (DBTN) approach, a spatiotemporal fMRI constrained EEG source imaging method, has recently been developed to address these issues by solving the EEG inverse problem in a Bayesian framework, utilizing fMRI priors in a spatial and temporal variant manner. This paper presents a computer simulation study to provide a detailed characterization of the spatial and temporal accuracy of the DBTN method. Approach. Synthetic EEG data were generated in a series of computer simulations, designed to represent realistic and complex brain activity at superficial and deep sources with highly dynamical activity time-courses. The source reconstruction performance of the DBTN method was tested against the fMRI-constrained minimum norm estimates algorithm (fMRIMNE). The performances of the two inverse methods were evaluated both in terms of spatial and temporal accuracy. Main results. In comparison with the commonly used fMRIMNE method, results showed that the DBTN method produces results with increased spatial and temporal accuracy. The DBTN method also demonstrated the capability to reduce crosstalk in the reconstructed cortical time-course(s) induced by neighboring regions, mitigate depth bias and improve overall localization accuracy. Significance. The improved spatiotemporal accuracy of the reconstruction allows for an improved characterization of complex neural activity. This improvement can be extended to any subsequent brain connectivity analyses used to construct the associated dynamic brain networks.

Aerosol Drug Delivery During Noninvasive Positive Pressure Ventilation: Effects of Intersubject Variability and Excipient Enhanced Growth

PubMed Central

Walenga, Ross L.; Kaviratna, Anubhav; Hindle, Michael

2017-01-01

Abstract Background: Nebulized aerosol drug delivery during the administration of noninvasive positive pressure ventilation (NPPV) is commonly implemented. While studies have shown improved patient outcomes for this therapeutic approach, aerosol delivery efficiency is reported to be low with high variability in lung-deposited dose. Excipient enhanced growth (EEG) aerosol delivery is a newly proposed technique that may improve drug delivery efficiency and reduce intersubject aerosol delivery variability when coupled with NPPV. Materials and Methods: A combined approach using in vitro experiments and computational fluid dynamics (CFD) was used to characterize aerosol delivery efficiency during NPPV in two new nasal cavity models that include face mask interfaces. Mesh nebulizer and in-line dry powder inhaler (DPI) sources of conventional and EEG aerosols were both considered. Results: Based on validated steady-state CFD predictions, EEG aerosol delivery improved lung penetration fraction (PF) values by factors ranging from 1.3 to 6.4 compared with conventional-sized aerosols. Furthermore, intersubject variability in lung PF was very high for conventional aerosol sizes (relative differences between subjects in the range of 54.5%–134.3%) and was reduced by an order of magnitude with the EEG approach (relative differences between subjects in the range of 5.5%–17.4%). Realistic in vitro experiments of cyclic NPPV demonstrated similar trends in lung delivery to those observed with the steady-state simulations, but with lower lung delivery efficiencies. Reaching the lung delivery efficiencies reported with the steady-state simulations of 80%–90% will require synchronization of aerosol administration during inspiration and reducing the size of the EEG aerosol delivery unit. Conclusions: The EEG approach enabled high-efficiency lung delivery of aerosols administered during NPPV and reduced intersubject aerosol delivery variability by an order of magnitude. Use of an in-line DPI device that connects to the NPPV mask appears to be a convenient method to rapidly administer an EEG aerosol and synchronize the delivery with inspiration. PMID:28075194

Neurofeedback training aimed to improve focused attention and alertness in children with ADHD: a study of relative power of EEG rhythms using custom-made software application.

PubMed

Hillard, Brent; El-Baz, Ayman S; Sears, Lonnie; Tasman, Allan; Sokhadze, Estate M

2013-07-01

Neurofeedback is a nonpharmacological treatment for attention-deficit hyperactivity disorder (ADHD). We propose that operant conditioning of electroencephalogram (EEG) in neurofeedback training aimed to mitigate inattention and low arousal in ADHD, will be accompanied by changes in EEG bands' relative power. Patients were 18 children diagnosed with ADHD. The neurofeedback protocol ("Focus/Alertness" by Peak Achievement Trainer) has a focused attention and alertness training mode. The neurofeedback protocol provides one for Focus and one for Alertness. This does not allow for collecting information regarding changes in specific EEG bands (delta, theta, alpha, low and high beta, and gamma) power within the 2 to 45 Hz range. Quantitative EEG analysis was completed on each of twelve 25-minute-long sessions using a custom-made MatLab application to determine the relative power of each of the aforementioned EEG bands throughout each session, and from the first session to the last session. Additional statistical analysis determined significant changes in relative power within sessions (from minute 1 to minute 25) and between sessions (from session 1 to session 12). Analysis was of relative power of theta, alpha, low and high beta, theta/alpha, theta/beta, and theta/low beta and theta/high beta ratios. Additional secondary measures of patients' post-neurofeedback outcomes were assessed, using an audiovisual selective attention test (IVA + Plus) and behavioral evaluation scores from the Aberrant Behavior Checklist. Analysis of data computed in the MatLab application, determined that theta/low beta and theta/alpha ratios decreased significantly from session 1 to session 12, and from minute 1 to minute 25 within sessions. The findings regarding EEG changes resulting from brain wave self-regulation training, along with behavioral evaluations, will help elucidate neural mechanisms of neurofeedback aimed to improve focused attention and alertness in ADHD.

Studentized continuous wavelet transform (t-CWT) in the analysis of individual ERPs: real and simulated EEG data

PubMed Central

Real, Ruben G. L.; Kotchoubey, Boris; Kübler, Andrea

2014-01-01

This study aimed at evaluating the performance of the Studentized Continuous Wavelet Transform (t-CWT) as a method for the extraction and assessment of event-related brain potentials (ERP) in data from a single subject. Sensitivity, specificity, positive (PPV) and negative predictive values (NPV) of the t-CWT were assessed and compared to a variety of competing procedures using simulated EEG data at six low signal-to-noise ratios. Results show that the t-CWT combines high sensitivity and specificity with favorable PPV and NPV. Applying the t-CWT to authentic EEG data obtained from 14 healthy participants confirmed its high sensitivity. The t-CWT may thus be well suited for the assessment of weak ERPs in single-subject settings. PMID:25309308

Studentized continuous wavelet transform (t-CWT) in the analysis of individual ERPs: real and simulated EEG data.

PubMed

Real, Ruben G L; Kotchoubey, Boris; Kübler, Andrea

2014-01-01

This study aimed at evaluating the performance of the Studentized Continuous Wavelet Transform (t-CWT) as a method for the extraction and assessment of event-related brain potentials (ERP) in data from a single subject. Sensitivity, specificity, positive (PPV) and negative predictive values (NPV) of the t-CWT were assessed and compared to a variety of competing procedures using simulated EEG data at six low signal-to-noise ratios. Results show that the t-CWT combines high sensitivity and specificity with favorable PPV and NPV. Applying the t-CWT to authentic EEG data obtained from 14 healthy participants confirmed its high sensitivity. The t-CWT may thus be well suited for the assessment of weak ERPs in single-subject settings.

Hyperglycemia is associated with simultaneous alterations in electrical brain activity in youths with type 1 diabetes mellitus.

PubMed

Rachmiel, M; Cohen, M; Heymen, E; Lezinger, M; Inbar, D; Gilat, S; Bistritzer, T; Leshem, G; Kan-Dror, E; Lahat, E; Ekstein, D

2016-02-01

To assess the association between hyperglycemia and electrical brain activity in type 1 diabetes mellitus (T1DM). Nine youths with T1DM were monitored simultaneously and continuously by EEG and continuous glucose monitor system, for 40 h. EEG powers of 0.5-80 Hz frequency bands in all the different brain regions were analyzed according to interstitial glucose concentration (IGC) ranges of 4-11 mmol/l, 11-15.5 mmol/l and >15.5 mmol/l. Analysis of variance was used to examine the differences in EEG power of each frequency band between the subgroups of IGC. Analysis was performed separately during wakefulness and sleep, controlling for age, gender and HbA1c. Mean IGC was 11.49 ± 5.26 mmol/l in 1253 combined measurements. IGC>15.5 mmol/l compared to 4-11 mmol/l was associated during wakefulness with increased EEG power of low frequencies and with decreased EEG power of high frequencies. During sleep, it was associated with increased EEG power of low frequencies in all brain areas and of high frequencies in frontal and central areas. Asymptomatic transient hyperglycemia in youth with T1DM is associated with simultaneous alterations in electrical brain activity during wakefulness and sleep. The clinical implications of immediate electrical brain alterations under hyperglycemia need to be studied and may lead to adaptations of management. Copyright © 2015. Published by Elsevier Ireland Ltd.

Evaluation of a piezoelectric system as an alternative to electroencephalogram/ electromyogram recordings in mouse sleep studies.

PubMed

Mang, Géraldine M; Nicod, Jérôme; Emmenegger, Yann; Donohue, Kevin D; O'Hara, Bruce F; Franken, Paul

2014-08-01

Traditionally, sleep studies in mammals are performed using electroencephalogram/electromyogram (EEG/EMG) recordings to determine sleep-wake state. In laboratory animals, this requires surgery and recovery time and causes discomfort to the animal. In this study, we evaluated the performance of an alternative, noninvasive approach utilizing piezoelectric films to determine sleep and wakefulness in mice by simultaneous EEG/EMG recordings. The piezoelectric films detect the animal's movements with high sensitivity and the regularity of the piezo output signal, related to the regular breathing movements characteristic of sleep, serves to automatically determine sleep. Although the system is commercially available (Signal Solutions LLC, Lexington, KY), this is the first statistical validation of various aspects of sleep. EEG/EMG and piezo signals were recorded simultaneously during 48 h. Mouse sleep laboratory. Nine male and nine female CFW outbred mice. EEG/EMG surgery. The results showed a high correspondence between EEG/EMG-determined and piezo-determined total sleep time and the distribution of sleep over a 48-h baseline recording with 18 mice. Moreover, the piezo system was capable of assessing sleep quality (i.e., sleep consolidation) and interesting observations at transitions to and from rapid eye movement sleep were made that could be exploited in the future to also distinguish the two sleep states. The piezo system proved to be a reliable alternative to electroencephalogram/electromyogram recording in the mouse and will be useful for first-pass, large-scale sleep screens for genetic or pharmacological studies. Mang GM, Nicod J, Emmenegger Y, Donohue KD, O'Hara BF, Franken P. Evaluation of a piezoelectric system as an alternative to electroencephalogram/electromyogram recordings in mouse sleep studies.

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

Ordinal patterns in epileptic brains: Analysis of intracranial EEG and simultaneous EEG-fMRI

NASA Astrophysics Data System (ADS)

Rummel, C.; Abela, E.; Hauf, M.; Wiest, R.; Schindler, K.

2013-06-01

Epileptic seizures are associated with high behavioral stereotypy of the patients. In the EEG of epilepsy patients characteristic signal patterns can be found during and between seizures. Here we use ordinal patterns to analyze EEGs of epilepsy patients and quantify the degree of signal determinism. Besides relative signal redundancy and the fraction of forbidden patterns we introduce the fraction of under-represented patterns as a new measure. Using the logistic map, parameter scans are performed to explore the sensitivity of the measures to signal determinism. Thereafter, application is made to two types of EEGs recorded in two epilepsy patients. Intracranial EEG shows pronounced determinism peaks during seizures. Finally, we demonstrate that ordinal patterns may be useful for improving analysis of non-invasive simultaneous EEG-fMRI.

Detection of artifacts from high energy bursts in neonatal EEG.

PubMed

Bhattacharyya, Sourya; Biswas, Arunava; Mukherjee, Jayanta; Majumdar, Arun Kumar; Majumdar, Bandana; Mukherjee, Suchandra; Singh, Arun Kumar

2013-11-01

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well. © 2013 Elsevier Ltd. All rights reserved.

Mouse EEG spike detection based on the adapted continuous wavelet transform

NASA Astrophysics Data System (ADS)

Tieng, Quang M.; Kharatishvili, Irina; Chen, Min; Reutens, David C.

2016-04-01

Objective. Electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. Interictal spikes on EEG are used to monitor the development of epilepsy and the effects of drug therapy. EEG recordings are generally long and the data voluminous. Thus developing a sensitive and reliable automated algorithm for analyzing EEG data is necessary. Approach. A new algorithm for detecting and classifying interictal spikes in mouse EEG recordings is proposed, based on the adapted continuous wavelet transform (CWT). The construction of the adapted mother wavelet is founded on a template obtained from a sample comprising the first few minutes of an EEG data set. Main Result. The algorithm was tested with EEG data from a mouse model of epilepsy and experimental results showed that the algorithm could distinguish EEG spikes from other transient waveforms with a high degree of sensitivity and specificity. Significance. Differing from existing approaches, the proposed approach combines wavelet denoising, to isolate transient signals, with adapted CWT-based template matching, to detect true interictal spikes. Using the adapted wavelet constructed from a predefined template, the adapted CWT is calculated on small EEG segments to fit dynamical changes in the EEG recording.

Non-restraining EEG Radiotelemetry: Epidural and Deep Intracerebral Stereotaxic EEG Electrode Placement.

PubMed

Papazoglou, Anna; Lundt, Andreas; Wormuth, Carola; Ehninger, Dan; Henseler, Christina; Soós, Julien; Broich, Karl; Weiergräber, Marco

2016-06-25

Implantable EEG radiotelemetry is of central relevance in the neurological characterization of transgenic mouse models of neuropsychiatric and neurodegenerative diseases as well as epilepsies. This powerful technique does not only provide valuable insights into the underlying pathophysiological mechanisms, i.e., the etiopathogenesis of CNS related diseases, it also facilitates the development of new translational, i.e., therapeutic approaches. Whereas competing techniques that make use of recorder systems used in jackets or tethered systems suffer from their unphysiological restraining to semi-restraining character, radiotelemetric EEG recordings overcome these disadvantages. Technically, implantable EEG radiotelemetry allows for precise and highly sensitive measurement of epidural and deep, intracerebral EEGs under various physiological and pathophysiological conditions. First, we present a detailed protocol of a straight forward, successful, quick and efficient technique for epidural (surface) EEG recordings resulting in high-quality electrocorticograms. Second, we demonstrate how to implant deep, intracerebral EEG electrodes, e.g., in the hippocampus (electrohippocampogram). For both approaches, a computerized 3D stereotaxic electrode implantation system is used. The radiofrequency transmitter itself is implanted into a subcutaneous pouch in both mice and rats. Special attention also has to be paid to pre-, peri- and postoperative treatment of the experimental animals. Preoperative preparation of mice and rats, suitable anesthesia as well as postoperative treatment and pain management are described in detail.

Propofol, more than halothane, depresses electroencephalographic activation resulting from electrical stimulation in reticular formation.

PubMed

Antognini, J F; Bravo, E; Atherley, R; Carstens, E

2006-09-01

Halothane and propofol depress the central nervous system, and this is partly manifested by a decrease in electroencephalographic (EEG) activity. Little work has been performed to determine the differences between these anesthetics with regard to their effects on evoked EEG activity. We examined the effects of halothane and propofol on EEG responses to electrical stimulation of the reticular formation. Rats (n= 12) were anesthetized with either halothane or propofol, and EEG responses were recorded before and after electrical stimulation of the reticular formation. Two anesthetic concentrations were used (0.8 and 1.2 times the amount needed to prevent gross, purposeful movement in response to supramaximal noxious stimulation), and both anesthetics were studied in each rat using a cross-over design. Electrical stimulation in the reticular formation increased the spectral edge (SEF) and median edge (MEF) frequencies by approximately 1-2 Hz during halothane anesthesia at low and high concentrations. During propofol anesthesia, MEF increased at the low propofol infusion rate, but SEF was unaffected. At the high propofol infusion rate, SEF and MEF decreased following electrical stimulation in the reticular formation. At immobilizing concentrations, propofol produces a larger decrease than halothane in EEG responses to reticular formation stimulation, consistent with propofol having a more profound depressant effect on cortical and subcortical structures.

The Dynamics of Visual Experience, an EEG Study of Subjective Pattern Formation

PubMed Central

Elliott, Mark A.; Twomey, Deirdre; Glennon, Mark

2012-01-01

Background Since the origin of psychological science a number of studies have reported visual pattern formation in the absence of either physiological stimulation or direct visual-spatial references. Subjective patterns range from simple phosphenes to complex patterns but are highly specific and reported reliably across studies. Methodology/Principal Findings Using independent-component analysis (ICA) we report a reduction in amplitude variance consistent with subjective-pattern formation in ventral posterior areas of the electroencephalogram (EEG). The EEG exhibits significantly increased power at delta/theta and gamma-frequencies (point and circle patterns) or a series of high-frequency harmonics of a delta oscillation (spiral patterns). Conclusions/Significance Subjective-pattern formation may be described in a way entirely consistent with identical pattern formation in fluids or granular flows. In this manner, we propose subjective-pattern structure to be represented within a spatio-temporal lattice of harmonic oscillations which bind topographically organized visual-neuronal assemblies by virtue of low frequency modulation. PMID:22292053

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.

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.

Scatterplot analysis of EEG slow-wave magnitude and heart rate variability: an integrative exploration of cerebral cortical and autonomic functions.

PubMed

Kuo, Terry B J; Yang, Cheryl C H

2004-06-15

To explore interactions between cerebral cortical and autonomic functions in different sleep-wake states. Active waking (AW), quiet sleep (QS), and paradoxical sleep (PS) of adult male Wistar-Kyoto rats (WKY) on their daytime sleep were compared. Ten WKY. All rats had electrodes implanted for polygraphic recordings. One week later, a 6-hour daytime sleep-wakefulness recording session was performed. A scatterplot analysis of electroencephalogram (EEG) slow-wave magnitude (0.5-4 Hz) and heart rate variability (HRV) was applied in each rat. The EEG slow-wave-RR interval scatterplot from all of the recordings revealed a propeller-like pattern. If the scatterplot was divided into AW, PS, and QS according to the corresponding EEG mean power frequency and nuchal electromyogram, the EEG slow wave-RR interval relationship became nil, negative, and positive for AW, PS, and QS, respectively. A significant negative relationship was found for EEG slow-wave and high-frequency power of HRV (HF) coupling during PS and for EEG slow wave and low-frequency power of HRV to HF ratio (LF/HF) coupling during QS. The optimal time lags for the slow wave-LF/HF relationship were different between PS and QS. Bradycardia noted in QS and PS was related to sympathetic suppression and vagal excitation, respectively. The EEG slow wave-HRV scatterplot may provide unique insights into studies of sleep, and such a relationship may delineate the sleep-state-dependent fluctuations in autonomic nervous system activity.

Computational Electromagnetic Analysis in a Human Head Model with EEG Electrodes and Leads Exposed to RF-Field Sources at 915 MHz and 1748 MHz

PubMed Central

Angelone, Leonardo M.; Bit-Babik, Giorgi; Chou, Chung-Kwang

2010-01-01

An electromagnetic analysis of a human head with EEG electrodes and leads exposed to RF-field sources was performed by means of Finite-Difference Time-Domain simulations on a 1-mm3 MRI-based human head model. RF-field source models included a half-wave dipole, a patch antenna, and a realistic CAD-based mobile phone at 915 MHz and 1748 MHz. EEG electrodes/leads models included two configurations of EEG leads, both a standard 10–20 montage with 19 electrodes and a 32-electrode cap, and metallic and high resistive leads. Whole-head and peak 10-g average SAR showed less than 20% changes with and without leads. Peak 1-g and 10-g average SARs were below the ICNIRP and IEEE guideline limits. Conversely, a comprehensive volumetric assessment of changes in the RF field with and without metallic EEG leads showed an increase of two orders of magnitude in single-voxel power absorption in the epidermis and a 40-fold increase in the brain during exposure to the 915 MHz mobile phone. Results varied with the geometry and conductivity of EEG electrodes/leads. This enhancement confirms the validity of the question whether any observed effects in studies involving EEG recordings during RF-field exposure are directly related to the RF fields generated by the source or indirectly to the RF-field-induced currents due to the presence of conductive EEG leads. PMID:20681803

Epileptogenic developmental venous anomaly: insights from simultaneous EEG/fMRI.

PubMed

Scheidegger, Olivier; Wiest, Roland; Jann, Kay; König, Thomas; Meyer, Klaus; Hauf, Martinus

2013-04-01

Developmental venous anomalies (DVAs) are associated with epileptic seizures; however, the role of DVA in the epileptogenesis is still not established. Simultaneous interictal electroencephalogram/functional magnetic resonance imaging (EEG/fMRI) recordings provide supplementary information to electroclinical data about the epileptic generators, and thus aid in the differentiation of clinically equivocal epilepsy syndromes. The main objective of our study was to characterize the epileptic network in a patient with DVA and epilepsy by simultaneous EEG/fMRI recordings. A 17-year-old woman with recently emerging generalized tonic-clonic seizures, and atypical generalized discharges, was investigated using simultaneous EEG/fMRI at the university hospital. Previous high-resolution MRI showed no structural abnormalities, except a DVA in the right frontal operculum. Interictal EEG recordings showed atypical generalized discharges, corresponding to positive focal blood oxygen level dependent (BOLD) correlates in the right frontal operculum, a region drained by the DVA. Additionally, widespread cortical bilateral negative BOLD correlates in the frontal and parietal lobes were delineated, resembling a generalized epileptic network. The EEG/fMRI recordings support a right frontal lobe epilepsy, originating in the vicinity of the DVA, propagating rapidly to both frontal and parietal lobes, as expressed on the scalp EEG by secondary bilateral synchrony. The DVA may be causative of focal epilepsies in cases where no concomitant epileptogenic lesions can be detected. Advanced imaging techniques, such as simultaneous EEG/fMRI, may thus aid in the differentiation of clinically equivocal epilepsy syndromes.

Resting state glutamate predicts elevated pre-stimulus alpha during self-relatedness: A combined EEG-MRS study on "rest-self overlap".

PubMed

Bai, Yu; Nakao, Takashi; Xu, Jiameng; Qin, Pengmin; Chaves, Pedro; Heinzel, Alexander; Duncan, Niall; Lane, Timothy; Yen, Nai-Shing; Tsai, Shang-Yueh; Northoff, Georg

2016-01-01

Recent studies have demonstrated neural overlap between resting state activity and self-referential processing. This "rest-self" overlap occurs especially in anterior cortical midline structures like the perigenual anterior cingulate cortex (PACC). However, the exact neurotemporal and biochemical mechanisms remain to be identified. Therefore, we conducted a combined electroencephalography (EEG)-magnetic resonance spectroscopy (MRS) study. EEG focused on pre-stimulus (e.g., prior to stimulus presentation or perception) power changes to assess the degree to which those changes can predict subjects' perception (and judgment) of subsequent stimuli as high or low self-related. MRS measured resting state concentration of glutamate, focusing on PACC. High pre-stimulus (e.g., prior to stimulus presentation or perception) alpha power significantly correlated with both perception of stimuli judged to be highly self-related and with resting state glutamate concentrations in the PACC. In sum, our results show (i) pre-stimulus (e.g., prior to stimulus presentation or perception) alpha power and resting state glutamate concentration to mediate rest-self overlap that (ii) dispose or incline subjects to assign high degrees of self-relatedness to perceptual stimuli.

Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset.

PubMed

Lin, Yuan-Pin; Wang, Yijun; Jung, Tzyy-Ping

2014-08-09

Bridging the gap between laboratory brain-computer interface (BCI) demonstrations and real-life applications has gained increasing attention nowadays in translational neuroscience. An urgent need is to explore the feasibility of using a low-cost, ease-of-use electroencephalogram (EEG) headset for monitoring individuals' EEG signals in their natural head/body positions and movements. This study aimed to assess the feasibility of using a consumer-level EEG headset to realize an online steady-state visual-evoked potential (SSVEP)-based BCI during human walking. This study adopted a 14-channel Emotiv EEG headset to implement a four-target online SSVEP decoding system, and included treadmill walking at the speeds of 0.45, 0.89, and 1.34 meters per second (m/s) to initiate the walking locomotion. Seventeen participants were instructed to perform the online BCI tasks while standing or walking on the treadmill. To maintain a constant viewing distance to the visual targets, participants held the hand-grip of the treadmill during the experiment. Along with online BCI performance, the concurrent SSVEP signals were recorded for offline assessment. Despite walking-related attenuation of SSVEPs, the online BCI obtained an information transfer rate (ITR) over 12 bits/min during slow walking (below 0.89 m/s). SSVEP-based BCI systems are deployable to users in treadmill walking that mimics natural walking rather than in highly-controlled laboratory settings. This study considerably promotes the use of a consumer-level EEG headset towards the real-life BCI applications.

Comparison of EEG propagation speeds under emotional stimuli on smartphone between the different anxiety states

PubMed Central

Asakawa, Tetsuya; Muramatsu, Ayumi; Hayashi, Takuto; Urata, Tatsuya; Taya, Masato; Mizuno-Matsumoto, Yuko

2014-01-01

The current study evaluated the effect of different anxiety states on information processing as measured by an electroencephalography (EEG) using emotional stimuli on a smartphone. Twenty-three healthy subjects were assessed for their anxiety states using The State Trait Anxiety Inventory (STAI) and divided into two groups: low anxiety (I, II) or high anxiety (III and IV, V). An EEG was performed while the participant was presented with emotionally laden audiovisual stimuli (resting, pleasant, and unpleasant sessions) and emotionally laden sentence stimuli (pleasant sentence, unpleasant sentence sessions) and EEG data was analyzed using propagation speed analysis. The propagation speed of the low anxiety group at the medial coronal for resting stimuli for all time segments was higher than those of high anxiety group. The low anxiety group propagation speeds at the medial sagittal for unpleasant stimuli in the 0–30 and 60–150 s time frames were higher than those of high anxiety group. The propagation speeds at 150 s for all stimuli in the low anxiety group were significantly higher than the correspondent propagation speeds of the high anxiety group. These events suggest that neural information processes concerning emotional stimuli differ based on current anxiety state. PMID:25540618

EEG alpha synchronization is related to top-down processing in convergent and divergent thinking

PubMed Central

Benedek, Mathias; Bergner, Sabine; Könen, Tanja; Fink, Andreas; Neubauer, Aljoscha C.

2011-01-01

Synchronization of EEG alpha activity has been referred to as being indicative of cortical idling, but according to more recent evidence it has also been associated with active internal processing and creative thinking. The main objective of this study was to investigate to what extent EEG alpha synchronization is related to internal processing demands and to specific cognitive process involved in creative thinking. To this end, EEG was measured during a convergent and a divergent thinking task (i.e., creativity-related task) which once were processed involving low and once involving high internal processing demands. High internal processing demands were established by masking the stimulus (after encoding) and thus preventing further bottom-up processing. Frontal alpha synchronization was observed during convergent and divergent thinking only under exclusive top-down control (high internal processing demands), but not when bottom-up processing was allowed (low internal processing demands). We conclude that frontal alpha synchronization is related to top-down control rather than to specific creativity-related cognitive processes. Frontal alpha synchronization, which has been observed in a variety of different creativity tasks, thus may not reflect a brain state that is specific for creative cognition but can probably be attributed to high internal processing demands which are typically involved in creative thinking. PMID:21925520

Application of polymer sensitive MRI sequence to localization of EEG electrodes.

PubMed

Butler, Russell; Gilbert, Guillaume; Descoteaux, Maxime; Bernier, Pierre-Michel; Whittingstall, Kevin

2017-02-15

The growing popularity of simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) opens up the possibility of imaging EEG electrodes while the subject is in the scanner. Such information could be useful for improving the fusion of EEG-fMRI datasets. Here, we report for the first time how an ultra-short echo time (UTE) MR sequence can image the materials of an MR-compatible EEG cap, finding that electrodes and some parts of the wiring are visible in a high resolution UTE. Using these images, we developed a segmentation procedure to obtain electrode coordinates based on voxel intensity from the raw UTE, using hand labeled coordinates as the starting point. We were able to visualize and segment 95% of EEG electrodes using a short (3.5min) UTE sequence. We provide scripts and template images so this approach can now be easily implemented to obtain precise, subject-specific EEG electrode positions while adding minimal acquisition time to the simultaneous EEG-fMRI protocol. T1 gel artifacts are not robust enough to localize all electrodes across subjects, the polymers composing Brainvision cap electrodes are not visible on a T1, and adding T1 visible materials to the EEG cap is not always possible. We therefore consider our method superior to existing methods for obtaining electrode positions in the scanner, as it is hardware free and should work on a wide range of materials (caps). EEG electrode positions are obtained with high precision and no additional hardware. Copyright © 2016 Elsevier B.V. All rights reserved.

Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine.

PubMed

Zhou, Jing; Wu, Xiao-ming; Zeng, Wei-jie

2015-12-01

Sleep apnea syndrome (SAS) is prevalent in individuals and recently, there are many studies focus on using simple and efficient methods for SAS detection instead of polysomnography. However, not much work has been done on using nonlinear behavior of the electroencephalogram (EEG) signals. The purpose of this study is to find a novel and simpler method for detecting apnea patients and to quantify nonlinear characteristics of the sleep apnea. 30 min EEG scaling exponents that quantify power-law correlations were computed using detrended fluctuation analysis (DFA) and compared between six SAS and six healthy subjects during sleep. The mean scaling exponents were calculated every 30 s and 360 control values and 360 apnea values were obtained. These values were compared between the two groups and support vector machine (SVM) was used to classify apnea patients. Significant difference was found between EEG scaling exponents of the two groups (p < 0.001). SVM was used and obtained high and consistent recognition rate: average classification accuracy reached 95.1% corresponding to the sensitivity 93.2% and specificity 98.6%. DFA of EEG is an efficient and practicable method and is helpful clinically in diagnosis of sleep apnea.

[Objective Assessment of Emotion Processing. Forensic Case Report].

PubMed

Reyes, Ana Calzada; Gutiérrez Manso, Ana Teresa; González, Mariloly Acosta

2014-03-01

The main objective of the emotions is to ensure the homeostasis, the survival and the well-being of the organism. To demonstrate the usefulness of performing neurophysiological and neuropsychological assessments in patients, in order to demonstrate the significant role of the emotions in the execution of certain behaviours. A forensic psychiatric interview was conducted. EEG in vigil state was registered, the generators of current density to theta band were calculated, and the emotions recognition test was performed. The results of the psychiatric interview demonstrated that fear was an important element in acting impulsively, and lack of foresight of the accused. A substantial decrease was demonstrated in the ability to understand the scope of the acts and the direction of the behaviour during the time the crime occurred. The EEG showed alterations in frontal regions, and the generators of current density were located in frontal-temporal regions and occipital associative areas. It is recommended to associate these studies with the forensic psychiatric assessment, in order to increase the objectivity of the diagnoses formulated by medical experts. Copyright © 2014 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.

Novel hydrogel-based preparation-free EEG electrode.

PubMed

Alba, Nicolas Alexander; Sclabassi, Robert J; Sun, Mingui; Cui, Xinyan Tracy

2010-08-01

The largest obstacles to signal transduction for electroencephalography (EEG) recording are the hair and the epidermal stratum corneum of the skin. In typical clinical situations, hair is parted or removed, and the stratum corneum is either abraded or punctured using invasive penetration devices. These steps increase preparation time, discomfort, and the risk of infection. Cross-linked sodium polyacrylate gel swelled with electrolyte was explored as a possible skin contact element for a prototype preparation-free EEG electrode. As a superabsorbent hydrogel, polyacrylate can swell with electrolyte solution to a degree far beyond typical contemporary electrode materials, delivering a strong hydrating effect to the skin surface. This hydrating power allows the material to increase the effective skin contact surface area through wetting, and noninvasively decrease or bypass the highly resistive barrier of the stratum corneum, allowing for reduced impedance and improved electrode performance. For the purposes of the tests performed in this study, the polyacrylate was prepared both as a solid elastic gel and as a flowable paste designed to penetrate dense scalp hair. The gel can hold 99.2% DI water or 91% electrolyte solution, and the water content remains high after 29 h of air exposure. The electrical impedance of the gel electrode on unprepared human forearm is significantly lower than a number of commercial ECG and EEG electrodes. This low impedance was maintained for at least 8 h (the longest time period measured). When a paste form of the electrode was applied directly onto scalp hair, the impedance was found to be lower than that measured with commercially available EEG paste applied in the same manner. Time-frequency transformation analysis of frontal lobe EEG recordings indicated comparable frequency response between the polyacrylate-based electrode on unprepared skin and the commercial EEG electrode on abraded skin. Evoked potential recordings demonstrated signal-to-noise ratios of the experimental and commercial electrodes to be effectively equivalent. These results suggest that the polyacrylate-based electrode offers a powerful option for EEG recording without scalp preparation.

A Comparison of Multiscale Permutation Entropy Measures in On-Line Depth of Anesthesia Monitoring

PubMed Central

Li, Xiaoli; Li, Duan; Li, Yongwang; Ursino, Mauro

2016-01-01

Objective Multiscale permutation entropy (MSPE) is becoming an interesting tool to explore neurophysiological mechanisms in recent years. In this study, six MSPE measures were proposed for on-line depth of anesthesia (DoA) monitoring to quantify the anesthetic effect on the real-time EEG recordings. The performance of these measures in describing the transient characters of simulated neural populations and clinical anesthesia EEG were evaluated and compared. Methods Six MSPE algorithms—derived from Shannon permutation entropy (SPE), Renyi permutation entropy (RPE) and Tsallis permutation entropy (TPE) combined with the decomposition procedures of coarse-graining (CG) method and moving average (MA) analysis—were studied. A thalamo-cortical neural mass model (TCNMM) was used to generate noise-free EEG under anesthesia to quantitatively assess the robustness of each MSPE measure against noise. Then, the clinical anesthesia EEG recordings from 20 patients were analyzed with these measures. To validate their effectiveness, the ability of six measures were compared in terms of tracking the dynamical changes in EEG data and the performance in state discrimination. The Pearson correlation coefficient (R) was used to assess the relationship among MSPE measures. Results CG-based MSPEs failed in on-line DoA monitoring at multiscale analysis. In on-line EEG analysis, the MA-based MSPE measures at 5 decomposed scales could track the transient changes of EEG recordings and statistically distinguish the awake state, unconsciousness and recovery of consciousness (RoC) state significantly. Compared to single-scale SPE and RPE, MSPEs had better anti-noise ability and MA-RPE at scale 5 performed best in this aspect. MA-TPE outperformed other measures with faster tracking speed of the loss of unconsciousness. Conclusions MA-based multiscale permutation entropies have the potential for on-line anesthesia EEG analysis with its simple computation and sensitivity to drug effect changes. CG-based multiscale permutation entropies may fail to describe the characteristics of EEG at high decomposition scales. PMID:27723803

A Comparison of Multiscale Permutation Entropy Measures in On-Line Depth of Anesthesia Monitoring.

PubMed

Su, Cui; Liang, Zhenhu; Li, Xiaoli; Li, Duan; Li, Yongwang; Ursino, Mauro

2016-01-01

Multiscale permutation entropy (MSPE) is becoming an interesting tool to explore neurophysiological mechanisms in recent years. In this study, six MSPE measures were proposed for on-line depth of anesthesia (DoA) monitoring to quantify the anesthetic effect on the real-time EEG recordings. The performance of these measures in describing the transient characters of simulated neural populations and clinical anesthesia EEG were evaluated and compared. Six MSPE algorithms-derived from Shannon permutation entropy (SPE), Renyi permutation entropy (RPE) and Tsallis permutation entropy (TPE) combined with the decomposition procedures of coarse-graining (CG) method and moving average (MA) analysis-were studied. A thalamo-cortical neural mass model (TCNMM) was used to generate noise-free EEG under anesthesia to quantitatively assess the robustness of each MSPE measure against noise. Then, the clinical anesthesia EEG recordings from 20 patients were analyzed with these measures. To validate their effectiveness, the ability of six measures were compared in terms of tracking the dynamical changes in EEG data and the performance in state discrimination. The Pearson correlation coefficient (R) was used to assess the relationship among MSPE measures. CG-based MSPEs failed in on-line DoA monitoring at multiscale analysis. In on-line EEG analysis, the MA-based MSPE measures at 5 decomposed scales could track the transient changes of EEG recordings and statistically distinguish the awake state, unconsciousness and recovery of consciousness (RoC) state significantly. Compared to single-scale SPE and RPE, MSPEs had better anti-noise ability and MA-RPE at scale 5 performed best in this aspect. MA-TPE outperformed other measures with faster tracking speed of the loss of unconsciousness. MA-based multiscale permutation entropies have the potential for on-line anesthesia EEG analysis with its simple computation and sensitivity to drug effect changes. CG-based multiscale permutation entropies may fail to describe the characteristics of EEG at high decomposition scales.

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

Diagnostic accuracy of EEG changes during carotid endarterectomy in predicting perioperative strokes.

PubMed

Thirumala, Parthasarathy D; Thiagarajan, Karthy; Gedela, Satyanarayana; Crammond, Donald J; Balzer, Jeffrey R

2016-03-01

The 30 day stroke rate following carotid endarterectomy (CEA) ranges between 2-6%. Such periprocedural strokes are associated with a three-fold increased risk of mortality. Our primary aim was to determine the diagnostic accuracy of electroencephalogram (EEG) in predicting perioperative strokes through meta-analysis of existing literature. An extensive search for relevant literature was undertaken using PubMed and Web of Science databases. Studies were included after screening using predetermined criteria. Data was extracted and analyzed. Summary sensitivity, specificity and diagnostic odds ratio were obtained. Subgroup analysis of studies using eight or more EEG channels was done. Perioperative stroke rate for the cohort of 8765 patients was 1.75%. Pooled sensitivity and specificity of EEG changes in predicting these strokes were 52% (95% confidence interval [CI], 43-61%) and 84% (95% CI, 81-86%) respectively. Summary estimates of the subgroup were similar. The diagnostic odds ratio was 5.85 (95% CI, 3.71-9.22). For the observed stroke rate, the positive likelihood ratio was 3.25 while the negative predictive value was 98.99%. According to these results, patients with perioperative strokes have six times greater odds of experiencing an intraoperative change in EEG during CEA. EEG monitoring was found to be highly specific in predicting perioperative strokes after CEA. Copyright © 2015 Elsevier Ltd. All rights reserved.

Is EEG-biofeedback an effective treatment in autism spectrum disorders? A randomized controlled trial.

PubMed

Kouijzer, Mirjam E J; van Schie, Hein T; Gerrits, Berrie J L; Buitelaar, Jan K; de Moor, Jan M H

2013-03-01

EEG-biofeedback has been reported to reduce symptoms of autism spectrum disorders (ASD) in several studies. However, these studies did not control for nonspecific effects of EEG-biofeedback and did not distinguish between participants who succeeded in influencing their own EEG activity and participants who did not. To overcome these methodological shortcomings, this study evaluated the effects of EEG-biofeedback in ASD in a randomized pretest-posttest control group design with blinded active comparator and six months follow-up. Thirty-eight participants were randomly allocated to the EEG-biofeedback, skin conductance (SC)-biofeedback or waiting list group. EEG- and SC-biofeedback sessions were similar and participants were blinded to the type of feedback they received. Assessments pre-treatment, post-treatment, and after 6 months included parent ratings of symptoms of ASD, executive function tasks, and 19-channel EEG recordings. Fifty-four percent of the participants significantly reduced delta and/or theta power during EEG-biofeedback sessions and were identified as EEG-regulators. In these EEG-regulators, no statistically significant reductions of symptoms of ASD were observed, but they showed significant improvement in cognitive flexibility as compared to participants who managed to regulate SC. EEG-biofeedback seems to be an applicable tool to regulate EEG activity and has specific effects on cognitive flexibility, but it did not result in significant reductions in symptoms of ASD. An important finding was that no nonspecific effects of EEG-biofeedback were demonstrated.

EEG in the classroom: Synchronised neural recordings during video presentation

PubMed Central

Poulsen, Andreas Trier; Kamronn, Simon; Dmochowski, Jacek; Parra, Lucas C.; Hansen, Lars Kai

2017-01-01

We performed simultaneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked by a common video stimulus. The neural reliability, as quantified by ISC, has been linked to engagement and attentional modulation in earlier studies that used high-grade equipment in laboratory settings. Here we reproduce many of the results from these studies using portable low-cost equipment, focusing on the robustness of using ISC for subjects experiencing naturalistic stimuli. The present data shows that stimulus-evoked neural responses, known to be modulated by attention, can be tracked for groups of students with synchronized EEG acquisition. This is a step towards real-time inference of engagement in the classroom. PMID:28266588

EEG in the classroom: Synchronised neural recordings during video presentation

NASA Astrophysics Data System (ADS)

Poulsen, Andreas Trier; Kamronn, Simon; Dmochowski, Jacek; Parra, Lucas C.; Hansen, Lars Kai

2017-03-01

We performed simultaneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked by a common video stimulus. The neural reliability, as quantified by ISC, has been linked to engagement and attentional modulation in earlier studies that used high-grade equipment in laboratory settings. Here we reproduce many of the results from these studies using portable low-cost equipment, focusing on the robustness of using ISC for subjects experiencing naturalistic stimuli. The present data shows that stimulus-evoked neural responses, known to be modulated by attention, can be tracked for groups of students with synchronized EEG acquisition. This is a step towards real-time inference of engagement in the classroom.

Highly Efficient Compression Algorithms for Multichannel EEG.

PubMed

Shaw, Laxmi; Rahman, Daleef; Routray, Aurobinda

2018-05-01

The difficulty associated with processing and understanding the high dimensionality of electroencephalogram (EEG) data requires developing efficient and robust compression algorithms. In this paper, different lossless compression techniques of single and multichannel EEG data, including Huffman coding, arithmetic coding, Markov predictor, linear predictor, context-based error modeling, multivariate autoregression (MVAR), and a low complexity bivariate model have been examined and their performances have been compared. Furthermore, a high compression algorithm named general MVAR and a modified context-based error modeling for multichannel EEG have been proposed. The resulting compression algorithm produces a higher relative compression ratio of 70.64% on average compared with the existing methods, and in some cases, it goes up to 83.06%. The proposed methods are designed to compress a large amount of multichannel EEG data efficiently so that the data storage and transmission bandwidth can be effectively used. These methods have been validated using several experimental multichannel EEG recordings of different subjects and publicly available standard databases. The satisfactory parametric measures of these methods, namely percent-root-mean square distortion, peak signal-to-noise ratio, root-mean-square error, and cross correlation, show their superiority over the state-of-the-art compression methods.

EEG Brain Wave Activity at Rest and during Evoked Attention in Children with Attention-Deficit/Hyperactivity Disorder and Effects of Methylphenidate.

PubMed

Thomas, Bianca Lee; Viljoen, Margaretha

2016-01-01

The aim of this study was to assess baseline EEG brain wave activity in children with attention-deficit/hyperactivity disorder (ADHD) and to examine the effects of evoked attention and methylphenidate on this activity. Children with ADHD (n = 19) were tested while they were stimulant free and during a period in which they were on stimulant (methylphenidate) medication. Control subjects (n = 18) were tested once. EEG brain wave activity was tested both at baseline and during focussed attention. Attention was evoked and EEG brain wave activity was determined by means of the BioGraph Infiniti biofeedback apparatus. The main finding of this study was that control subjects and stimulant-free children with ADHD exhibited the expected reactivity in high alpha-wave activity (11-12 Hz) from baseline to focussed attention; however, methylphenidate appeared to abolish this reactivity. Methylphenidate attenuates the normal cortical response to a cognitive challenge. © 2016 S. Karger AG, Basel.

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.

Telemetry video-electroencephalography (EEG) in rats, dogs and non-human primates: methods in follow-up safety pharmacology seizure liability assessments.

PubMed

Bassett, Leanne; Troncy, Eric; Pouliot, Mylene; Paquette, Dominique; Ascah, Alexis; Authier, Simon

2014-01-01

Non-clinical seizure liability studies typically aim to: 1) confirm the nature of EEG activity during abnormal clinical signs, 2) identify premonitory clinical signs, 3) measure plasma levels at seizure onset, 4) demonstrate that drug-induced seizures are self-limiting, 5) confirm that conventional drugs (e.g. diazepam) can treat drug-induced seizures and 6) confirm the no observed adverse effect level (NOAEL) at EEG. Our aim was to originally characterize several of these items in a three species comparative study. Cynomolgus monkey, Beagle dog and Sprague-Dawley rat with EEG telemetry transmitters were used to obtain EEG using the 10-20 system. Pentylenetetrazol (PTZ) was used to determine seizure threshold or as a positive seizurogenic agent. Clinical signs were recorded and premonitory signs were evaluated. In complement, other pharmacological agents were used to illustrate various safety testing strategies. Intravenous PTZ doses required to induce clonic convulsions were 36.1 (3.8), 56.1 (12.7) and 49.4 (11.7) mg/kg, in Beagle dogs, cynomolgus monkeys and Sprague-Dawley rats, respectively. Premonitory clinical signs typically included decreased physical activity, enhanced physiological tremors, hypersalivation, ataxia, emesis (except in rats) and myoclonus. In Sprague-Dawley rats, amphetamine (PO) increased high (approximately 40-120Hz), and decreased low (1-14Hz) frequencies. In cynomolgus monkeys, caffeine (IM) increased power in high (14-127Hz), and attenuated power in low (1-13Hz) frequencies. In the rat PTZ infusion seizure threshold model, yohimbine (SC and IV) and phenobarbital (IP) confirmed to be reliable positive controls as pro- and anticonvulsants, respectively. Telemetry video-EEG for seizure liability investigations was characterized in three species. Rats represent a first-line model in seizure liability assessments. Beagle dogs are often associated with overt susceptibility to seizure and are typically used in seizure liability studies only if required by regulators. Non-human primates represent an important model in seizure liability assessments given similarities to humans and a high translational potential. Copyright © 2014. Published by Elsevier Inc.

Efficient fluorescence quenching in electrochemically exfoliated graphene decorated with gold nanoparticles

NASA Astrophysics Data System (ADS)

Hurtado-Morales, M.; Ortiz, M.; Acuña, C.; Nerl, H. C.; Nicolosi, V.; Hernández, Y.

2016-07-01

High surface area graphene sheets were obtained by electrochemical exfoliation of graphite in an acid medium under constant potential conditions. Filtration and centrifugation processes played an important role in order to obtain stable dispersions in water. Scanning electron microscopy and transmission electron microscopy imaging revealed highly exfoliated crystalline samples of ∼5 μm. Raman, Fourier transform infrared and x-ray photoelectron spectroscopy further confirmed the high quality of the exfoliated material. The electrochemically exfoliated graphene (EEG) was decorated with gold nanoparticles (AuNPs) using sodium cholate as a buffer layer. This approach allowed for a non-covalent functionalization without altering the desirable electronic properties of the EEG. The AuNP-EEG samples were characterized with various techniques including absorbance and fluorescence spectroscopy. These samples displayed a fluorescence signal using an excitation wavelength of 290 nm. The calculated quantum yield (Φ) for these samples was 40.04%, a high efficiency compared to previous studies using solution processable graphene.

Characterizing and Modulating Brain Circuitry through Transcranial Magnetic Stimulation Combined with Electroencephalography.

PubMed

Farzan, Faranak; Vernet, Marine; Shafi, Mouhsin M D; Rotenberg, Alexander; Daskalakis, Zafiris J; Pascual-Leone, Alvaro

2016-01-01

The concurrent combination of transcranial magnetic stimulation (TMS) with electroencephalography (TMS-EEG) is a powerful technology for characterizing and modulating brain networks across developmental, behavioral, and disease states. Given the global initiatives in mapping the human brain, recognition of the utility of this technique is growing across neuroscience disciplines. Importantly, TMS-EEG offers translational biomarkers that can be applied in health and disease, across the lifespan, and in humans and animals, bridging the gap between animal models and human studies. However, to utilize the full potential of TMS-EEG methodology, standardization of TMS-EEG study protocols is needed. In this article, we review the principles of TMS-EEG methodology, factors impacting TMS-EEG outcome measures, and the techniques for preventing and correcting artifacts in TMS-EEG data. To promote the standardization of this technique, we provide comprehensive guides for designing TMS-EEG studies and conducting TMS-EEG experiments. We conclude by reviewing the application of TMS-EEG in basic, cognitive and clinical neurosciences, and evaluate the potential of this emerging technology in brain research.

Characterizing and Modulating Brain Circuitry through Transcranial Magnetic Stimulation Combined with Electroencephalography

PubMed Central

Farzan, Faranak; Vernet, Marine; Shafi, Mouhsin M. D.; Rotenberg, Alexander; Daskalakis, Zafiris J.; Pascual-Leone, Alvaro

2016-01-01

The concurrent combination of transcranial magnetic stimulation (TMS) with electroencephalography (TMS-EEG) is a powerful technology for characterizing and modulating brain networks across developmental, behavioral, and disease states. Given the global initiatives in mapping the human brain, recognition of the utility of this technique is growing across neuroscience disciplines. Importantly, TMS-EEG offers translational biomarkers that can be applied in health and disease, across the lifespan, and in humans and animals, bridging the gap between animal models and human studies. However, to utilize the full potential of TMS-EEG methodology, standardization of TMS-EEG study protocols is needed. In this article, we review the principles of TMS-EEG methodology, factors impacting TMS-EEG outcome measures, and the techniques for preventing and correcting artifacts in TMS-EEG data. To promote the standardization of this technique, we provide comprehensive guides for designing TMS-EEG studies and conducting TMS-EEG experiments. We conclude by reviewing the application of TMS-EEG in basic, cognitive and clinical neurosciences, and evaluate the potential of this emerging technology in brain research. PMID:27713691

Analyzing large data sets acquired through telemetry from rats exposed to organophosphorous compounds: an EEG study.

PubMed

de Araujo Furtado, Marcio; Zheng, Andy; Sedigh-Sarvestani, Madineh; Lumley, Lucille; Lichtenstein, Spencer; Yourick, Debra

2009-10-30

The organophosphorous compound soman is an acetylcholinesterase inhibitor that causes damage to the brain. Exposure to soman causes neuropathology as a result of prolonged and recurrent seizures. In the present study, long-term recordings of cortical EEG were used to develop an unbiased means to quantify measures of seizure activity in a large data set while excluding other signal types. Rats were implanted with telemetry transmitters and exposed to soman followed by treatment with therapeutics similar to those administered in the field after nerve agent exposure. EEG, activity and temperature were recorded continuously for a minimum of 2 days pre-exposure and 15 days post-exposure. A set of automatic MATLAB algorithms have been developed to remove artifacts and measure the characteristics of long-term EEG recordings. The algorithms use short-time Fourier transforms to compute the power spectrum of the signal for 2-s intervals. The spectrum is then divided into the delta, theta, alpha, and beta frequency bands. A linear fit to the power spectrum is used to distinguish normal EEG activity from artifacts and high amplitude spike wave activity. Changes in time spent in seizure over a prolonged period are a powerful indicator of the effects of novel therapeutics against seizures. A graphical user interface has been created that simultaneously plots the raw EEG in the time domain, the power spectrum, and the wavelet transform. Motor activity and temperature are associated with EEG changes. The accuracy of this algorithm is also verified against visual inspection of video recordings up to 3 days after exposure.

Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata

PubMed Central

Liu, Aiming; Liu, Quan; Ai, Qingsong; Xie, Yi; Chen, Anqi

2017-01-01

Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain–computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain–computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain–computer interface systems. PMID:29117100

Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata.

PubMed

Liu, Aiming; Chen, Kun; Liu, Quan; Ai, Qingsong; Xie, Yi; Chen, Anqi

2017-11-08

Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain-computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain-computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain-computer interface systems.

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.

Comparison of Foam-Based and Spring-Loaded Dry EEG Electrodes with Wet Electrodes in Resting and Moving Conditions*

PubMed Central

Yeung, Arnold; Garudadri, Harinath; Van Toen, Carolyn; Mercier, Patrick; Balkan, Ozgur; Makeig, Scott; Virji-Babul, Naznin

2018-01-01

The introduction of dry electrodes for EEG measurements has opened up possibilities of recording EEG outside of standard clinical environments by reducing required preparation and maintenance. However, the signal quality of dry electrodes in comparison with wet electrodes has not yet been evaluated under activities of daily life (ADL) or high motion tasks. In this study, we compared the performances of foam-based and spring-loaded dry electrodes with wet electrodes under three different task conditions: resting state, walking, and cycling. Our analysis showed signals obtained by the 2 types of dry electrodes and obtained by wet electrodes displayed high correlation for all conditions, while being prone to similar environmental and electrode-based artifacts. Overall, our results suggest that dry electrodes have a similar signal quality in comparison to wet electrodes and may be more practical for use in mobile and real-time motion applications due to their convenience. In addition, we conclude that as with wet electrodes, post-processing can mitigate motion artifacts in ambulatory EEG acquisition. PMID:26737936

Independent component processes underlying emotions during natural music listening

PubMed Central

Zollinger, Nina; Elmer, Stefan; Jäncke, Lutz

2016-01-01

The aim of this study was to investigate the brain processes underlying emotions during natural music listening. To address this, we recorded high-density electroencephalography (EEG) from 22 subjects while presenting a set of individually matched whole musical excerpts varying in valence and arousal. Independent component analysis was applied to decompose the EEG data into functionally distinct brain processes. A k-means cluster analysis calculated on the basis of a combination of spatial (scalp topography and dipole location mapped onto the Montreal Neurological Institute brain template) and functional (spectra) characteristics revealed 10 clusters referring to brain areas typically involved in music and emotion processing, namely in the proximity of thalamic-limbic and orbitofrontal regions as well as at frontal, fronto-parietal, parietal, parieto-occipital, temporo-occipital and occipital areas. This analysis revealed that arousal was associated with a suppression of power in the alpha frequency range. On the other hand, valence was associated with an increase in theta frequency power in response to excerpts inducing happiness compared to sadness. These findings are partly compatible with the model proposed by Heller, arguing that the frontal lobe is involved in modulating valenced experiences (the left frontal hemisphere for positive emotions) whereas the right parieto-temporal region contributes to the emotional arousal. PMID:27217116

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

Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis.

PubMed

Kaya, Yılmaz

2015-09-01

This paper proposes a novel approach to detect epilepsy seizures by using Electroencephalography (EEG), which is one of the most common methods for the diagnosis of epilepsy, based on 1-Dimension Local Binary Pattern (1D-LBP) and grey relational analysis (GRA) methods. The main aim of this paper is to evaluate and validate a novel approach, which is a computer-based quantitative EEG analyzing method and based on grey systems, aimed to help decision-maker. In this study, 1D-LBP, which utilizes all data points, was employed for extracting features in raw EEG signals, Fisher score (FS) was employed to select the representative features, which can also be determined as hidden patterns. Additionally, GRA is performed to classify EEG signals through these Fisher scored features. The experimental results of the proposed approach, which was employed in a public dataset for validation, showed that it has a high accuracy in identifying epileptic EEG signals. For various combinations of epileptic EEG, such as A-E, B-E, C-E, D-E, and A-D clusters, 100, 96, 100, 99.00 and 100% were achieved, respectively. Also, this work presents an attempt to develop a new general-purpose hidden pattern determination scheme, which can be utilized for different categories of time-varying signals.

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.

Design, fabrication and experimental validation of a novel dry-contact sensor for measuring electroencephalography signals without skin preparation.

PubMed

Liao, Lun-De; Wang, I-Jan; Chen, Sheng-Fu; Chang, Jyh-Yeong; Lin, Chin-Teng

2011-01-01

In the present study, novel dry-contact sensors for measuring electro-encephalography (EEG) signals without any skin preparation are designed, fabricated by an injection molding manufacturing process and experimentally validated. Conventional wet electrodes are commonly used to measure EEG signals; they provide excellent EEG signals subject to proper skin preparation and conductive gel application. However, a series of skin preparation procedures for applying the wet electrodes is always required and usually creates trouble for users. To overcome these drawbacks, novel dry-contact EEG sensors were proposed for potential operation in the presence or absence of hair and without any skin preparation or conductive gel usage. The dry EEG sensors were designed to contact the scalp surface with 17 spring contact probes. Each probe was designed to include a probe head, plunger, spring, and barrel. The 17 probes were inserted into a flexible substrate using a one-time forming process via an established injection molding procedure. With these 17 spring contact probes, the flexible substrate allows for high geometric conformity between the sensor and the irregular scalp surface to maintain low skin-sensor interface impedance. Additionally, the flexible substrate also initiates a sensor buffer effect, eliminating pain when force is applied. The proposed dry EEG sensor was reliable in measuring EEG signals without any skin preparation or conductive gel usage, as compared with the conventional wet electrodes.

Design, Fabrication and Experimental Validation of a Novel Dry-Contact Sensor for Measuring Electroencephalography Signals without Skin Preparation

PubMed Central

Liao, Lun-De; Wang, I-Jan; Chen, Sheng-Fu; Chang, Jyh-Yeong; Lin, Chin-Teng

2011-01-01

In the present study, novel dry-contact sensors for measuring electro-encephalography (EEG) signals without any skin preparation are designed, fabricated by an injection molding manufacturing process and experimentally validated. Conventional wet electrodes are commonly used to measure EEG signals; they provide excellent EEG signals subject to proper skin preparation and conductive gel application. However, a series of skin preparation procedures for applying the wet electrodes is always required and usually creates trouble for users. To overcome these drawbacks, novel dry-contact EEG sensors were proposed for potential operation in the presence or absence of hair and without any skin preparation or conductive gel usage. The dry EEG sensors were designed to contact the scalp surface with 17 spring contact probes. Each probe was designed to include a probe head, plunger, spring, and barrel. The 17 probes were inserted into a flexible substrate using a one-time forming process via an established injection molding procedure. With these 17 spring contact probes, the flexible substrate allows for high geometric conformity between the sensor and the irregular scalp surface to maintain low skin-sensor interface impedance. Additionally, the flexible substrate also initiates a sensor buffer effect, eliminating pain when force is applied. The proposed dry EEG sensor was reliable in measuring EEG signals without any skin preparation or conductive gel usage, as compared with the conventional wet electrodes. PMID:22163929

Information theoretic measures of network coordination in high-frequency scalp EEG reveal dynamic patterns associated with seizure termination

PubMed Central

Stamoulis, Catherine; Schomer, Donald L.; Chang, Bernard S.

2013-01-01

How a seizure terminates is still under-studied and, despite its clinical importance, remains an obscure phase of seizure evolution. Recent studies of seizure-related scalp EEGs at frequencies >100 Hz suggest that neural activity, in the form of oscillations and/or neuronal network interactions, may play an important role in preictal/ictal seizure evolution [2, 31]. However, the role of high-frequency activity in seizure termination, is unknown, if it exists at all. Using information theoretic measures of network coordination, this study investigated ictal and immediate postictal neurodynamic interactions encoded in scalp EEGs from a relatively small sample of 8 patients with focal epilepsy and multiple seizures originating in temporal and/or frontal brain regions, at frequencies ≤100 Hz and >100 Hz, respectively. Despite some heterogeneity in the dynamics of these interactions, consistent patterns were also estimated. Specifically, in several seizures, linear or non-linear increase in high-frequency neuronal coordination during ictal intervals, coincided with a corresponding decrease in coordination at frequencies <100 Hz, suggesting a potential interference role of high-frequency activity, to disrupt abnormal ictal synchrony at lower frequencies. These changes in network synchrony started at least 20–30 sec prior to seizure offset, depending on the seizure duration. Opposite patterns were estimated at frequencies ≤100 Hz in several seizures. These results raise the possibility that high-frequency interference may occur in the form of progressive network coordination during the ictal interval, which continues during the postictal interval. This may be one of several possible mechanisms that facilitate seizure termination. In fact, inhibition of pairwise interactions between EEGs by other signals in their spatial neighborhood, quantified by negative interaction information, was estimated at frequencies ≤100 Hz, at least in some seizures. PMID:23608198

How to write an EEG report

PubMed Central

Benbadis, Selim R.

2013-01-01

The EEG report is structured to include demographics of the patient studied and reason for the EEG; specifics of the EEG techniques used; a description of the patterns, frequencies, voltages, and progression of the EEG pattern that were recorded; and finally a clinical impression of the EEG significance. The interpretation should be concise, clear and to the point, avoid jargon and EEG specifics, and should be understandable by any health care practitioner. PMID:23267044

Combining early post-resuscitation EEG and HRV features improves the prognostic performance in cardiac arrest model of rats.

PubMed

Dai, Chenxi; Wang, Zhi; Wei, Liang; Chen, Gang; Chen, Bihua; Zuo, Feng; Li, Yongqin

2018-04-09

Early and reliable prediction of neurological outcome remains a challenge for comatose survivors of cardiac arrest (CA). The purpose of this study was to evaluate the predictive ability of EEG, heart rate variability (HRV) features and the combination of them for outcome prognostication in CA model of rats. Forty-eight male Sprague-Dawley rats were randomized into 6 groups (n=8 each) with different cause and duration of untreated arrest. Cardiopulmonary resuscitation was initiated after 5, 6 and 7min of ventricular fibrillation or 4, 6 and 8min of asphyxia. EEG and ECG were continuously recorded for 4h under normothermia after resuscitation. The relationships between features of early post-resuscitation EEG, HRV and 96-hour outcome were investigated. Prognostic performances were evaluated using the area under receiver operating characteristic curve (AUC). All of the animals were successfully resuscitated and 27 of them survived to 96h. Weighted-permutation entropy (WPE) and normalized high frequency (nHF) outperformed other EEG and HRV features for the prediction of survival. The AUC of WPE was markedly higher than that of nHF (0.892 vs. 0.759, p<0.001). The AUC was 0.954 when WPE and nHF were combined using a logistic regression model, which was significantly higher than the individual EEG (p=0.018) and HRV (p<0.001) features. Earlier post-resuscitation HRV provided prognostic information complementary to quantitative EEG in the CA model of rats. The combination of EEG and HRV features leads to improving performance of outcome prognostication compared to either EEG or HRV based features alone. Copyright © 2018. Published by Elsevier Inc.

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.

How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review

PubMed Central

Gurau, Oana; Bosl, William J.; Newton, Charles R.

2017-01-01

Autism spectrum disorders (ASD) are thought to be associated with abnormal neural connectivity. Presently, neural connectivity is a theoretical construct that cannot be easily measured. Research in network science and time series analysis suggests that neural network structure, a marker of neural activity, can be measured with electroencephalography (EEG). EEG can be quantified by different methods of analysis to potentially detect brain abnormalities. The aim of this review is to examine evidence for the utility of three methods of EEG signal analysis in the ASD diagnosis and subtype delineation. We conducted a review of literature in which 40 studies were identified and classified according to the principal method of EEG analysis in three categories: functional connectivity analysis, spectral power analysis, and information dynamics. All studies identified significant differences between ASD patients and non-ASD subjects. However, due to high heterogeneity in the results, generalizations could not be inferred and none of the methods alone are currently useful as a new diagnostic tool. The lack of studies prevented the analysis of these methods as tools for ASD subtypes delineation. These results confirm EEG abnormalities in ASD, but as yet not sufficient to help in the diagnosis. Future research with larger samples and more robust study designs could allow for higher sensitivity and consistency in characterizing ASD, paving the way for developing new means of diagnosis. PMID:28747892

[Correlations of central nervous system and thyroid function under chronic emotional stress].

PubMed

Amiragova, M G; Arkhangel'skaia, M I

1982-06-01

Experiments on cats exposed to chronic emotional stress induced during one week by 4-hour immobilization of the animals in conjunction with aperiodic electrocutaneous stimulation were made to study correlations of the time course of changes in the EEG of the cortical and subcortical structures and the content of thyroxin in the peripheral blood at varying time of the experiments. It was demonstrated that in the course of stress, the EEG manifests the cycles of "burst" activity of slow waves, which are first recorded in the posterior hypothalamus and then get generalized. This is accompanied by a significantly high thyroxin secretion. As the stress exposures are repeated, the EEG changes become dominant, also corresponding with high thyroxin secretion. After the experiments are over, the cycles of "burst" activity accompanied by enhanced thyroid function are still recordable over several days.

EEG power during waking and NREM sleep in primary insomnia.

PubMed

Wu, You Meme; Pietrone, Regina; Cashmere, J David; Begley, Amy; Miewald, Jean M; Germain, Anne; Buysse, Daniel J

2013-10-15

Pathophysiological models of insomnia invoke the concept of 24-hour hyperarousal, which could lead to symptoms and physiological findings during waking and sleep. We hypothesized that this arousal could be seen in the waking electroencephalogram (EEG) of individuals with primary insomnia (PI), and that waking EEG power would correlate with non-REM (NREM) EEG. Subjects included 50 PI and 32 good sleeper controls (GSC). Five minutes of eyes closed waking EEG were collected at subjects' usual bedtimes, followed by polysomnography (PSG) at habitual sleep times. An automated algorithm and visual editing were used to remove artifacts from waking and sleep EEGs, followed by power spectral analysis to estimate power from 0.5-32 Hz. We did not find significant differences in waking or NREM EEG spectral power of PI and GSC. Significant correlations between waking and NREM sleep power were observed across all frequency bands in the PI group and in most frequency bands in the GSC group. The absence of significant differences between groups in waking or NREM EEG power suggests that our sample was not characterized by a high degree of cortical arousal. The consistent correlations between waking and NREM EEG power suggest that, in samples with elevated NREM EEG beta activity, waking EEG power may show a similar pattern.

A capacitive, biocompatible and adhesive electrode for long-term and cap-free monitoring of EEG signals.

PubMed

Lee, Seung Min; Kim, Jeong Hun; Byeon, Hang Jin; Choi, Yoon Young; Park, Kwang Suk; Lee, Sang-Hoon

2013-06-01

Long-term electroencephalogram (EEG) monitoring broadens EEG applications to various areas, but it requires cap-free recording of EEG signals. Our objective here is to develop a capacitive, small-sized, adhesive and biocompatible electrode for the cap-free and long-term EEG monitoring. We have developed an electrode made of polydimethylsiloxane (PDMS) and adhesive PDMS for EEG monitoring. This electrode can be attached to a hairy scalp and be completely hidden by the hair. We tested its electrical and mechanical (adhesive) properties by measuring voltage gain to frequency and adhesive force using 30 repeat cycles of the attachment and detachment test. Electrode performance on EEG was evaluated by alpha rhythm detection and measuring steady state visually evoked potential and N100 auditory evoked potential. We observed the successful recording of alpha rhythm and evoked signals to diverse stimuli with high signal quality. The biocompatibility of the electrode was verified and a survey found that the electrode was comfortable and convenient to wear. These results indicate that the proposed EEG electrode is suitable and convenient for long term EEG monitoring.

EEG monitoring of a free-swimming diver at a working depth of 15 meters.

NASA Technical Reports Server (NTRS)

Zweizig, J. R.; Adey, W. R.; Hanley, J.; Hahn, P. M.; Pilmanis, A. A.; Given, R. R.; Cockett, A. T. K.

1972-01-01

Feasibility has been shown for underwater transmission of physiological signals, using frequency modulation of carriers transmitted by return-current-density methods, as part of a personal biotelemetry system. In the prototype system, a standard IRIG subcarrier frequency (2300 Hz) was used. Power requirements, antenna design, and signal attenuation are compatible with free-ranging diving activity at distances up to 15 meters from the receiver. Extrapolation from this study and further developments are expected to substantially increase the range. Advantages of this system include subcarrier compatibility with standard IRIG demodulators using channels 1 through 10, the absence of highly specialized antenna requirements, and reasonable attenuation characteristics for transmission through the turbidity of typical seawater. Moreover, the system would not appear to be depth limited, and to be compatible with use of high-powered transponders of the same type for long-distance transmission, with or without further encoding.

Education research: evaluating the use of podcasting for residents during EEG instruction: a pilot study.

PubMed

Bensalem-Owen, Meriem; Chau, Destiny F; Sardam, Sean C; Fahy, Brenda G

2011-08-23

Educational methods for residents are shifting toward greater learner independence aided by technological advances. A Web-based program using a podcast was created for resident EEG instruction, replacing conventional didactics. The EEG curriculum also consisted of EEG interpretations under the tutelage of a neurophysiologist. This pilot study aimed to objectively evaluate the effectiveness of the podcast as a new teaching tool. A podcast for resident EEG instruction was implemented on the Web, replacing the traditional lecture. After Institutional Review Board approval, consent was obtained from the participating residents. Using 25-question evaluation tools, participants were assessed at baseline before any EEG instruction, and reassessed after podcasting and after 10 clinical EEG exposures. Each 25-item evaluation tool contained tracings used for clinical EEG interpretations. Scores after podcast training were also compared to scores after traditional didactic training from a previous study among anesthesiology trainees. Ten anesthesiology residents completed the study. The mean scores with standard deviations are 9.50 ± 2.92 at baseline, 13.40 ± 3.31 (p = 0.034) after the podcast, and 16.20 ± 1.87 (p = 0.019) after interpreting 10 EEGs. No differences were noted between the mean educational tool scores for those who underwent podcasting training compared to those who had undergone traditional didactic training. In this pilot study, podcast training was as effective as the prior conventional lecture in meeting the curricular goals of increasing EEG knowledge after 10 EEG interpretations as measured by assessment tools.