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.

Reliability of MEG source imaging of anterior temporal spikes: analysis of an intracranially characterized spike focus.

PubMed

Wennberg, Richard; Cheyne, Douglas

2014-05-01

To assess the reliability of MEG source imaging (MSI) of anterior temporal spikes through detailed analysis of the localization and orientation of source solutions obtained for a large number of spikes that were separately confirmed by intracranial EEG to be focally generated within a single, well-characterized spike focus. MSI was performed on 64 identical right anterior temporal spikes from an anterolateral temporal neocortical spike focus. The effects of different volume conductors (sphere and realistic head model), removal of noise with low frequency filters (LFFs) and averaging multiple spikes were assessed in terms of the reliability of the source solutions. MSI of single spikes resulted in scattered dipole source solutions that showed reasonable reliability for localization at the lobar level, but only for solutions with a goodness-of-fit exceeding 80% using a LFF of 3 Hz. Reliability at a finer level of intralobar localization was limited. Spike averaging significantly improved the reliability of source solutions and averaging 8 or more spikes reduced dependency on goodness-of-fit and data filtering. MSI performed on topographically identical individual spikes from an intracranially defined classical anterior temporal lobe spike focus was limited by low reliability (i.e., scattered source solutions) in terms of fine, sublobar localization within the ipsilateral temporal lobe. Spike averaging significantly improved reliability. MSI performed on individual anterior temporal spikes is limited by low reliability. Reduction of background noise through spike averaging significantly improves the reliability of MSI solutions. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. 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.

[A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

PubMed

Zhang, Y; Liu, A; Yu, K

1999-06-01

A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

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

The relationship between seizures, interictal spikes and antiepileptic drugs.

PubMed

Goncharova, Irina I; Alkawadri, Rafeed; Gaspard, Nicolas; Duckrow, Robert B; Spencer, Dennis D; Hirsch, Lawrence J; Spencer, Susan S; Zaveri, Hitten P

2016-09-01

A considerable decrease in spike rate accompanies antiepileptic drug (AED) taper during intracranial EEG (icEEG) monitoring. Since spike rate during icEEG monitoring can be influenced by surgery to place intracranial electrodes, we studied spike rate during long-term scalp EEG monitoring to further test this observation. We analyzed spike rate, seizure occurrence and AED taper in 130 consecutive patients over an average of 8.9days (range 5-17days). We observed a significant relationship between time to the first seizure, spike rate, AED taper and seizure occurrence (F (3,126)=19.77, p<0.0001). A high spike rate was related to a longer time to the first seizure. Further, in a subset of 79 patients who experienced seizures on or after day 4 of monitoring, spike rate decreased initially from an on- to off-AEDs epoch (from 505.0 to 382.3 spikes per hour, p<0.00001), and increased thereafter with the occurrence of seizures. There is an interplay between seizures, spikes and AEDs such that spike rate decreases with AED taper and increases after seizure occurrence. The direct relationship between spike rate and AEDs and between spike rate and time to the first seizure suggests that spikes are a marker of inhibition rather than excitation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

An empirical investigation of motion effects in eMRI of interictal epileptiform spikes.

PubMed

Sundaram, Padmavathi; Mulkern, Robert V; Wells, William M; Triantafyllou, Christina; Loddenkemper, Tobias; Bubrick, Ellen J; Orbach, Darren B

2011-12-01

We recently developed a functional neuroimaging technique called encephalographic magnetic resonance imaging (eMRI). Our method acquires rapid single-shot gradient-echo echo-planar MRI (repetition time=47 ms); it attempts to measure an MR signal more directly linked to neuronal electromagnetic activity than existing methods. To increase the likelihood of detecting such an MR signal, we recorded concurrent MRI and scalp electroencephalography (EEG) during fast (20-200 ms), localized, high-amplitude (>50 μV on EEG) cortical discharges in a cohort of focal epilepsy patients. Seen on EEG as interictal spikes, these discharges occur in between seizures and induced easily detectable MR magnitude and phase changes concurrent with the spikes with a lag of milliseconds to tens of milliseconds. Due to the time scale of the responses, localized changes in blood flow or hemoglobin oxygenation are unlikely to cause the MR signal changes that we observed. While the precise underlying mechanisms are unclear, in this study, we empirically investigate one potentially important confounding variable - motion. Head motion in the scanner affects both EEG and MR recording. It can produce brief "spike-like" artifacts on EEG and induce large MR signal changes similar to our interictal spike-related signal changes. In order to explore the possibility that interictal spikes were associated with head motions (although such an association had never been reported), we had previously tracked head position in epilepsy patients during interictal spikes and explicitly demonstrated a lack of associated head motion. However, that study was performed outside the MR scanner, and the root-mean-square error in the head position measurement was 0.7 mm. The large inaccuracy in this measurement therefore did not definitively rule out motion as a possible signal generator. In this study, we instructed healthy subjects to make deliberate brief (<500 ms) head motions inside the MR scanner and imaged these head motions with concurrent EEG and MRI. We compared these artifactual MR and EEG data to genuine interictal spikes. While per-voxel MR and per-electrode EEG time courses for the motion case can mimic the corresponding time courses associated with a genuine interictal spike, head motion can be unambiguously differentiated from interictal spikes via scalp EEG potential maps. Motion induces widespread changes in scalp potential, whereas interictal spikes are localized and have a regional fall-off in amplitude. These findings make bulk head motion an unlikely generator of the large spike-related MR signal changes that we had observed. Further work is required to precisely identify the underlying mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

Classification of epileptiform and wicket spike of EEG pattern using backpropagation neural network

NASA Astrophysics Data System (ADS)

Puspita, Juni Wijayanti; Jaya, Agus Indra; Gunadharma, Suryani

2017-03-01

Epilepsy is characterized by recurrent seizures that is resulted by permanent brain abnormalities. One of tools to support the diagnosis of epilepsy is Electroencephalograph (EEG), which describes the recording of brain electrical activity. Abnormal EEG patterns in epilepsy patients consist of Spike and Sharp waves. While both waves, there is a normal pattern that sometimes misinterpreted as epileptiform by electroenchepalographer (EEGer), namely Wicket Spike. The main difference of the three waves are on the time duration that related to the frequency. In this study, we proposed a method to classify a EEG wave into Sharp wave, Spike wave or Wicket spike group using Backpropagation Neural Network based on the frequency and amplitude of each wave. The results show that the proposed method can classifies the three group of waves with good accuracy.

Wavelet analysis of epileptic spikes

NASA Astrophysics Data System (ADS)

Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.

2003-05-01

Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.

A novel unsupervised spike sorting algorithm for intracranial EEG.

PubMed

Yadav, R; Shah, A K; Loeb, J A; Swamy, M N S; Agarwal, R

2011-01-01

This paper presents a novel, unsupervised spike classification algorithm for intracranial EEG. The method combines template matching and principal component analysis (PCA) for building a dynamic patient-specific codebook without a priori knowledge of the spike waveforms. The problem of misclassification due to overlapping classes is resolved by identifying similar classes in the codebook using hierarchical clustering. Cluster quality is visually assessed by projecting inter- and intra-clusters onto a 3D plot. Intracranial EEG from 5 patients was utilized to optimize the algorithm. The resulting codebook retains 82.1% of the detected spikes in non-overlapping and disjoint clusters. Initial results suggest a definite role of this method for both rapid review and quantitation of interictal spikes that could enhance both clinical treatment and research studies on epileptic patients.

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

NASA Astrophysics Data System (ADS)

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

2006-08-01

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

Combined process automation for large-scale EEG analysis.

PubMed

Sfondouris, John L; Quebedeaux, Tabitha M; Holdgraf, Chris; Musto, Alberto E

2012-01-01

Epileptogenesis is a dynamic process producing increased seizure susceptibility. Electroencephalography (EEG) data provides information critical in understanding the evolution of epileptiform changes throughout epileptic foci. We designed an algorithm to facilitate efficient large-scale EEG analysis via linked automation of multiple data processing steps. Using EEG recordings obtained from electrical stimulation studies, the following steps of EEG analysis were automated: (1) alignment and isolation of pre- and post-stimulation intervals, (2) generation of user-defined band frequency waveforms, (3) spike-sorting, (4) quantification of spike and burst data and (5) power spectral density analysis. This algorithm allows for quicker, more efficient EEG analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.

A case-control study of wicket spikes using video-EEG monitoring.

PubMed

Vallabhaneni, Maya; Baldassari, Laura E; Scribner, James T; Cho, Yong Won; Motamedi, Gholam K

2013-01-01

To investigate clinical characteristics associated with wicket spikes in patients undergoing long-term video-EEG monitoring. A case-control study was performed in 479 patients undergoing video-EEG monitoring, with 3 age- (±3 years) and gender-matched controls per patient with wicket spikes. Logistic regression was utilized to investigate the association between wicket spikes and other factors, including conditions that have been previously associated with wicket spikes. Wicket spikes were recorded in 48 patients. There was a significantly higher prevalence of dizziness/vertigo (p=0.002), headaches (p=0.005), migraine (p=0.015), and seizures (p=0.016) in patients with wickets. The majority of patients with wicket spikes did not exhibit epileptiform activity on EEG; however, patients with history of seizures were more likely to have wickets (p=0.017). There was no significant difference in the prevalence of psychogenic non-epileptic seizures between the groups. Wickets were more common on the left, during sleep, and more likely to be first recorded on day 1-2 of monitoring. Patients with wicket spikes are more likely to have dizziness/vertigo, headaches, migraine, and seizures. Patients with history of seizures are more likely to have wickets. The prevalence of psychogenic non-epileptic seizures is not significantly higher in patients with wickets. Copyright © 2012 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

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

PubMed Central

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

2014-01-01

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

Electrical activity of the cingulate cortex. II. Cholinergic modulation.

PubMed

Borst, J G; Leung, L W; MacFabe, D F

1987-03-24

The role of the cholinergic innervation in the modulation of cingulate electrical activity was studied by means of pharmacological manipulations and brain lesions. In the normal rat, an irregular slow activity (ISA) accompanied with EEG-spikes was recorded in the cingulate cortex during immobility as compared to walking. Atropine sulfate, but not atropine methyl nitrate, increased ISA and the frequency of cingulate EEG-spikes. Pilocarpine suppressed ISA and EEG-spikes during immobility, and induced a slow (4-7 Hz) theta rhythm. Unilateral or bilateral lesions of the substantia innominata and ventral globus pallidus area using kainic acid did not significantly change the cingulate EEG or its relation to behavior. Large electrolytic lesions of the medial septal nuclei and vertical limbs of the diagonal band generally decreased or abolished all theta activity in the cingulate cortex and the hippocampus. However, in 5 rats the cingulate theta rhythm increased while the hippocampal theta disappeared after a medial septal lesion. The large, postlesion cingulate theta, accompanied by sharp EEG-spikes during its negative phase, is an unequivocal demonstration of the existence of a theta rhythm in the cingulate cortex, independent of the hippocampal rhythm. Cholinergic afferents from the medial septum and diagonal band nuclei are inferred to be responsible for the behavioral suppression of cingulate EEG-spikes and ISA, and partially for the generation of a local cingulate theta rhythm. However, an atropine-resistant pathway and a theta-suppressing pathway, possibly coming from the medial septum or the hippocampus, may also be important in cingulate theta generation.

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

PubMed

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

2007-09-01

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

Artifact removal from EEG signals using adaptive filters in cascade

NASA Astrophysics Data System (ADS)

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

2007-11-01

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

Correlation of EEG with neuropsychological status in children with epilepsy.

PubMed

Hsu, David A; Rayer, Katherine; Jackson, Daren C; Stafstrom, Carl E; Hsu, Murielle; Ferrazzano, Peter A; Dabbs, Kevin; Worrell, Gregory A; Jones, Jana E; Hermann, Bruce P

2016-02-01

To determine correlations of the EEG frequency spectrum with neuropsychological status in children with idiopathic epilepsy. Forty-six children ages 8-18 years old with idiopathic epilepsy were retrospectively identified and analyzed for correlations between EEG spectra and neuropsychological status using multivariate linear regression. In addition, the theta/beta ratio, which has been suggested as a clinically useful EEG marker of attention-deficit hyperactivity disorder (ADHD), and an EEG spike count were calculated for each subject. Neuropsychological status was highly correlated with posterior alpha (8-15 Hz) EEG activity in a complex way, with both positive and negative correlations at lower and higher alpha frequency sub-bands for each cognitive task in a pattern that depends on the specific cognitive task. In addition, the theta/beta ratio was a specific but insensitive indicator of ADHD status in children with epilepsy; most children both with and without epilepsy have normal theta/beta ratios. The spike count showed no correlations with neuropsychological status. (1) The alpha rhythm may have at least two sub-bands which serve different purposes. (2) The theta/beta ratio is not a sensitive indicator of ADHD status in children with epilepsy. (3) The EEG frequency spectrum correlates more robustly with neuropsychological status than spike count analysis in children with idiopathic epilepsy. (1) The role of posterior alpha rhythms in cognition is complex and can be overlooked if EEG spectral resolution is too coarse or if neuropsychological status is assessed too narrowly. (2) ADHD in children with idiopathic epilepsy may involve different mechanisms from those in children without epilepsy. (3) Reliable correlations with neuropsychological status require longer EEG samples when using spike count analysis than when using frequency spectra. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

SpikeGUI: software for rapid interictal discharge annotation via template matching and online machine learning.

PubMed

Jing Jin; Dauwels, Justin; Cash, Sydney; Westover, M Brandon

2014-01-01

Detection of interictal discharges is a key element of interpreting EEGs during the diagnosis and management of epilepsy. Because interpretation of clinical EEG data is time-intensive and reliant on experts who are in short supply, there is a great need for automated spike detectors. However, attempts to develop general-purpose spike detectors have so far been severely limited by a lack of expert-annotated data. Huge databases of interictal discharges are therefore in great demand for the development of general-purpose detectors. Detailed manual annotation of interictal discharges is time consuming, which severely limits the willingness of experts to participate. To address such problems, a graphical user interface "SpikeGUI" was developed in our work for the purposes of EEG viewing and rapid interictal discharge annotation. "SpikeGUI" substantially speeds up the task of annotating interictal discharges using a custom-built algorithm based on a combination of template matching and online machine learning techniques. While the algorithm is currently tailored to annotation of interictal epileptiform discharges, it can easily be generalized to other waveforms and signal types.

SpikeGUI: Software for Rapid Interictal Discharge Annotation via Template Matching and Online Machine Learning

PubMed Central

Jin, Jing; Dauwels, Justin; Cash, Sydney; Westover, M. Brandon

2015-01-01

Detection of interictal discharges is a key element of interpreting EEGs during the diagnosis and management of epilepsy. Because interpretation of clinical EEG data is time-intensive and reliant on experts who are in short supply, there is a great need for automated spike detectors. However, attempts to develop general-purpose spike detectors have so far been severely limited by a lack of expert-annotated data. Huge databases of interictal discharges are therefore in great demand for the development of general-purpose detectors. Detailed manual annotation of interictal discharges is time consuming, which severely limits the willingness of experts to participate. To address such problems, a graphical user interface “SpikeGUI” was developed in our work for the purposes of EEG viewing and rapid interictal discharge annotation. “SpikeGUI” substantially speeds up the task of annotating interictal discharges using a custom-built algorithm based on a combination of template matching and online machine learning techniques. While the algorithm is currently tailored to annotation of interictal epileptiform discharges, it can easily be generalized to other waveforms and signal types. PMID:25570976

Joint time-frequency analysis of EEG signals based on a phase-space interpretation of the recording process

NASA Astrophysics Data System (ADS)

Testorf, M. E.; Jobst, B. C.; Kleen, J. K.; Titiz, A.; Guillory, S.; Scott, R.; Bujarski, K. A.; Roberts, D. W.; Holmes, G. L.; Lenck-Santini, P.-P.

2012-10-01

Time-frequency transforms are used to identify events in clinical EEG data. Data are recorded as part of a study for correlating the performance of human subjects during a memory task with pathological events in the EEG, called spikes. The spectrogram and the scalogram are reviewed as tools for evaluating spike activity. A statistical evaluation of the continuous wavelet transform across trials is used to quantify phase-locking events. For simultaneously improving the time and frequency resolution, and for representing the EEG of several channels or trials in a single time-frequency plane, a multichannel matching pursuit algorithm is used. Fundamental properties of the algorithm are discussed as well as preliminary results, which were obtained with clinical EEG data.

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

PubMed

Lopez Rincon, Alejandro; Shimoda, Shingo

2016-12-01

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

Stimulus-dependent spiking relationships with the EEG

PubMed Central

Snyder, Adam C.

2015-01-01

The development and refinement of noninvasive techniques for imaging neural activity is of paramount importance for human neuroscience. Currently, the most accessible and popular technique is electroencephalography (EEG). However, nearly all of what we know about the neural events that underlie EEG signals is based on inference, because of the dearth of studies that have simultaneously paired EEG recordings with direct recordings of single neurons. From the perspective of electrophysiologists there is growing interest in understanding how spiking activity coordinates with large-scale cortical networks. Evidence from recordings at both scales highlights that sensory neurons operate in very distinct states during spontaneous and visually evoked activity, which appear to form extremes in a continuum of coordination in neural networks. We hypothesized that individual neurons have idiosyncratic relationships to large-scale network activity indexed by EEG signals, owing to the neurons' distinct computational roles within the local circuitry. We tested this by recording neuronal populations in visual area V4 of rhesus macaques while we simultaneously recorded EEG. We found substantial heterogeneity in the timing and strength of spike-EEG relationships and that these relationships became more diverse during visual stimulation compared with the spontaneous state. The visual stimulus apparently shifts V4 neurons from a state in which they are relatively uniformly embedded in large-scale network activity to a state in which their distinct roles within the local population are more prominent, suggesting that the specific way in which individual neurons relate to EEG signals may hold clues regarding their computational roles. PMID:26108954

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.

Application of Independent Component Analysis for the Data Mining of Simultaneous EEG-fMRI: Preliminary Experience on Sleep Onset

PubMed Central

Lee, Jong-Hwan; Oh, Sungsuk; Jolesz, Ferenc A.; Park, Hyunwook; Yoo, Seung-Schik

2010-01-01

The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with the ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta- and alpha-rhythms that are sleep onset related EEG signatures along with the subsequent neural circuitries from a sleep deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEG-fMRI acquisitions, especially when a reference paradigm is unavailable. PMID:19922343

Application of independent component analysis for the data mining of simultaneous Eeg-fMRI: preliminary experience on sleep onset.

PubMed

Lee, Jong-Hwan; Oh, Sungsuk; Jolesz, Ferenc A; Park, Hyunwook; Yoo, Seung-Schik

2009-01-01

The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta and alpha rhythms that are sleep onset-related EEG signatures along with the subsequent neural circuitries from a sleep-deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEG-fMRI acquisitions, especially when a reference paradigm is unavailable.

Advanced dynamic statistical parametric mapping with MEG in localizing epileptogenicity of the bottom of sulcus dysplasia.

PubMed

Nakajima, Midori; Wong, Simeon; Widjaja, Elysa; Baba, Shiro; Okanishi, Tohru; Takada, Lynne; Sato, Yosuke; Iwata, Hiroki; Sogabe, Maya; Morooka, Hikaru; Whitney, Robyn; Ueda, Yuki; Ito, Tomoshiro; Yagyu, Kazuyori; Ochi, Ayako; Carter Snead, O; Rutka, James T; Drake, James M; Doesburg, Sam; Takeuchi, Fumiya; Shiraishi, Hideaki; Otsubo, Hiroshi

2018-06-01

To investigate whether advanced dynamic statistical parametric mapping (AdSPM) using magnetoencephalography (MEG) can better localize focal cortical dysplasia at bottom of sulcus (FCDB). We analyzed 15 children with diagnosis of FCDB in surgical specimen and 3 T MRI by using MEG. Using AdSPM, we analyzed a ±50 ms epoch relative to each single moving dipole (SMD) and applied summation technique to estimate the source activity. The most active area in AdSPM was defined as the location of AdSPM spike source. We compared spatial congruence between MRI-visible FCDB and (1) dipole cluster in SMD method; and (2) AdSPM spike source. AdSPM localized FCDB in 12 (80%) of 15 children whereas dipole cluster localized six (40%). AdSPM spike source was concordant within seizure onset zone in nine (82%) of 11 children with intracranial video EEG. Eleven children with resective surgery achieved seizure freedom with follow-up period of 1.9 ± 1.5 years. Ten (91%) of them had an AdSPM spike source in the resection area. AdSPM can noninvasively and neurophysiologically localize epileptogenic FCDB, whether it overlaps with the dipole cluster or not. This is the first study to localize epileptogenic FCDB using MEG. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Sensitivity and Specificity of Interictal EEG-fMRI for Detecting the Ictal Onset Zone at Different Statistical Thresholds

PubMed Central

Tousseyn, Simon; Dupont, Patrick; Goffin, Karolien; Sunaert, Stefan; Van Paesschen, Wim

2014-01-01

There is currently a lack of knowledge about electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) specificity. Our aim was to define sensitivity and specificity of blood oxygen level dependent (BOLD) responses to interictal epileptic spikes during EEG-fMRI for detecting the ictal onset zone (IOZ). We studied 21 refractory focal epilepsy patients who had a well-defined IOZ after a full presurgical evaluation and interictal spikes during EEG-fMRI. Areas of spike-related BOLD changes overlapping the IOZ in patients were considered as true positives; if no overlap was found, they were treated as false-negatives. Matched healthy case-controls had undergone similar EEG-fMRI in order to determine true-negative and false-positive fractions. The spike-related regressor of the patient was used in the design matrix of the healthy case-control. Suprathreshold BOLD changes in the brain of controls were considered as false positives, absence of these changes as true negatives. Sensitivity and specificity were calculated for different statistical thresholds at the voxel level combined with different cluster size thresholds and represented in receiver operating characteristic (ROC)-curves. Additionally, we calculated the ROC-curves based on the cluster containing the maximal significant activation. We achieved a combination of 100% specificity and 62% sensitivity, using a Z-threshold in the interval 3.4–3.5 and cluster size threshold of 350 voxels. We could obtain higher sensitivity at the expense of specificity. Similar performance was found when using the cluster containing the maximal significant activation. Our data provide a guideline for different EEG-fMRI settings with their respective sensitivity and specificity for detecting the IOZ. The unique cluster containing the maximal significant BOLD activation was a sensitive and specific marker of the IOZ. PMID:25101049

Mimickers of generalized spike and wave discharges.

PubMed

Azzam, Raed; Bhatt, Amar B

2014-06-01

Overinterpretation of benign EEG variants is a common problem that can lead to the misdiagnosis of epilepsy. We review four normal patterns that mimic generalized spike and wave discharges: phantom spike-and-wave, hyperventilation hypersynchrony, hypnagogic/ hypnopompic hypersynchrony, and mitten patterns.

Evaluation of an automated spike-and-wave complex detection algorithm in the EEG from a rat model of absence epilepsy.

PubMed

Bauquier, Sebastien H; Lai, Alan; Jiang, Jonathan L; Sui, Yi; Cook, Mark J

2015-10-01

The aim of this prospective blinded study was to evaluate an automated algorithm for spike-and-wave discharge (SWD) detection applied to EEGs from genetic absence epilepsy rats from Strasbourg (GAERS). Five GAERS underwent four sessions of 20-min EEG recording. Each EEG was manually analyzed for SWDs longer than one second by two investigators and automatically using an algorithm developed in MATLAB®. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the manual (reference) versus the automatic (test) methods. The results showed that the algorithm had specificity, sensitivity, PPV and NPV >94%, comparable to published methods that are based on analyzing EEG changes in the frequency domain. This provides a good alternative as a method designed to mimic human manual marking in the time domain.

Effects of oral amines on the EEG.

PubMed

Scott, D F; Moffett, A M; Swash, M

1977-02-01

Oral tyramine activated pre-existing episodic EEG abnormalities--namely, sharp waves, spike and wave, and localised theta activity--in epileptic patients. Little change was found in the EEGs of migrainous subjects after chocolate or beta-phenylethylamine. The implications of the findings with tyramine are discussed.

Multichannel interictal spike activity detection using time-frequency entropy measure.

PubMed

Thanaraj, Palani; Parvathavarthini, B

2017-06-01

Localization of interictal spikes is an important clinical step in the pre-surgical assessment of pharmacoresistant epileptic patients. The manual selection of interictal spike periods is cumbersome and involves a considerable amount of analysis workload for the physician. The primary focus of this paper is to automate the detection of interictal spikes for clinical applications in epilepsy localization. The epilepsy localization procedure involves detection of spikes in a multichannel EEG epoch. Therefore, a multichannel Time-Frequency (T-F) entropy measure is proposed to extract features related to the interictal spike activity. Least squares support vector machine is used to train the proposed feature to classify the EEG epochs as either normal or interictal spike period. The proposed T-F entropy measure, when validated with epilepsy dataset of 15 patients, shows an interictal spike classification accuracy of 91.20%, sensitivity of 100% and specificity of 84.23%. Moreover, the area under the curve of Receiver Operating Characteristics plot of 0.9339 shows the superior classification performance of the proposed T-F entropy measure. The results of this paper show a good spike detection accuracy without any prior information about the spike morphology.

[Quantitative evaluation of inhibitory effects of epileptic spikes on theta rhythms in the network of hippocampal CA3 and entorhinal cortex in patients with temporal lobe epilepsy].

PubMed

Ge, Man-Ling; Guo, Jun-Dan; Chen, Sheng-Hua; Zhang, Ji-Chang; Fu, Xiao-Xuan; Chen, Yu-Min

2017-02-25

Epileptic spike is an indicator of hyper-excitability and hyper-synchrony in the neural networks. The inhibitory effects of spikes on theta rhythms (4-8 Hz) might be helpful to understand the mechanism of epileptic damage on the cognitive functions. To quantitatively evaluate the inhibitory effects of spikes on theta rhythms, intracerebral electroencephalogram (EEG) recordings with both sporadic spikes (SSs) and spike-free transient period between adjacent spikes were selected in 4 patients in the status of rapid eyes movement (REM) sleep with temporal lobe epilepsy (TLE) under the pre-surgical monitoring. The electrodes of hippocampal CA3 and entorhinal cortex (EC) were employed, since CA3 and EC built up one of key loops to investigate cognition and epilepsy. These SSs occurred only in CA3, only in EC, or in both CA3 and EC synchronously. Theta power was respectively estimated around SSs and during the spike-free transient period by Gabor wavelet transform and Hilbert transform. The intermittent extent was then estimated to represent for the loss of theta rhythms during the spike-free transient period. The following findings were obtained: (1) The prominent rhythms were in theta frequency band; (2) The spikes could transiently reduce theta power, and the inhibitory effect was severer around SSs in both CA3 and EC synchronously than that around either SSs only in EC or SSs only in CA3; (3) During the spike-free transient period, theta rhythms were interrupted with the intermittent theta rhythms left and theta power level continued dropping, implying the inhibitory effect was sustained. Additionally, the intermittent extent of theta rhythms was converged to the inhibitory extent around SSs; (4) The average theta power level during the spike-free transient period might not be in line with the inhibitory extent of theta rhythms around SSs. It was concluded that the SSs had negative effects on theta rhythms transiently and directly, the inhibitory effects aroused by SSs sustained during the spike-free transient period and were directly related to the intermittent extent. It was indicated that the loss of theta rhythms might qualify exactly the sustained inhibitory effects on theta rhythms aroused by spikes in EEG. The work provided an argumentation about the relationship between the transient negative impact of interictal spike and the loss of theta rhythms during spike-free activity for the first time, offered an intuitive methodology to estimate the inhibitory effect of spikes by EEG, and might be helpful to the analysis of EEG rhythms based on local field potentials (LFPs) in deep brain.

Effects of oral amines on the EEG.

PubMed Central

Scott, D F; Moffett, A M; Swash, M

1977-01-01

Oral tyramine activated pre-existing episodic EEG abnormalities--namely, sharp waves, spike and wave, and localised theta activity--in epileptic patients. Little change was found in the EEGs of migrainous subjects after chocolate or beta-phenylethylamine. The implications of the findings with tyramine are discussed. Images PMID:864482

Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution

NASA Astrophysics Data System (ADS)

Fukami, Tadanori; Shimada, Takamasa; Ishikawa, Bunnoshin

2018-06-01

Objective. In the current study, we tested a proposed method for fast spike detection in electroencephalography (EEG). Approach. We performed eigenvalue analysis in two-dimensional space spanned by gradients calculated from two neighboring samples to detect high-amplitude negative peaks. We extracted the spike candidates by imposing restrictions on parameters regarding spike shape and eigenvalues reflecting detection characteristics of individual medical doctors. We subsequently performed clustering, classifying detected peaks by considering the amplitude distribution at 19 scalp electrodes. Clusters with a small number of candidates were excluded. We then defined a score for eliminating spike candidates for which the pattern of detected electrodes differed from the overall pattern in a cluster. Spikes were detected by setting the score threshold. Main results. Based on visual inspection by a psychiatrist experienced in EEG, we evaluated the proposed method using two statistical measures of precision and recall with respect to detection performance. We found that precision and recall exhibited a trade-off relationship. The average recall value was 0.708 in eight subjects with the score threshold that maximized the F-measure, with 58.6  ±  36.2 spikes per subject. Under this condition, the average precision was 0.390, corresponding to a false positive rate 2.09 times higher than the true positive rate. Analysis of the required processing time revealed that, using a general-purpose computer, our method could be used to perform spike detection in 12.1% of the recording time. The process of narrowing down spike candidates based on shape occupied most of the processing time. Significance. Although the average recall value was comparable with that of other studies, the proposed method significantly shortened the processing time.

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

Interictal EEG spikes identify the region of seizure onset in some, but not all pediatric epilepsy patients

PubMed Central

Marsh, Eric D.; Peltzer, Bradley; Brown, Merritt W.; Wusthoff, Courtney; Storm, Phillip B.; Litt, Brian; Porter, Brenda E.

2010-01-01

Purpose The role of sharps and spikes, interictal epileptiform discharges (IEDs), in guiding epilepsy surgery in children remains controversial, particularly with intracranial EEG (IEEG). While ictal recording is the mainstay of localizing epileptic networks for surgical resection, current practice dictates removing regions generating frequent IEDs if they are near the ictal onset zone. Indeed, past studies suggest an inconsistent relationship between IED and seizure onset location, though these studies were based upon relatively short EEG epochs. Methods We employ a previously validated, computerized spike detector, to measure and localize IED activity over prolonged, representative segments of IEEG recorded from 19 children with intractable, mostly extra temporal lobe epilepsy. Approximately 8 hours of IEEG, randomly selected thirty-minute segments of continuous interictal IEEG per patient were analyzed over all intracranial electrode contacts. Results When spike frequency was averaged over the 16-time segments, electrodes with the highest mean spike frequency were found to be within the seizure onset region in 11 of 19 patients. There was significant variability between individual 30-minute segments in these patients, indicating that large statistical samples of interictal activity were required for improved localization. Low voltage fast EEG at seizure onset was the only clinical factor predicting IED localization to the seizure onset region. Conclusions Our data suggest that automated IED detection over multiple representative samples of IEEG may be of utility in planning epilepsy surgery for children with intractable epilepsy. Further research is required to better determine which patients may benefit from this technique a priori. PMID:19780794

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

PubMed

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

2013-01-01

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

Surface mapping of spike potential fields: experienced EEGers vs. computerized analysis.

PubMed

Koszer, S; Moshé, S L; Legatt, A D; Shinnar, S; Goldensohn, E S

1996-03-01

An EEG epileptiform spike focus recorded with scalp electrodes is clinically localized by visual estimation of the point of maximal voltage and the distribution of its surrounding voltages. We compared such estimated voltage maps, drawn by experienced electroencephalographers (EEGers), with a computerized spline interpolation technique employed in the commercially available software package FOCUS. Twenty-two spikes were recorded from 15 patients during long-term continuous EEG monitoring. Maps of voltage distribution from the 28 electrodes surrounding the points of maximum change in slope (the spike maximum) were constructed by the EEGer. The same points of maximum spike and voltage distributions at the 29 electrodes were mapped by computerized spline interpolation and a comparison between the two methods was made. The findings indicate that the computerized spline mapping techniques employed in FOCUS construct voltage maps with similar maxima and distributions as the maps created by experienced EEGers. The dynamics of spike activity, including correlations, are better visualized using the computerized technique than by manual interpretation alone. Its use as a technique for spike localization is accurate and adds information of potential clinical value.

Headache Following Occipital Brain Lesion: A Case of Migraine Triggered by Occipital Spikes?

PubMed

Vollono, Catello; Mariotti, Paolo; Losurdo, Anna; Giannantoni, Nadia Mariagrazia; Mazzucchi, Edoardo; Valentini, Piero; De Rose, Paola; Della Marca, Giacomo

2015-10-01

This study describes the case of an 8-year-old boy who developed a genuine migraine after the surgical excision, from the right occipital lobe, of brain abscesses due to selective infestation of the cerebrum by Entamoeba histolytica. After the surgical treatment, the boy presented daily headaches with typical migraine features, including right-side parieto-temporal pain, nausea, vomiting, and photophobia. Electroencephalography (EEG) showed epileptiform discharges in the right occipital lobe, although he never presented seizures. Clinical and neurophysiological observations were performed, including video-EEG and polygraphic recordings. EEG showed "interictal" epileptiform discharges in the right occipital lobe. A prolonged video-EEG recording performed before, during, and after an acute attack ruled out ictal or postictal migraine. In this boy, an occipital lesion caused occipital epileptiform EEG discharges without seizures, probably prevented by the treatment. We speculate that occipital spikes, in turn, could have caused a chronic headache with features of migraine without aura. Occipital epileptiform discharges, even in absence of seizures, may trigger a genuine migraine, probably by means of either the trigeminovascular or brainstem system. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Detection of changes of high-frequency activity by statistical time-frequency analysis in epileptic spikes

PubMed Central

Kobayashi, Katsuhiro; Jacobs, Julia; Gotman, Jean

2013-01-01

Objective A novel type of statistical time-frequency analysis was developed to elucidate changes of high-frequency EEG activity associated with epileptic spikes. Methods The method uses the Gabor Transform and detects changes of power in comparison to background activity using t-statistics that are controlled by the false discovery rate (FDR) to correct type I error of multiple testing. The analysis was applied to EEGs recorded at 2000 Hz from three patients with mesial temporal lobe epilepsy. Results Spike-related increase of high-frequency oscillations (HFOs) was clearly shown in the FDR-controlled t-spectra: it was most dramatic in spikes recorded from the hippocampus when the hippocampus was the seizure onset zone (SOZ). Depression of fast activity was observed immediately after the spikes, especially consistently in the discharges from the hippocampal SOZ. It corresponded to the slow wave part in case of spike-and-slow-wave complexes, but it was noted even in spikes without apparent slow waves. In one patient, a gradual increase of power above 200 Hz preceded spikes. Conclusions FDR-controlled t-spectra clearly detected the spike-related changes of HFOs that were unclear in standard power spectra. Significance We developed a promising tool to study the HFOs that may be closely linked to the pathophysiology of epileptogenesis. PMID:19394892

Epileptic encephalopathy with continuous spike-waves during sleep: the need for transition from childhood to adulthood medical care appears to be related to etiology.

PubMed

de Saint-Martin, Anne; Rudolf, Gabrielle; Seegmuller, Caroline; Valenti-Hirsch, Maria Paola; Hirsch, Edouard

2014-08-01

Epileptic encephalopathy with continuous diffuse spike-waves during slow-wave sleep (ECSWS) presents clinically with infrequent nocturnal focal seizures, atypical absences related to secondary bilateral synchrony, negative myoclonia, and atonic and rare generalized tonic-clonic seizures. The unique electroencephalography (EEG) pattern found in ECSWS consists of continuous, diffuse, bilateral spike-waves during slow-wave sleep. Despite the eventual disappearance of clinical seizures and EEG abnormalities by adolescence, the prognosis is guarded in most cases because of neuropsychological and behavioral deficits. ECSWS has a heterogeneous etiology (genetic, structural, and unknown). Because epilepsy and electroencephalography (EEG) abnormalities in epileptic encephalopathy with continuous diffuse spike-waves during slow-wave sleep (ECSWS) are self-limited and age related, the need for ongoing medical care and transition to adult care might be questioned. For adolescents in whom etiology remains unknown (possibly genetic) and who experience the disappearance of seizures and EEG abnormalities, there is rarely need for long-term neurologic follow-up, because often a relatively normal cognitive and social evolution follows. However, the majority of patients with structural and possibly "genetic syndromic" etiologies will have persistent cognitive deficits and will need suitable socioeducative care. Therefore, the transition process in ECSWS will depend mainly on etiology and its related features (epileptic active phase duration, and cognitive and behavioral evolution) and revolve around neuropsychological and social support rather than medical and pharmacologic follow-up. Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.

Levetiracetam reduces the frequency of interictal epileptiform discharges during NREM sleep in children with ADHD.

PubMed

Bakke, Kristin A; Larsson, Pål G; Eriksson, Ann-Sofie; Eeg-Olofsson, Orvar

2011-11-01

Symptoms of attention deficit hyperactivity disorder (ADHD) are more common in children with epilepsy than in the general paediatric population. Epileptiform discharges in EEG may be seen in children with ADHD also in those without seizure disorders. Sleep enhances these discharges which may be suppressed by levetiracetam. To assess the effect of levetiracetam on focal epileptiform discharges during sleep in children with ADHD. In this retrospective study a new semi-automatic quantitative method based on the calculation of spike index in 24-h ambulatory EEG recordings was applied. Thirty-five ADHD children, 17 with focal epilepsy, one with generalised epilepsy, and 17 with no seizure disorder were evaluated. Follow-up 24-h EEG recordings were performed after a median time of four months. Mean spike index was 50 prior to levetiracetam treatment and 21 during treatment. Seventeen children had no focal interictal epileptiform discharges in EEG at follow-up. Five children had a more than 50% reduction in spike index. Thus, a more than 50% reduction in spike index was found in 22/35 children (63%). Out of these an improved behaviour was noticed in 13 children (59%). This study shows that treatment with levetiracetam reduces interictal epileptiform discharges in children with ADHD. There is a complex relationship between epilepsy, ADHD and epileptiform activity, why it is a need for prospective studies in larger sample sizes, also to ascertain clinical benefits. Copyright © 2011 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.

Epileptogenic zone localization using magnetoencephalography predicts seizure freedom in epilepsy surgery

PubMed Central

Englot, Dario J.; Nagarajan, Srikantan S.; Imber, Brandon S.; Raygor, Kunal P.; Honma, Susanne M.; Mizuiri, Danielle; Mantle, Mary; Knowlton, Robert C.; Kirsch, Heidi E.; Chang, Edward F.

2015-01-01

Objective The efficacy of epilepsy surgery depends critically upon successful localization of the epileptogenic zone. Magnetoencephalography (MEG) enables non-invasive detection of interictal spike activity in epilepsy, which can then be localized in three dimensions using magnetic source imaging (MSI) techniques. However, the clinical value of MEG in the pre-surgical epilepsy evaluation is not fully understood, as studies to date are limited by either a lack of long-term seizure outcomes or small sample size. Methods We performed a retrospective cohort study of focal epilepsy patients who received MEG for interictal spike mapping followed by surgical resection at our institution. Results We studied 132 surgical patients, with mean post-operative follow-up of 3.6 years (minimum 1 year). Dipole source modelling was successful in 103 (78%) patients, while no interictal spikes were seen in others. Among patients with successful dipole modelling, MEG findings were concordant with and specific to: i) the region of resection in 66% of patients, ii) invasive electrocorticography (ECoG) findings in 67% of individuals, and iii) the MRI abnormality in 74% of cases. MEG showed discordant lateralization in ~5% of cases. After surgery, 70% of all patients achieved seizure-freedom (Engel class I outcome). Whereas 85% of patients with concordant and specific MEG findings became seizure-free, this outcome was achieved by only 37% of individuals with MEG findings that were non-specific or discordant with the region of resection (χ2 = 26.4, p < 0.001). MEG reliability was comparable in patients with or without localized scalp EEG, and overall, localizing MEG findings predicted seizure freedom with an odds ratio of 5.11 (2.23–11.8, 95% CI). Significance MEG is a valuable tool for non-invasive interictal spike mapping in epilepsy surgery, including patients with non-localized findings on long-term EEG monitoring, and localization of the epileptogenic zone using MEG is associated with improved seizure outcomes. PMID:25921215

Ripples on spikes show increased phase-amplitude coupling in mesial temporal lobe epilepsy seizure onset zones

PubMed Central

Weiss, Shennan A; Orosz, Iren; Salamon, Noriko; Moy, Stephanie; Wei, Linqing; Van ’t Klooster, Maryse A; Knight, Robert T; Harper, Ronald M; Bragin, Anatol; Fried, Itzhak; Engel, Jerome; Staba, Richard J

2016-01-01

Objective Ripples (80–150 Hz) recorded from clinical macroelectrodes have been shown to be an accurate biomarker of epileptogenic brain tissue. We investigated coupling between epileptiform spike phase and ripple amplitude to better understand the mechanisms that generate this type of pathological ripple (pRipple) event. Methods We quantified phase amplitude coupling (PAC) between epileptiform EEG spike phase and ripple amplitude recorded from intracranial depth macroelectrodes during episodes of sleep in 12 patients with mesial temporal lobe epilepsy. PAC was determined by 1) a phasor transform that corresponds to the strength and rate of ripples coupled with spikes, and a 2) ripple-triggered average to measure the strength, morphology, and spectral frequency of the modulating and modulated signals. Coupling strength was evaluated in relation to recording sites within and outside the seizure onset zone (SOZ). Results Both the phasor transform and ripple-triggered averaging methods showed ripple amplitude was often robustly coupled with epileptiform EEG spike phase. Coupling was more regularly found inside than outside the SOZ, and coupling strength correlated with the likelihood a macroelectrode’s location was within the SOZ (p<0.01). The ratio of the rate of ripples coupled with EEG spikes inside the SOZ to rates of coupled ripples in non-SOZ was greater than the ratio of rates of ripples on spikes detected irrespective of coupling (p<0.05). Coupling strength correlated with an increase in mean normalized ripple amplitude (p<0.01), and a decrease in mean ripple spectral frequency (p<0.05). Significance Generation of low-frequency (80–150 Hz) pRipples in the SOZ involves coupling between epileptiform spike phase and ripple amplitude. The changes in excitability reflected as epileptiform spikes may also cause clusters of pathologically interconnected bursting neurons to grow and synchronize into aberrantly large neuronal assemblies. PMID:27723936

Automatic interpretation and writing report of the adult waking electroencephalogram.

PubMed

Shibasaki, Hiroshi; Nakamura, Masatoshi; Sugi, Takenao; Nishida, Shigeto; Nagamine, Takashi; Ikeda, Akio

2014-06-01

Automatic interpretation of the EEG has so far been faced with significant difficulties because of a large amount of spatial as well as temporal information contained in the EEG, continuous fluctuation of the background activity depending on changes in the subject's vigilance and attention level, the occurrence of paroxysmal activities such as spikes and spike-and-slow-waves, contamination of the EEG with a variety of artefacts and the use of different recording electrodes and montages. Therefore, previous attempts of automatic EEG interpretation have been focussed only on a specific EEG feature such as paroxysmal abnormalities, delta waves, sleep stages and artefact detection. As a result of a long-standing cooperation between clinical neurophysiologists and system engineers, we report for the first time on a comprehensive, computer-assisted, automatic interpretation of the adult waking EEG. This system analyses the background activity, intermittent abnormalities, artefacts and the level of vigilance and attention of the subject, and automatically presents its report in written form. Besides, it also detects paroxysmal abnormalities and evaluates the effects of intermittent photic stimulation and hyperventilation on the EEG. This system of automatic EEG interpretation was formed by adopting the strategy that the qualified EEGers employ for the systematic visual inspection. This system can be used as a supplementary tool for the EEGer's visual inspection, and for educating EEG trainees and EEG technicians. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively.

PubMed

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

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). 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. 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). 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. Strategies to improve the spatial resolution of intracranial EEG and reduce artifact can help improve the clinical utility of HFO biomarkers. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Effective Synchronization of EEG and EMG for Mobile Brain/Body Imaging in Clinical Settings.

PubMed

Artoni, Fiorenzo; Barsotti, Annalisa; Guanziroli, Eleonora; Micera, Silvestro; Landi, Alberto; Molteni, Franco

2017-01-01

Mobile Brain/Body Imaging (MoBI) is rapidly gaining traction as a new imaging modality to study how cognitive processes support locomotion. Electroencephalogram (EEG) and electromyogram (EMG), due to their time resolution, non-invasiveness and portability are the techniques of choice for MoBI, but synchronization requirements among others restrict its use to high-end research facilities. Here we test the effectiveness of a technique that enables us to achieve MoBI-grade synchronization of EEG and EMG, even when other strategies (such as Lab Streaming Layer (LSL)) cannot be used e.g., due to the unavailability of proprietary Application Programming Interfaces (APIs), which is often the case in clinical settings. The proposed strategy is that of aligning several spikes at the beginning and end of the session. We delivered a train of spikes to the EEG amplifier and EMG electrodes every 2 s over a 10-min time period. We selected a variable number of spikes (from 1 to 10) both at the beginning and end of the time series and linearly resampled the data so as to align them. We then compared the misalignment of the "middle" spikes over the whole recording to test for jitter and synchronization drifts, highlighting possible nonlinearities (due to hardware filters) and estimated the maximum length of the recording to achieve a [-5 to 5] ms misalignment range. We demonstrate that MoBI-grade synchronization can be achieved within 10-min recordings with a 1.7 ms jitter and [-5 5] ms misalignment range. We show that repeated spike delivery can be used to test online synchronization options and to troubleshoot synchronization issues over EEG and EMG. We also show that synchronization cannot rely only on the equipment sampling rate advertised by manufacturers. The synchronization strategy described can be used virtually in every clinical environment, and may increase the interest among a broader spectrum of clinicians and researchers in the MoBI framework, ultimately leading to a better understanding of the brain processes underlying locomotion control and the development of more effective rehabilitation approaches.

Interictal Epileptiform Discharges (IEDs) classification in EEG data of epilepsy patients

NASA Astrophysics Data System (ADS)

Puspita, J. W.; Soemarno, G.; Jaya, A. I.; Soewono, E.

2017-12-01

Interictal Epileptiform Dischargers (IEDs), which consists of spike waves and sharp waves, in human electroencephalogram (EEG) are characteristic signatures of epilepsy. Spike waves are characterized by a pointed peak with a duration of 20-70 ms, while sharp waves has a duration of 70-200 ms. The purpose of the study was to classify spike wave and sharp wave of EEG data of epilepsy patients using Backpropagation Neural Network. The proposed method consists of two main stages: feature extraction stage and classification stage. In the feature extraction stage, we use frequency, amplitude and statistical feature, such as mean, standard deviation, and median, of each wave. The frequency values of the IEDs are very sensitive to the selection of the wave baseline. The selected baseline must contain all data of rising and falling slopes of the IEDs. Thus, we have a feature that is able to represent the type of IEDs, appropriately. The results show that the proposed method achieves the best classification results with the recognition rate of 93.75 % for binary sigmoid activation function and learning rate of 0.1.

Rett syndrome: EEG presentation.

PubMed

Robertson, R; Langill, L; Wong, P K; Ho, H H

1988-11-01

Rett syndrome, a degenerative neurological disorder of girls, has a classical presentation and typical EEG findings. The electroencephalograms (EEGs) of 7 girls whose records have been followed from the onset of symptoms to the age of 5 or more are presented. These findings are tabulated with the Clinical Staging System of Hagberg and Witt-Engerström (1986). The records show a progressive deterioration in background rhythms in waking and sleep. The abnormalities of the background activity may only become evident at 4-5 years of age or during stage 2--the Rapid Destructive Stage. The marked contrast between waking and sleep background may not occur until stage 3--the Pseudostationary Stage. In essence EEG changes appear to lag behind clinical symptomatology by 1-3 years. An unexpected, but frequent, abnormality was central spikes seen in 5 of 7 girls. They appeared to be age related and could be evoked by tactile stimulation in 2 patients. We hypothesize that the prominent 'hand washing' mannerism may be self-stimulating and related to the appearance of central spike discharges.

Understanding the pathophysiology of reflex epilepsy using simultaneous EEG-fMRI.

PubMed

Sandhya, Manglore; Bharath, Rose Dawn; Panda, Rajanikant; Chandra, S R; Kumar, Naveen; George, Lija; Thamodharan, A; Gupta, Arun Kumar; Satishchandra, P

2014-03-01

Measuring neuro-haemodynamic correlates in the brain of epilepsy patients using EEG-fMRI has opened new avenues in clinical neuroscience, as these are two complementary methods for understanding brain function. In this study, we investigated three patients with drug-resistant reflex epilepsy using EEG-fMRI. Different types of reflex epilepsy such as eating, startle myoclonus, and hot water epilepsy were included in the study. The analysis of EEG-fMRI data was based on the visual identification of interictal epileptiform discharges on scalp EEG. The convolution of onset time and duration of these epilepsy spikes was estimated, and using these condition-specific effects in a general linear model approach, we evaluated activation of fMRI. Patients with startle myoclonus epilepsy experienced epilepsy in response to sudden sound or touch, in association with increased delta and theta activity with a spike-and-slow-wave pattern of interictal epileptiform discharges on EEG and fronto-parietal network activation pattern on SPECT and EEG-fMRI. Eating epilepsy was triggered by sight or smell of food and fronto-temporal discharges were noted on video-EEG (VEEG). Similarly, fronto-temporo-parietal involvement was noted on SPECT and EEG-fMRI. Hot water epilepsy was triggered by contact with hot water either in the bath or by hand immersion, and VEEG showed fronto-parietal involvement. SPECT and EEG fMRI revealed a similar fronto-parietal-occipital involvement. From these results, we conclude that continuous EEG recording can improve the modelling of BOLD changes related to interictal epileptic activity and this can thus be used to understand the neuro-haemodynamic substrates involved in reflex epilepsy.

Spike persistence and normalization in benign epilepsy with centrotemporal spikes - Implications for management.

PubMed

Kim, Hunmin; Kim, Soo Yeon; Lim, Byung Chan; Hwang, Hee; Chae, Jong-Hee; Choi, Jieun; Kim, Ki Joong; Dlugos, Dennis J

2018-05-10

This study was performed 1) to determine the timing of spike normalization in patients with benign epilepsy with centrotemporal spikes (BECTS); 2) to identify relationships between age of seizure onset, age of spike normalization, years of spike persistence and treatment; and 3) to assess final outcomes between groups of patients with or without spikes at the time of medication tapering. Retrospective analysis of BECTS patients confirmed by clinical data, including age of onset, seizure semiology and serial electroencephalography (EEG) from diagnosis to remission. Age at spike normalization, years of spike persistence, and time of treatment onset to spike normalization were assessed. Final seizure and EEG outcome were compared between the groups with or without spikes at the time of AED tapering. One hundred and thirty-four patients were included. Mean age at seizure onset was 7.52 ± 2.11 years. Mean age at spike normalization was 11.89 ± 2.11 (range: 6.3-16.8) years. Mean time of treatment onset to spike normalization was 4.11 ± 2.13 (range: 0.24-10.08) years. Younger age of seizure onset was correlated with longer duration of spike persistence (r = -0.41, p < 0.001). In treated patients, spikes persisted for 4.1 ± 1.95 years, compared with 2.9 ± 1.97 years in untreated patients. No patients had recurrent seizures after AED was discontinued, regardless of the presence/absence of spikes at time of AED tapering. Years of spike persistence was longer in early onset BECTS patients. Treatment with AEDs did not shorten years of spike persistence. Persistence of spikes at time of treatment withdrawal was not associated with seizure recurrence. Copyright © 2018 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

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.

Correspondence between large-scale ictal and interictal epileptic networks revealed by single photon emission computed tomography (SPECT) and electroencephalography (EEG)-functional magnetic resonance imaging (fMRI).

PubMed

Tousseyn, Simon; Dupont, Patrick; Goffin, Karolien; Sunaert, Stefan; Van Paesschen, Wim

2015-03-01

Epilepsy is increasingly recognized as a network disorder, but the spatial relationship between ictal and interictal networks is still largely unexplored. In this work, we compared hemodynamic changes related to seizures and interictal spikes on a whole brain scale. Twenty-eight patients with refractory focal epilepsy (14 temporal and 14 extratemporal lobe) underwent both subtraction ictal single photon emission computed tomography (SPECT) coregistered to magnetic resonance imaging (MRI) (SISCOM) and spike-related electroencephalography (EEG-functional MRI (fMRI). SISCOM visualized relative perfusion changes during seizures, whereas EEG-fMRI mapped blood oxygen level-dependent (BOLD) changes related to spikes. Similarity between statistical maps of both modalities was analyzed per patient using the following two measures: (1) correlation between unthresholded statistical maps (Pearson's correlation coefficient) and (2) overlap between thresholded images (Dice coefficient). Overlap was evaluated at a regional level, for hyperperfusions and activations and for hypoperfusions and deactivations separately, using different thresholds. Nonparametric permutation tests were applied to assess statistical significance (p ≤ 0.05). We found significant and positive correlations between hemodynamic changes related to seizures and spikes in 27 (96%) of 28 cases (median correlation coefficient 0.29 [range -0.12 to 0.62]). In 20 (71%) of 28 cases, spatial overlap between hyperperfusion on SISCOM and activation on EEG-fMRI was significantly larger than expected by chance. Congruent changes were not restricted to the territory of the presumed epileptogenic zone, but could be seen at distant sites (e.g., cerebellum and basal ganglia). Overlap between ictal hypoperfusion and interictal deactivation was statistically significant in 22 (79%) of 28 patients. Despite the high rate of congruence, discrepancies were observed for both modalities. We conclude that hemodynamic changes related to seizures and spikes varied spatially with the same sign and within a common network. Overlap was present in regions nearby and distant from discharge origin. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

Inter-ictal spike detection using a database of smart templates.

PubMed

Lodder, Shaun S; Askamp, Jessica; van Putten, Michel J A M

2013-12-01

Visual analysis of EEG is time consuming and suffers from inter-observer variability. Assisted automated analysis helps by summarizing key aspects for the reviewer and providing consistent feedback. Our objective is to design an accurate and robust system for the detection of inter-ictal epileptiform discharges (IEDs) in scalp EEG. IED Templates are extracted from the raw data of an EEG training set. By construction, the templates are given the ability to learn by searching for other IEDs within the training set using a time-shifted correlation. True and false detections are remembered and classifiers are trained for improving future predictions. During detection, trained templates search for IEDs in the new EEG. Overlapping detections from all templates are grouped and form one IED. Certainty values are added based on the reliability of the templates involved. For evaluation, 2160 templates were used on an evaluation dataset of 15 continuous recordings containing 241 IEDs (0.79/min). Sensitivities up to 0.99 (7.24fp/min) were reached. To reduce false detections, higher certainty thresholds led to a mean sensitivity of 0.90 with 2.36fp/min. By using many templates, this technique is less vulnerable to variations in spike morphology. A certainty value for each detection allows the system to present findings in a more efficient manner and simplifies the review process. Automated spike detection can assist in visual interpretation of the EEG which may lead to faster review times. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Reactivity of rolandic spikes.

PubMed

Fonseca, L C; Tedrus, G M; Bastos, A; Bosco, A; Laloni, D T

1996-07-01

Factors influencing the frequency of rolandic spikes are rarely reported. We examined the influence of movement, tactile stimulation and cognitive test on the discharge rate of rolandic spikes. We studied 35 children with EEG rolandic spikes. Eighteen children had nonfebrile convulsions. Benign childhood epilepsy with centrotemporal spikes was diagnosed in 12 cases. Testing during the EEG included hand and tongue movements, cognitive tasks and tactile stimulation of face and hands. Rolandic spikes were counted by visual analysis in each phase of the test and the frequency expressed as mean number per min. During tongue movements there was a significantly lower discharge rate when compared to previous and subsequent rest phases. For the left hemisphere there was reduction in discharge rate during right hand movements and looking at colored spots but only when compared with the previous rest phase. The reduction in mean number of spikes/min by tongue movements occurred in patients with and without epilepsy. For patients with cerebral lesion the decrease in discharge rate during tongue movements was not significant whereas for those without cerebral lesion a significant reduction was evident. A decrease in the mean number of spikes/min of 50% or more was significantly more frequent among 29 patients without than among 6 patients with cerebral lesion. Our study showed the inhibition of rolandic spikes by tongue movements confirming the hypothesis that the localization of discharges is an important factor in determining its reactivity. The presence or not of cerebral lesion may be an important factor in the degree of reactivity.

The electrophysiological "delayed effect" of focal interictal epileptiform discharges. A low resolution electromagnetic tomography (LORETA) study.

PubMed

Clemens, Béla; Piros, Pálma; Bessenyei, Mónika; Varga, Edit; Puskás, Szilvia; Fekete, István

2009-08-01

Collating the findings regarding the role of focal interictal epileptiform discharges (IEDs) on CNS functions raises the possibility that IEDs might have negative impact that outlasts the duration of the spike-and-wave complexes. The aim of this study was the electrophysiological demonstration of the "delayed effect" of the IEDs. 19-channel, linked-ears referenced, digital waking EEG records of 11 children (aged 6-14 years, eight with idiopathic, three with cryptogenic focal epilepsy, showing a single spike focus) were retrospectively selected from our database. A minimum of 20 (preferably, 30), 2-s epochs containing a single focal spike-and-wave complex were selected (Spike epochs). Thereafter, Postspike-1 (Ps1), Postspike-2 (Ps2) and Postspike-3 (Ps3) epochs were selected, representing the first and second seconds (Ps1), the third and fourth seconds (Ps2) and the fifth and sixth seconds (Ps3) after the Spike epoch, respectively. Interspike epochs (Is) were selected at a distance at least 10s after the Spike epoch. Individual analysis: the frequency of interest (FOI=the individual frequency of the wave component of the IEDs), and the region of interest (ROI=the site of the IEDs) were identified by reading the raw EEG waveform and the instant power spectrum. Very narrow band LORETA (low resolution electromagnetic tomography) analysis at the FOI and ROI was carried out. Age-adjusted, Z-transformed LORETA "activity" (=current source density, amperes/meters squared) was compared in the Spike, Ps1, Ps2, Ps3 and Is epochs. the greatest (uppermost pathological) Z-scores and the greatest spatial extension of the LORETA-abnormality were always found in the Spike epochs, followed by the gradual decrease of activity in terms of severity and spatial extension in the Ps1, Ps2, Ps3 epochs. The lowest (baseline) level and extension of the abnormality was found in the Is epochs. Group analysis: average values of activity across the patients were computed for the temporal decrease of the abnormality. a clear tendency for the decrease of abnormality was demonstrated. the "delayed effect" of the IEDs was demonstrated electrophysiologically and quantified. The method may be utilized in the individual assessment of the effect of IEDs on cortical activity, the degree and temporo-spatial extension of the abnormality.

Epileptic Encephalopathies and Their Relationship to Developmental Disorders: Do Spikes Cause Autism?

ERIC Educational Resources Information Center

Tharp, Barry R.

2004-01-01

Epileptic encephalopathies are progressive clinical and electroencephalographic syndromes where deterioration is thought to be caused by frequent seizures and abundant EEG epileptiform activity. Seizures occur in approximately 10-15% of children with pervasive developmental disorders (PDD) and 8-10% have epileptiform EEG abnormalities without…

Correlation between perceived stigma and EEG paroxysmal abnormality in childhood epilepsy.

PubMed

Kanemura, Hideaki; Sano, Fumikazu; Ohyama, Tetsuo; Sugita, Kanji; Aihara, Masao

2015-11-01

We investigated the relationship between abnormal electroencephalogram (EEG) findings such as localized EEG paroxysmal abnormality (PA) and the perception of stigma to determine EEG factors associated with perceived stigma in childhood epilepsy. Participants comprised 40 patients (21 boys, 19 girls; mean age, 14.6 years) with epilepsy at enrollment. The criteria for inclusion were as follows: 1) age of 12-18 years, inclusive; 2) ≥6 months after epilepsy onset; 3) the ability to read and speak Japanese; and 4) the presence of EEG PA. Fifteen healthy seizure-free children were included as a control group. Participants were asked to rate how often they felt or acted in the ways described in the items of the Child Stigma Scale using a 5-point scale. Electroencephalogram paroxysms were classified based on the presence of spikes, sharp waves, or spike-wave complexes, whether focal or generalized. Participants showed significantly higher stigma scores than healthy subjects (p<0.01). A higher score reflects a greater perception of stigma. The average total scores of patients presenting with EEG PA at generalized, frontal, RD, midtemporal, and occipital regions were 2.3, 4.0, 2.4, 3.2, and 2.2, respectively. The scores of all questions were higher in the frontal group than those in other regions (p<0.01). Children presenting with frontal EEG PA perceived a greater stigma than children presenting with nonfrontal EEG PA (p<0.01). A relationship was identified between frontal EEG PA and a greater perception of stigma. Further studies are needed to confirm whether frontal EEG PA may function as a mediator of emotional responses such as perceived stigma in childhood epilepsy. Copyright © 2015 Elsevier Inc. All rights reserved.

The choice of the source space and the Laplacian matrix in LORETA and the spatio-temporal Kalman filter EEG inverse methods.

PubMed

Habboush, Nawar; Hamid, Laith; Japaridze, Natia; Wiegand, Gert; Heute, Ulrich; Stephani, Ulrich; Galka, Andreas; Siniatchkin, Michael

2015-08-01

The discretization of the brain and the definition of the Laplacian matrix influence the results of methods based on spatial and spatio-temporal smoothness, since the Laplacian operator is used to define the smoothness based on the neighborhood of each grid point. In this paper, the results of low resolution electromagnetic tomography (LORETA) and the spatiotemporal Kalman filter (STKF) are computed using, first, a greymatter source space with the standard definition of the Laplacian matrix and, second, using a whole-brain source space and a modified definition of the Laplacian matrix. Electroencephalographic (EEG) source imaging results of five inter-ictal spikes from a pre-surgical patient with epilepsy are used to validate the two aforementioned approaches. The results using the whole-brain source space and the modified definition of the Laplacian matrix were concentrated in a single source activation, stable, and concordant with the location of the focal cortical dysplasia (FCD) in the patient's brain compared with the results which use a grey-matter grid and the classical definition of the Laplacian matrix. This proof-of-concept study demonstrates a substantial improvement of source localization with both LORETA and STKF and constitutes a basis for further research in a large population of patients with epilepsy.

Interictal EEG spikes identify the region of electrographic seizure onset in some, but not all, pediatric epilepsy patients.

PubMed

Marsh, Eric D; Peltzer, Bradley; Brown, Merritt W; Wusthoff, Courtney; Storm, Phillip B; Litt, Brian; Porter, Brenda E

2010-04-01

The role of sharps and spikes, interictal epileptiform discharges (IEDs), in guiding epilepsy surgery in children remains controversial, particularly with intracranial electroencephalography (IEEG). Although ictal recording is the mainstay of localizing epileptic networks for surgical resection, current practice dictates removing regions generating frequent IEDs if they are near the ictal onset zone. Indeed, past studies suggest an inconsistent relationship between IED and seizure-onset location, although these studies were based upon relatively short EEG epochs. We employ a previously validated, computerized spike detector to measure and localize IED activity over prolonged, representative segments of IEEG recorded from 19 children with intractable, mostly extratemporal lobe epilepsy. Approximately 8 h of IEEG, randomly selected 30-min segments of continuous interictal IEEG per patient, were analyzed over all intracranial electrode contacts. When spike frequency was averaged over the 16-time segments, electrodes with the highest mean spike frequency were found to be within the seizure-onset region in 11 of 19 patients. There was significant variability between individual 30-min segments in these patients, indicating that large statistical samples of interictal activity were required for improved localization. Low-voltage fast EEG at seizure onset was the only clinical factor predicting IED localization to the seizure-onset region. Our data suggest that automated IED detection over multiple representative samples of IEEG may be of utility in planning epilepsy surgery for children with intractable epilepsy. Further research is required to better determine which patients may benefit from this technique a priori.

Combining EEG and eye tracking: identification, characterization, and correction of eye movement artifacts in electroencephalographic data

PubMed Central

Plöchl, Michael; Ossandón, José P.; König, Peter

2012-01-01

Eye movements introduce large artifacts to electroencephalographic recordings (EEG) and thus render data analysis difficult or even impossible. Trials contaminated by eye movement and blink artifacts have to be discarded, hence in standard EEG-paradigms subjects are required to fixate on the screen. To overcome this restriction, several correction methods including regression and blind source separation have been proposed. Yet, there is no automated standard procedure established. By simultaneously recording eye movements and 64-channel-EEG during a guided eye movement paradigm, we investigate and review the properties of eye movement artifacts, including corneo-retinal dipole changes, saccadic spike potentials and eyelid artifacts, and study their interrelations during different types of eye- and eyelid movements. In concordance with earlier studies our results confirm that these artifacts arise from different independent sources and that depending on electrode site, gaze direction, and choice of reference these sources contribute differently to the measured signal. We assess the respective implications for artifact correction methods and therefore compare the performance of two prominent approaches, namely linear regression and independent component analysis (ICA). We show and discuss that due to the independence of eye artifact sources, regression-based correction methods inevitably over- or under-correct individual artifact components, while ICA is in principle suited to address such mixtures of different types of artifacts. Finally, we propose an algorithm, which uses eye tracker information to objectively identify eye-artifact related ICA-components (ICs) in an automated manner. In the data presented here, the algorithm performed very similar to human experts when those were given both, the topographies of the ICs and their respective activations in a large amount of trials. Moreover it performed more reliable and almost twice as effective than human experts when those had to base their decision on IC topographies only. Furthermore, a receiver operating characteristic (ROC) analysis demonstrated an optimal balance of false positive and false negative at an area under curve (AUC) of more than 0.99. Removing the automatically detected ICs from the data resulted in removal or substantial suppression of ocular artifacts including microsaccadic spike potentials, while the relevant neural signal remained unaffected. In conclusion the present work aims at a better understanding of individual eye movement artifacts, their interrelations and the respective implications for eye artifact correction. Additionally, the proposed ICA-procedure provides a tool for optimized detection and correction of eye movement-related artifact components. PMID:23087632

Relationship between cortical state and spiking activity in the lateral geniculate nucleus of marmosets

PubMed Central

Pietersen, Alexander N.J.; Cheong, Soon Keen; Munn, Brandon; Gong, Pulin; Solomon, Samuel G.

2017-01-01

Key points How parallel are the primate visual pathways? In the present study, we demonstrate that parallel visual pathways in the dorsal lateral geniculate nucleus (LGN) show distinct patterns of interaction with rhythmic activity in the primary visual cortex (V1).In the V1 of anaesthetized marmosets, the EEG frequency spectrum undergoes transient changes that are characterized by fluctuations in delta‐band EEG power.We show that, on multisecond timescales, spiking activity in an evolutionary primitive (koniocellular) LGN pathway is specifically linked to these slow EEG spectrum changes. By contrast, on subsecond (delta frequency) timescales, cortical oscillations can entrain spiking activity throughout the entire LGN.Our results are consistent with the hypothesis that, in waking animals, the koniocellular pathway selectively participates in brain circuits controlling vigilance and attention. Abstract The major afferent cortical pathway in the visual system passes through the dorsal lateral geniculate nucleus (LGN), where nerve signals originating in the eye can first interact with brain circuits regulating visual processing, vigilance and attention. In the present study, we investigated how ongoing and visually driven activity in magnocellular (M), parvocellular (P) and koniocellular (K) layers of the LGN are related to cortical state. We recorded extracellular spiking activity in the LGN simultaneously with local field potentials (LFP) in primary visual cortex, in sufentanil‐anaesthetized marmoset monkeys. We found that asynchronous cortical states (marked by low power in delta‐band LFPs) are linked to high spike rates in K cells (but not P cells or M cells), on multisecond timescales. Cortical asynchrony precedes the increases in K cell spike rates by 1–3 s, implying causality. At subsecond timescales, the spiking activity in many cells of all (M, P and K) classes is phase‐locked to delta waves in the cortical LFP, and more cells are phase‐locked during synchronous cortical states than during asynchronous cortical states. The switch from low‐to‐high spike rates in K cells does not degrade their visual signalling capacity. By contrast, during asynchronous cortical states, the fidelity of visual signals transmitted by K cells is improved, probably because K cell responses become less rectified. Overall, the data show that slow fluctuations in cortical state are selectively linked to K pathway spiking activity, whereas delta‐frequency cortical oscillations entrain spiking activity throughout the entire LGN, in anaesthetized marmosets. PMID:28116750

EEG power as a biomarker to predict the outcome after cardiac arrest and cardiopulmonary resuscitation induced global ischemia.

PubMed

Weitzel, Lindsay-Rae; Sampath, Dayalan; Shimizu, Kaori; White, Andrew M; Herson, Paco S; Raol, Yogendra H

2016-11-15

Cardiac arrest (CA) is a major cause of mortality and survivors often develop neurologic deficits. The objective of this study was to determine the effect of CA and cardiopulmonary resuscitation (CPR) in mice on the EEG and neurologic outcomes, and identify biomarkers that can prognosticate poor outcomes. Video-EEG records were obtained at various periods following CA-CPR and examined manually to determine the presence of spikes and sharp-waves, and seizures. EEG power was calculated using a fast Fourier transform (FFT) algorithm. Fifty percent mice died within 72h following CA and successful CPR. Universal suppression of the background EEG was observed in all mice following CA-CPR, however, a more severe and sustained reduction in EEG power occurred in the mice that did not survive beyond 72h than those that survived until sacrificed. Spikes and sharp wave activity appeared in the cortex and hippocampus of all mice, but only one out of eight mice developed a purely electrographic seizure in the acute period after CA-CPR. Interestingly, none of the mice that died experienced any acute seizures. At 10days after the CA-CPR, 25% of the mice developed spontaneous convulsive and nonconvulsive seizures that remained restricted to the hippocampus. The frequency of nonconvulsive seizures was higher than that of convulsive seizures. A strong association between changes in EEG power and mortality following CA-CPR were observed in our study. Therefore, we suggest that the EEG power can be used to prognosticate mortality following CA-CPR induced global ischemia. Copyright © 2016 Elsevier Inc. All rights reserved.

The Antiepileptic Drug Levetiracetam Suppresses Non-Convulsive Seizure Activity and Reduces Ischemic Brain Damage in Rats Subjected to Permanent Middle Cerebral Artery Occlusion

PubMed Central

Cuomo, Ornella; Rispoli, Vincenzo; Leo, Antonio; Politi, Giovanni Bosco; Vinciguerra, Antonio; di Renzo, Gianfranco; Cataldi, Mauro

2013-01-01

The antiepileptic drug Levetiracetam (Lev) has neuroprotective properties in experimental stroke, cerebral hemorrhage and neurotrauma. In these conditions, non-convulsive seizures (NCSs) propagate from the core of the focal lesion into perilesional tissue, enlarging the damaged area and promoting epileptogenesis. Here, we explore whether Lev neuroprotective effect is accompanied by changes in NCS generation or propagation. In particular, we performed continuous EEG recordings before and after the permanent occlusion of the middle cerebral artery (pMCAO) in rats that received Lev (100 mg/kg) or its vehicle immediately before surgery. Both in Lev-treated and in control rats, EEG activity was suppressed after pMCAO. In control but not in Lev-treated rats, EEG activity reappeared approximately 30-45 min after pMCAO. It initially consisted in single spikes and, then, evolved into spike-and-wave and polyspike-and-wave discharges. In Lev-treated rats, only rare spike events were observed and the EEG power was significantly smaller than in controls. Approximately 24 hours after pMCAO, EEG activity increased in Lev-treated rats because of the appearance of polyspike events whose power was, however, significantly smaller than in controls. In rats sacrificed 24 hours after pMCAO, the ischemic lesion was approximately 50% smaller in Lev-treated than in control rats. A similar neuroprotection was observed in rats sacrificed 72 hours after pMCAO. In conclusion, in rats subjected to pMCAO, a single Lev injection suppresses NCS occurrence for at least 24 hours. This electrophysiological effect could explain the long lasting reduction of ischemic brain damage caused by this drug. PMID:24236205

A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data From Healthy Versus Addiction Treated Versus Addiction Not Treated Subjects.

PubMed

Doborjeh, Maryam Gholami; Wang, Grace Y; Kasabov, Nikola K; Kydd, Robert; Russell, Bruce

2016-09-01

This paper introduces a method utilizing spiking neural networks (SNN) for learning, classification, and comparative analysis of brain data. As a case study, the method was applied to electroencephalography (EEG) data collected during a GO/NOGO cognitive task performed by untreated opiate addicts, those undergoing methadone maintenance treatment (MMT) for opiate dependence and a healthy control group. the method is based on an SNN architecture called NeuCube, trained on spatiotemporal EEG data. NeuCube was used to classify EEG data across subject groups and across GO versus NOGO trials, but also facilitated a deeper comparative analysis of the dynamic brain processes. This analysis results in a better understanding of human brain functioning across subject groups when performing a cognitive task. In terms of the EEG data classification, a NeuCube model obtained better results (the maximum obtained accuracy: 90.91%) when compared with traditional statistical and artificial intelligence methods (the maximum obtained accuracy: 50.55%). more importantly, new information about the effects of MMT on cognitive brain functions is revealed through the analysis of the SNN model connectivity and its dynamics. this paper presented a new method for EEG data modeling and revealed new knowledge on brain functions associated with mental activity which is different from the brain activity observed in a resting state of the same subjects.

Beamforming applied to surface EEG improves ripple visibility.

PubMed

van Klink, Nicole; Mol, Arjen; Ferrier, Cyrille; Hillebrand, Arjan; Huiskamp, Geertjan; Zijlmans, Maeike

2018-01-01

Surface EEG can show epileptiform ripples in people with focal epilepsy, but identification is impeded by the low signal-to-noise ratio of the electrode recordings. We used beamformer-based virtual electrodes to improve ripple identification. We analyzed ten minutes of interictal EEG of nine patients with refractory focal epilepsy. EEGs with more than 60 channels and 20 spikes were included. We computed ∼79 virtual electrodes using a scalar beamformer and marked ripples (80-250 Hz) co-occurring with spikes in physical and virtual electrodes. Ripple numbers in physical and virtual electrodes were compared, and sensitivity and specificity of ripples for the region of interest (ROI; based on clinical information) were determined. Five patients had ripples in the physical electrodes and eight in the virtual electrodes, with more ripples in virtual than in physical electrodes (101 vs. 57, p = .007). Ripples in virtual electrodes predicted the ROI better than physical electrodes (AUC 0.65 vs. 0.56, p = .03). Beamforming increased ripple visibility in surface EEG. Virtual ripples predicted the ROI better than physical ripples, although sensitivity was still poor. Beamforming can facilitate ripple identification in EEG. Ripple localization needs to be improved to enable its use for presurgical evaluation in people with epilepsy. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Spreading Photoparoxysmal EEG Response is Associated with an Abnormal Cortical Excitability Pattern

ERIC Educational Resources Information Center

Siniatchkin, Michael; Groppa, Sergey; Jerosch, Bettina; Muhle, Hiltrud; Kurth, Christoph; Shepherd, Alex J.; Siebner, Hartwig; Stephani, Ulrich

2007-01-01

Photosensitivity or photoparoxysmal response (PPR) is a highly heritable electroencephalographic trait characterized by an abnormal cortical response to intermittent photic stimulation (IPS). In PPR-positive individuals, IPS induces spikes, spike-waves or intermittent slow waves. The PPR may be restricted to posterior visual areas (i.e. local PPR…

Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment.

PubMed

Capecci, Elisa; Kasabov, Nikola; Wang, Grace Y

2015-08-01

The paper presents a methodology for the analysis of functional changes in brain activity across different conditions and different groups of subjects. This analysis is based on the recently proposed NeuCube spiking neural network (SNN) framework and more specifically on the analysis of the connectivity of a NeuCube model trained with electroencephalography (EEG) data. The case study data used to illustrate this method is EEG data collected from three groups-subjects with opiate addiction, patients undertaking methadone maintenance treatment, and non-drug users/healthy control group. The proposed method classifies more accurately the EEG data than traditional statistical and artificial intelligence (AI) methods and can be used to predict response to treatment and dose-related drug effect. But more importantly, the method can be used to compare functional brain activities of different subjects and the changes of these activities as a result of treatment, which is a step towards a better understanding of both the EEG data and the brain processes that generated it. The method can also be used for a wide range of applications, such as a better understanding of disease progression or aging. Copyright © 2015 Elsevier Ltd. All rights reserved.

Chess-playing epilepsy: a case report with video-EEG and back averaging.

PubMed

Mann, M W; Gueguen, B; Guillou, S; Debrand, E; Soufflet, C

2004-12-01

A patient suffering from juvenile myoclonic epilepsy experienced myoclonic jerks, fairly regularly, while playing chess. The myoclonus appeared particularly when he had to plan his strategy, to choose between two solutions or while raising the arm to move a chess figure. Video-EEG-polygraphy was performed, with back averaging of the myoclonus registered during a chess match and during neuropsychological testing with Kohs cubes. The EEG spike wave complexes were localised in the fronto-central region. [Published with video sequences].

Zoomed MRI Guided by Combined EEG/MEG Source Analysis: A Multimodal Approach for Optimizing Presurgical Epilepsy Work-up and its Application in a Multi-focal Epilepsy Patient Case Study.

PubMed

Aydin, Ü; Rampp, S; Wollbrink, A; Kugel, H; Cho, J -H; Knösche, T R; Grova, C; Wellmer, J; Wolters, C H

2017-07-01

In recent years, the use of source analysis based on electroencephalography (EEG) and magnetoencephalography (MEG) has gained considerable attention in presurgical epilepsy diagnosis. However, in many cases the source analysis alone is not used to tailor surgery unless the findings are confirmed by lesions, such as, e.g., cortical malformations in MRI. For many patients, the histology of tissue resected from MRI negative epilepsy shows small lesions, which indicates the need for more sensitive MR sequences. In this paper, we describe a technique to maximize the synergy between combined EEG/MEG (EMEG) source analysis and high resolution MRI. The procedure has three main steps: (1) construction of a detailed and calibrated finite element head model that considers the variation of individual skull conductivities and white matter anisotropy, (2) EMEG source analysis performed on averaged interictal epileptic discharges (IED), (3) high resolution (0.5 mm) zoomed MR imaging, limited to small areas centered at the EMEG source locations. The proposed new diagnosis procedure was then applied in a particularly challenging case of an epilepsy patient: EMEG analysis at the peak of the IED coincided with a right frontal focal cortical dysplasia (FCD), which had been detected at standard 1 mm resolution MRI. Of higher interest, zoomed MR imaging (applying parallel transmission, 'ZOOMit') guided by EMEG at the spike onset revealed a second, fairly subtle, FCD in the left fronto-central region. The evaluation revealed that this second FCD, which had not been detectable with standard 1 mm resolution, was the trigger of the seizures.

High-frequency oscillations mirror disease activity in patients with epilepsy.

PubMed

Zijlmans, M; Jacobs, J; Zelmann, R; Dubeau, F; Gotman, J

2009-03-17

High-frequency oscillations (HFOs) can be recorded in epileptic patients with clinical intracranial EEG. HFOs have been associated with seizure genesis because they occur in the seizure focus and during seizure onset. HFOs are also found interictally, partly co-occurring with epileptic spikes. We studied how HFOs are influenced by antiepileptic medication and seizure occurrence, to improve understanding of the pathophysiology and clinical meaning of HFOs. Intracerebral depth EEG was partly sampled at 2,000 Hz in 42 patients with intractable focal epilepsy. Patients with five or more usable nights of recording were selected. A sample of slow-wave sleep from each night was analyzed, and HFOs (ripples: 80-250 Hz, fast ripples: 250-500 Hz) and spikes were identified on all artifact-free channels. The HFOs and spikes were compared before and after seizures with stable medication dose and during medication reduction with no intervening seizures. Twelve patients with five to eight nights were included. After seizures, there was an increase in spikes, whereas HFO rates remained the same. Medication reduction was followed by an increase in HFO rates and mean duration. Contrary to spikes, high-frequency oscillations (HFOs) do not increase after seizures, but do so after medication reduction, similarly to seizures. This implies that spikes and HFOs have different pathophysiologic mechanisms and that HFOs are more tightly linked to seizures than spikes. HFOs seem to play an important role in seizure genesis and can be a useful clinical marker for disease activity.

Spatiotemporal Mapping of Interictal Spike Propagation: A Novel Methodology Applied to Pediatric Intracranial EEG Recordings

PubMed Central

Tomlinson, Samuel B.; Bermudez, Camilo; Conley, Chiara; Brown, Merritt W.; Porter, Brenda E.; Marsh, Eric D.

2016-01-01

Synchronized cortical activity is implicated in both normative cognitive functioning and many neurologic disorders. For epilepsy patients with intractable seizures, irregular synchronization within the epileptogenic zone (EZ) is believed to provide the network substrate through which seizures initiate and propagate. Mapping the EZ prior to epilepsy surgery is critical for detecting seizure networks in order to achieve postsurgical seizure control. However, automated techniques for characterizing epileptic networks have yet to gain traction in the clinical setting. Recent advances in signal processing and spike detection have made it possible to examine the spatiotemporal propagation of interictal spike discharges across the epileptic cortex. In this study, we present a novel methodology for detecting, extracting, and visualizing spike propagation and demonstrate its potential utility as a biomarker for the EZ. Eighteen presurgical intracranial EEG recordings were obtained from pediatric patients ultimately experiencing favorable (i.e., seizure-free, n = 9) or unfavorable (i.e., seizure-persistent, n = 9) surgical outcomes. Novel algorithms were applied to extract multichannel spike discharges and visualize their spatiotemporal propagation. Quantitative analysis of spike propagation was performed using trajectory clustering and spatial autocorrelation techniques. Comparison of interictal propagation patterns revealed an increase in trajectory organization (i.e., spatial autocorrelation) among Sz-Free patients compared with Sz-Persist patients. The pathophysiological basis and clinical implications of these findings are considered. PMID:28066315

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.

The additional lateralizing and localizing value of the postictal EEG in frontal lobe epilepsy.

PubMed

Whitehead, Kimberley; Gollwitzer, Stephanie; Millward, Helen; Wehner, Tim; Scott, Catherine; Diehl, Beate

2016-03-01

The aim of this study was to describe the additional lateralizing and localizing value of the postictal EEG in frontal lobe epilepsy (FLE). The ictal EEG in FLE is frequently challenging to localize. We identified patients investigated for epilepsy surgery with unilateral FLE based on consistent semiology, a clear lesion and/or with frontal onset on intracranial EEG. A one hour section of postictal EEG was analyzed by two raters for new or activated EEG features and it was assessed whether these features offered additional information when compared to the ictal EEG. Postictal features assessed included asymmetrical return of the posterior dominant rhythm and potentiated lateralized or regional frontal slowing, spikes or sharp waves. Thirty-eight patients were included who had a combined total of ninety-six seizures. 47/96 (49%) postictal periods contained correctly lateralizing or localizing information. The sensitivity for asymmetrical return of the posterior dominant rhythm was 24%. The sensitivity for regional frontal slow and frontal spikes was 23% and 20% respectively. Further analysis showed that in 14/38 (39%) patients, at least one seizure with an unhelpful ictal EEG was followed by postictal EEG features that added new localizing or lateralizing information. A subgroup of 11 patients who were ⩾1 year seizure-free (ILAE class 1) and thus classified as having a 'gold-standard' FLE diagnosis were analyzed separately and it was found that 14/30 of their seizures (47%) had extra postictal information. The new postictal information was always concordant with the ultimate diagnosis, except for asymmetric postictal return of background activity ipsilateral to the epileptogenic zone in three patients. This study shows that a close examination of the postictal EEG can offer additional information which can contribute to the identification of a potentially resectable epileptogenic zone. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

The development of analgesic, pro- and anti-convulsant opiate effects in the rat.

PubMed

Van Praag, H; Falcon, M; Guendelman, D; Frenk, H

1993-01-01

Evidence indicates that the neonate is capable, if not perceiving nociception, then at least reacting to nociceptive stimuli. These responses can be inhibited by opiates such as morphine. The analgesic potency of morphine in rat pups increases with maturation, due to (a) the proliferation of opiate receptors and (b), the maturation of supraspinal descending inhibition which becomes functional at 3 weeks post-natally. Tolerance to repeated injections of morphine in pups is less pronounced than in adults since it is masked by several processes, it has been demonstrated to occur within the first two weeks of life. Toxic effects of morphine in the neonate, as can be demonstrated both in behavior and EEG, differ from those in adults. Thus, convulsions induced by morphine which have been reported to occur in adults, were absent in pups. Excitatory effects of morphine in behavior develop in 3 different stages. During the first week morphine caused behavioral activation which is not mediated by specific opiate receptors. In the second week morphine produces EEG spikes in a dose-dependent fashion, but at this age these spikes were not reversible by opiate antagonists. Opiate specific EEG spikes and other opiate specific excitatory effects start to predominate during the third week of life.

Maternal care affects EEG properties of spike-wave seizures (including pre- and post ictal periods) in adult WAG/Rij rats with genetic predisposition to absence epilepsy.

PubMed

Sitnikova, Evgenia; Rutskova, Elizaveta M; Raevsky, Vladimir V

2016-10-01

WAG/Rij rats have a genetic predisposition to absence epilepsy and develop spontaneous spike-wave discharges in EEG during late ontogenesis (SWD, EEG manifestation of absence epilepsy). Changes in an environment during early postnatal ontogenesis can influence the genetically predetermined absence epilepsy. Here we examined the effect of maternal environment during weaning period on the EEG manifestation of absence epilepsy in adulthood. Experiments were performed in the offspring of WAG/Rij and Wistar rats. The newborn pups were fostered to dams of the same (in-fostering) or another strain (cross-fostering). Age-matched control WAG/Rij and Wistar rats were reared by their biological mothers. Absence seizures were uncommon in Wistar and were not aggravated in both in- and cross-fostered groups. In WAG/Rij rats, fewer SWD were found in the cross-fostered as compared to the in-fostered group. The cross-fostered WAG/Rij rats showed higher percentage of short-lasting SWD with duration <2s. The mean frequency of EEG at the beginning of SWD in the cross-fostered WAG/Rij rats was lower than in control (8.82 vs 9.25Hz), but it was higher in a period of 1.5s before and after SWD. It was concluded that a healthier maternal environment is able to alleviate genetically predetermined absence seizures in adulthood through changes in EEG rhythmic activity. Copyright © 2016 Elsevier Inc. All rights reserved.

CLUSTERING OF INTERICTAL SPIKES BY DYNAMIC TIME WARPING AND AFFINITY PROPAGATION

PubMed Central

Thomas, John; Jin, Jing; Dauwels, Justin; Cash, Sydney S.; Westover, M. Brandon

2018-01-01

Epilepsy is often associated with the presence of spikes in electroencephalograms (EEGs). The spike waveforms vary vastly among epilepsy patients, and also for the same patient across time. In order to develop semi-automated and automated methods for detecting spikes, it is crucial to obtain a better understanding of the various spike shapes. In this paper, we develop several approaches to extract exemplars of spikes. We generate spike exemplars by applying clustering algorithms to a database of spikes from 12 patients. As similarity measures for clustering, we consider the Euclidean distance and Dynamic Time Warping (DTW). We assess two clustering algorithms, namely, K-means clustering and affinity propagation. The clustering methods are compared based on the mean squared error, and the similarity measures are assessed based on the number of generated spike clusters. Affinity propagation with DTW is shown to be the best combination for clustering epileptic spikes, since it generates fewer spike templates and does not require to pre-specify the number of spike templates. PMID:29527130

Bradycardia from flash stimulation.

PubMed

Einspenner, Michael; Brunet, Donald G; Boissé Lomax, Lysa; Spiller, Allison E

2015-12-01

This case study documents a patient who experienced bradycardia brought on by flash stimulation during a routine outpatient EEG recording. The patient had known photosensitive seizures in the past. During this routine EEG, the patient's heart rate dropped to about 12 beats per minute with the EEG displaying slow-delta-frequency waves with no epileptiform spikes or sharp waves. During immediate follow-up, in our emergency department, the patient had a brief asystolic event, followed by bradycardia. Cardiology examinations were normal. We propose that this response was a photic-triggered reflex vasovagal reaction.

Electrical source localization by LORETA in patients with epilepsy: Confirmation by postoperative MRI

PubMed Central

Akdeniz, Gülsüm

2016-01-01

Background: Few studies have been conducted that have compared electrical source localization (ESL) results obtained by analyzing ictal patterns in scalp electroencephalogram (EEG) with the brain areas that are found to be responsible for seizures using other brain imaging techniques. Additionally, adequate studies have not been performed to confirm the accuracy of ESL methods. Materials and Methods: In this study, ESL was conducted using LORETA (Low Resolution Brain Electromagnetic Tomography) in 9 patients with lesions apparent on magnetic resonance imaging (MRI) and in 6 patients who did not exhibit lesions on their MRIs. EEGs of patients who underwent surgery for epilepsy and had follow-ups for at least 1 year after operations were analyzed for ictal spike, rhythmic, paroxysmal fast, and obscured EEG activities. Epileptogenic zones identified in postoperative MRIs were then compared with localizations obtained by LORETA model we employed. Results: We found that brain areas determined via ESL were in concordance with resected brain areas for 13 of the 15 patients evaluated, and those 13 patients were post-operatively determined as being seizure-free. Conclusion: ESL, which is a noninvasive technique, may contribute to the correct delineation of epileptogenic zones in patients who will eventually undergo surgery to treat epilepsy, (regardless of neuroimaging status). Moreover, ESL may aid in deciding on the number and localization of intracranial electrodes to be used in patients who are candidates for invasive recording. PMID:27011626

Electrical source localization by LORETA in patients with epilepsy: Confirmation by postoperative MRI.

PubMed

Akdeniz, Gülsüm

2016-01-01

Few studies have been conducted that have compared electrical source localization (ESL) results obtained by analyzing ictal patterns in scalp electroencephalogram (EEG) with the brain areas that are found to be responsible for seizures using other brain imaging techniques. Additionally, adequate studies have not been performed to confirm the accuracy of ESL methods. In this study, ESL was conducted using LORETA (Low Resolution Brain Electromagnetic Tomography) in 9 patients with lesions apparent on magnetic resonance imaging (MRI) and in 6 patients who did not exhibit lesions on their MRIs. EEGs of patients who underwent surgery for epilepsy and had follow-ups for at least 1 year after operations were analyzed for ictal spike, rhythmic, paroxysmal fast, and obscured EEG activities. Epileptogenic zones identified in postoperative MRIs were then compared with localizations obtained by LORETA model we employed. We found that brain areas determined via ESL were in concordance with resected brain areas for 13 of the 15 patients evaluated, and those 13 patients were post-operatively determined as being seizure-free. ESL, which is a noninvasive technique, may contribute to the correct delineation of epileptogenic zones in patients who will eventually undergo surgery to treat epilepsy, (regardless of neuroimaging status). Moreover, ESL may aid in deciding on the number and localization of intracranial electrodes to be used in patients who are candidates for invasive recording.

The use of Matlab for colour fuzzy representation of multichannel EEG short time spectra.

PubMed

Bigan, C; Strungaru, R

1998-01-01

During the last years, a lot of EEG research efforts was directed to intelligent methods for automatic analysis of data from multichannel EEG recordings. However, all the applications reported were focused on specific single tasks like detection of one specific "event" in the EEG signal: spikes, sleep spindles, epileptic seizures, K complexes, alpha or other rhythms or even artefacts. The aim of this paper is to present a complex system being able to perform a representation of the dynamic changes in frequency components of each EEG channel. This representation uses colours as a powerful means to show the only one frequency range chosen from the shortest epoch of signal able to be processed with the conventional "Short Time Fast Fourier Transform" (S.T.F.F.T.) method.

Electroencephalography in the Diagnosis of Genetic Generalized Epilepsy Syndromes

PubMed Central

Seneviratne, Udaya; Cook, Mark J.; D’Souza, Wendyl Jude

2017-01-01

Genetic generalized epilepsy (GGE) consists of several syndromes diagnosed and classified on the basis of clinical features and electroencephalographic (EEG) abnormalities. The main EEG feature of GGE is bilateral, synchronous, symmetric, and generalized spike-wave complex. Other classic EEG abnormalities are polyspikes, epileptiform K-complexes and sleep spindles, polyspike-wave discharges, occipital intermittent rhythmic delta activity, eye-closure sensitivity, fixation-off sensitivity, and photoparoxysmal response. However, admixed with typical changes, atypical epileptiform discharges are also commonly seen in GGE. There are circadian variations of generalized epileptiform discharges. Sleep, sleep deprivation, hyperventilation, intermittent photic stimulation, eye closure, and fixation-off are often used as activation techniques to increase the diagnostic yield of EEG recordings. Reflex seizure-related EEG abnormalities can be elicited by the use of triggers such as cognitive tasks and pattern stimulation during the EEG recording in selected patients. Distinct electrographic abnormalities to help classification can be identified among different electroclinical syndromes. PMID:28993753

Central nervous system hyperexcitability associated with glutamate dehydrogenase gain of function mutations.

PubMed

Raizen, David M; Brooks-Kayal, Amy; Steinkrauss, Linda; Tennekoon, Gihan I; Stanley, Charles A; Kelly, Andrea

2005-03-01

To describe seizure phenotypes associated with the hyperinsulinism/hyperammonemia syndrome (HI/HA), which is caused by gain of function mutations in the enzyme glutamate dehydrogenase (GDH). A retrospective review of records of 14 patients with HI/HA. Nine patients had seizures as the first symptom of HI/HA, and six had seizures in the absence of hypoglycemia. No electroencephalogram (EEG) background abnormalities were identified. In four patients, EEG recordings during seizures in the setting of normal blood glucose contained generalized epileptiform discharges. EEGs of three of these patients showed 0.5- to 2-second generalized irregular spike-and-wave discharge at 3 to 6 Hz corresponding to eye blinks, eye rolling, or staring. The EEG of the fourth patient consisted of 20 seconds of generalized regular spike-and-wave discharge at 3 Hz in the clinical context of staring and unresponsiveness. In two patients, seizure control worsened with carbamezapine or oxcarbezapine treatment. In patients with HI/HA, generalized seizures are common and can occur in the absence of hypoglycemia. The drugs carbamazepine and oxcarbazepine should be used with caution for treatment. Pathogenesis of epilepsy in these patients may be related to effects of GDH mutations in the brain, perhaps in combination with effects of recurrent hypoglycemia and chronic hyperammonemia.

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.

Proepileptic patterns in EEG of WAG/Rij rats

NASA Astrophysics Data System (ADS)

Grubov, Vadim V.; Sitnikova, Evgenia Yu.; Nedaivozov, Vladimir O.; Koronovskii, Alexey A.

2018-04-01

In this paper we study specific oscillatory patterns on EEG signals of WAG/Rij rats. These patterns are known as proepileptic because they occur in time period during the development of absence-epilepsy before fully-formed epileptic seizures. In the paper we analyze EEG signals of WAG/Rij rats with continuous wavelet transform and empirical mode decomposition in order to find particular features of epileptic spike-wave discharges and nonepileptic sleep spindles. Then we introduce proepileptic activity as patterns that combine features of epileptic and non-epileptic activity. We analyze proepileptic activity in order to specify its features and time-frequency structure.

Mapping (and modeling) physiological movements during EEG-fMRI recordings: the added value of the video acquired simultaneously.

PubMed

Ruggieri, Andrea; Vaudano, Anna Elisabetta; Benuzzi, Francesca; Serafini, Marco; Gessaroli, Giuliana; Farinelli, Valentina; Nichelli, Paolo Frigio; Meletti, Stefano

2015-01-15

During resting-state EEG-fMRI studies in epilepsy, patients' spontaneous head-face movements occur frequently. We tested the usefulness of synchronous video recording to identify and model the fMRI changes associated with non-epileptic movements to improve sensitivity and specificity of fMRI maps related to interictal epileptiform discharges (IED). Categorization of different facial/cranial movements during EEG-fMRI was obtained for 38 patients [with benign epilepsy with centro-temporal spikes (BECTS, n=16); with idiopathic generalized epilepsy (IGE, n=17); focal symptomatic/cryptogenic epilepsy (n=5)]. We compared at single subject- and at group-level the IED-related fMRI maps obtained with and without additional regressors related to spontaneous movements. As secondary aim, we considered facial movements as events of interest to test the usefulness of video information to obtain fMRI maps of the following face movements: swallowing, mouth-tongue movements, and blinking. Video information substantially improved the identification and classification of the artifacts with respect to the EEG observation alone (mean gain of 28 events per exam). Inclusion of physiological activities as additional regressors in the GLM model demonstrated an increased Z-score and number of voxels of the global maxima and/or new BOLD clusters in around three quarters of the patients. Video-related fMRI maps for swallowing, mouth-tongue movements, and blinking were comparable to the ones obtained in previous task-based fMRI studies. Video acquisition during EEG-fMRI is a useful source of information. Modeling physiological movements in EEG-fMRI studies for epilepsy will lead to more informative IED-related fMRI maps in different epileptic conditions. Copyright © 2014 Elsevier B.V. All rights reserved.

Dynamic timecourse of typical childhood absence seizures: EEG, behavior and fMRI

PubMed Central

Bai, X; Vestal, M; Berman, R; Negishi, M; Spann, M; Vega, C; Desalvo, M; Novotny, EJ; Constable, RT; Blumenfeld, H

2010-01-01

Absence seizures are 5–10 second episodes of impaired consciousness accompanied by 3–4Hz generalized spike-and-wave discharge on electroencephalography (EEG). The timecourse of functional magnetic resonance imaging (fMRI) changes in absence seizures in relation to EEG and behavior is not known. We acquired simultaneous EEG-fMRI in 88 typical childhood absence seizures from 9 pediatric patients. We investigated behavior concurrently using a continuous performance task (CPT) or simpler repetitive tapping task (RTT). EEG time-frequency analysis revealed abrupt onset and end of 3–4 Hz spike-wave discharges with a mean duration of 6.6 s. Behavioral analysis also showed rapid onset and end of deficits associated with electrographic seizure start and end. In contrast, we observed small early fMRI increases in the orbital/medial frontal and medial/lateral parietal cortex >5s before seizure onset, followed by profound fMRI decreases continuing >20s after seizure end. This timecourse differed markedly from the hemodynamic response function (HRF) model used in conventional fMRI analysis, consisting of large increases beginning after electrical event onset, followed by small fMRI decreases. Other regions, such as the lateral frontal cortex, showed more balanced fMRI increases followed by approximately equal decreases. The thalamus showed delayed increases after seizure onset followed by small decreases, most closely resembling the HRF model. These findings reveal a complex and long lasting sequence of fMRI changes in absence seizures, which are not detectible by conventional HRF modeling in many regions. These results may be important mechanistically for seizure initiation and termination and may also contribute to changes in EEG and behavior. PMID:20427649

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.

Functional connectivity analysis in EEG source space: The choice of method

PubMed Central

Knyazeva, Maria G.

2017-01-01

Functional connectivity (FC) is among the most informative features derived from EEG. However, the most straightforward sensor-space analysis of FC is unreliable owing to volume conductance effects. An alternative—source-space analysis of FC—is optimal for high- and mid-density EEG (hdEEG, mdEEG); however, it is questionable for widely used low-density EEG (ldEEG) because of inadequate surface sampling. Here, using simulations, we investigate the performance of the two source FC methods, the inverse-based source FC (ISFC) and the cortical partial coherence (CPC). To examine the effects of localization errors of the inverse method on the FC estimation, we simulated an oscillatory source with varying locations and SNRs. To compare the FC estimations by the two methods, we simulated two synchronized sources with varying between-source distance and SNR. The simulations were implemented for hdEEG, mdEEG, and ldEEG. We showed that the performance of both methods deteriorates for deep sources owing to their inaccurate localization and smoothing. The accuracy of both methods improves with the increasing between-source distance. The best ISFC performance was achieved using hd/mdEEG, while the best CPC performance was observed with ldEEG. In conclusion, with hdEEG, ISFC outperforms CPC and therefore should be the preferred method. In the studies based on ldEEG, the CPC is a method of choice. PMID:28727750

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.

Silent hippocampal seizures and spikes identified by foramen ovale electrodes in Alzheimer's disease.

PubMed

Lam, Alice D; Deck, Gina; Goldman, Alica; Eskandar, Emad N; Noebels, Jeffrey; Cole, Andrew J

2017-06-01

We directly assessed mesial temporal activity using intracranial foramen ovale electrodes in two patients with Alzheimer's disease (AD) without a history or EEG evidence of seizures. We detected clinically silent hippocampal seizures and epileptiform spikes during sleep, a period when these abnormalities were most likely to interfere with memory consolidation. The findings in these index cases support a model in which early development of occult hippocampal hyperexcitability may contribute to the pathogenesis of AD.

A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram.

PubMed

Chu, Catherine J; Chan, Arthur; Song, Dan; Staley, Kevin J; Stufflebeam, Steven M; Kramer, Mark A

2017-02-01

High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. Copyright © 2016 Elsevier B.V. All rights reserved.

A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram

PubMed Central

Chu, Catherine. J.; Chan, Arthur; Song, Dan; Staley, Kevin J.; Stufflebeam, Steven M.; Kramer, Mark A.

2017-01-01

Summary Background High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. New Method The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. Results We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. Comparison with Existing Method The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Conclusions Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. PMID:27988323

Forced normalisation precipitated by lamotrigine.

PubMed

Clemens, Béla

2005-10-01

To report two patients with lamotrigine-induced forced normalization (FN). Evaluation of the patient files, EEG, and video-EEG records, with special reference to the parallel clinical and EEG changes before, during, and after FN. This is the first documented report of lamotrigine-induced FN. The two epileptic patients (one of them was a 10-year-old girl) were successfully treated with lamotrigine. Their seizures ceased and interictal epileptiform events disappeared from the EEG record. Simultaneously, the patients displayed de novo occurrence of psychopathologic manifestations and disturbed behaviour. Reduction of the daily dose of LTG led to disappearance of the psychopathological symptoms and reappearance of the spikes but not the seizures. Lamotrigine may precipitate FN in adults and children. Analysis of the cases showed that lamotrigine-induced FN is a dose-dependent phenomenon and can be treated by reduction of the daily dose of the drug.

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.

Spatiotemporal mapping of interictal epileptiform discharges in human absence epilepsy: A MEG study.

PubMed

Rozendaal, Yvonne J W; van Luijtelaar, Gilles; Ossenblok, Pauly P W

2016-01-01

Although absence epilepsy is considered to be a prototypic type of generalized epilepsy, it is still under debate whether generalized 3 Hz spike-and-wave discharges (SWDs) might have a cortical focal origin. Here it is investigated whether focal interictal epileptiform discharges (IEDs), which typically occur in the electro- (EEG) and magnetoencephalogram (MEG) in case of focal epilepsy, are present in the MEG of children with absence epilepsy. Next, the location of the sources of the IEDs is established, and it is investigated whether the location is concordant to the earlier established focal cortical regions involved in the generalized SWDs of these children. Whole head MEG recordings of seven children with absence epilepsy were reviewed with respect to the presence of IEDs (spikes and sharp waves). These IEDs were grouped into distinct clusters, in which each contribution to a cluster yields a comparable magnetic field distribution. Source localization was then performed onto the average signal of each cluster using an equivalent current dipole model and a realistic head model of the cortical surface. IEDs were detected in 6 out of 7 patients. Source reconstruction indicated most often frontal, central or parietal origins of the IED in either the left and or right hemisphere. Spatiotemporal assessment of the IEDs indicated a stable location of the averages of these discharges, indicating a single underlying cortical source. The outcome of this pilot study shows that MEG is well suited for the detection of IEDs and suggests that their estimated sources coincide rather well with the cortical regions involved during the spikes of the SWDs. It is discussed whether the presence of IEDs, classically seen as a marker of focal epilepsies, indicate that absence epilepsy should be considered as a focal type of epilepsy, in which changes in the network are evolving rapidly. Copyright © 2015 Elsevier B.V. All rights reserved.

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

Using patient-specific hemodynamic response function in epileptic spike analysis of human epilepsy: a study based on EEG-fNIRS.

PubMed

Peng, Ke; Nguyen, Dang Khoa; Vannasing, Phetsamone; Tremblay, Julie; Lesage, Frédéric; Pouliot, Philippe

2016-02-01

Functional near-infrared spectroscopy (fNIRS) can be combined with electroencephalography (EEG) to continuously monitor the hemodynamic signal evoked by epileptic events such as seizures or interictal epileptiform discharges (IEDs, aka spikes). As estimation methods assuming a canonical shape of the hemodynamic response function (HRF) might not be optimal, we sought to model patient-specific HRF (sHRF) with a simple deconvolution approach for IED-related analysis with EEG-fNIRS data. Furthermore, a quadratic term was added to the model to account for the nonlinearity in the response when IEDs are frequent. Prior to analyzing clinical data, simulations were carried out to show that the HRF was estimable by the proposed deconvolution methods under proper conditions. EEG-fNIRS data of five patients with refractory focal epilepsy were selected due to the presence of frequent clear IEDs and their unambiguous focus localization. For each patient, both the linear sHRF and the nonlinear sHRF were estimated at each channel. Variability of the estimated sHRFs was seen across brain regions and different patients. Compared with the SPM8 canonical HRF (cHRF), including these sHRFs in the general linear model (GLM) analysis led to hemoglobin activations with higher statistical scores as well as larger spatial extents on all five patients. In particular, for patients with frequent IEDs, nonlinear sHRFs were seen to provide higher sensitivity in activation detection than linear sHRFs. These observations support using sHRFs in the analysis of IEDs with EEG-fNIRS data. Copyright © 2015 Elsevier Inc. All rights reserved.

Classification of EEG abnormalities in partial epilepsy with simultaneous EEG-fMRI recordings.

PubMed

Pedreira, C; Vaudano, A E; Thornton, R C; Chaudhary, U J; Vulliemoz, S; Laufs, H; Rodionov, R; Carmichael, D W; Lhatoo, S D; Guye, M; Quian Quiroga, R; Lemieux, L

2014-10-01

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the "irritative zone" (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram-functional Magnetic Resonance Imagining (EEG-fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG-fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG-fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy. Copyright © 2014 Elsevier Inc. All rights reserved.

Automatic EEG spike detection.

PubMed

Harner, Richard

2009-10-01

Since the 1970s advances in science and technology during each succeeding decade have renewed the expectation of efficient, reliable automatic epileptiform spike detection (AESD). But even when reinforced with better, faster tools, clinically reliable unsupervised spike detection remains beyond our reach. Expert-selected spike parameters were the first and still most widely used for AESD. Thresholds for amplitude, duration, sharpness, rise-time, fall-time, after-coming slow waves, background frequency, and more have been used. It is still unclear which of these wave parameters are essential, beyond peak-peak amplitude and duration. Wavelet parameters are very appropriate to AESD but need to be combined with other parameters to achieve desired levels of spike detection efficiency. Artificial Neural Network (ANN) and expert-system methods may have reached peak efficiency. Support Vector Machine (SVM) technology focuses on outliers rather than centroids of spike and nonspike data clusters and should improve AESD efficiency. An exemplary spike/nonspike database is suggested as a tool for assessing parameters and methods for AESD and is available in CSV or Matlab formats from the author at brainvue@gmail.com. Exploratory Data Analysis (EDA) is presented as a graphic method for finding better spike parameters and for the step-wise evaluation of the spike detection process.

Recognizing of stereotypic patterns in epileptic EEG using empirical modes and wavelets

NASA Astrophysics Data System (ADS)

Grubov, V. V.; Sitnikova, E.; Pavlov, A. N.; Koronovskii, A. A.; Hramov, A. E.

2017-11-01

Epileptic activity in the form of spike-wave discharges (SWD) appears in the electroencephalogram (EEG) during absence seizures. This paper evaluates two approaches for detecting stereotypic rhythmic activities in EEG, i.e., the continuous wavelet transform (CWT) and the empirical mode decomposition (EMD). The CWT is a well-known method of time-frequency analysis of EEG, whereas EMD is a relatively novel approach for extracting signal's waveforms. A new method for pattern recognition based on combination of CWT and EMD is proposed. It was found that this combined approach resulted to the sensitivity of 86.5% and specificity of 92.9% for sleep spindles and 97.6% and 93.2% for SWD, correspondingly. Considering strong within- and between-subjects variability of sleep spindles, the obtained efficiency in their detection was high in comparison with other methods based on CWT. It is concluded that the combination of a wavelet-based approach and empirical modes increases the quality of automatic detection of stereotypic patterns in rat's EEG.

Presurgical EEG-fMRI in a complex clinical case with seizure recurrence after epilepsy surgery

PubMed Central

Zhang, Jing; Liu, Qingzhu; Mei, Shanshan; Zhang, Xiaoming; Wang, Xiaofei; Liu, Weifang; Chen, Hui; Xia, Hong; Zhou, Zhen; Li, Yunlin

2013-01-01

Epilepsy surgery has improved over the last decade, but non-seizure-free outcome remains at 10%–40% in temporal lobe epilepsy (TLE) and 40%–60% in extratemporal lobe epilepsy (ETLE). This paper reports a complex multifocal case. With a normal magnetic resonance imaging (MRI) result and nonlocalizing electroencephalography (EEG) findings (bilateral TLE and ETLE, with more interictal epileptiform discharges [IEDs] in the right frontal and temporal regions), a presurgical EEG-functional MRI (fMRI) was performed before the intraoperative intracranial EEG (icEEG) monitoring (icEEG with right hemispheric coverage). Our previous EEG-fMRI analysis results (IEDs in the left hemisphere alone) were contradictory to the EEG and icEEG findings (IEDs in the right frontal and temporal regions). Thus, the EEG-fMRI data were reanalyzed with newly identified IED onsets and different fMRI model options. The reanalyzed EEG-fMRI findings were largely concordant with those of EEG and icEEG, and the failure of our previous EEG-fMRI analysis may lie in the inaccurate identification of IEDs and wrong usage of model options. The right frontal and temporal regions were resected in surgery, and dual pathology (hippocampus sclerosis and focal cortical dysplasia in the extrahippocampal region) was found. The patient became seizure-free for 3 months, but his seizures restarted after antiepileptic drugs (AEDs) were stopped. The seizures were not well controlled after resuming AEDs. Postsurgical EEGs indicated that ictal spikes in the right frontal and temporal regions reduced, while those in the left hemisphere became prominent. This case suggested that (1) EEG-fMRI is valuable in presurgical evaluation, but requires caution; and (2) the intact seizure focus in the remaining brain may cause the non-seizure-free outcome. PMID:23926432

Myoclonic Jerks and Schizophreniform Syndrome: Case Report and Literature Review.

PubMed

Endres, Dominique; Altenmüller, Dirk-M; Feige, Bernd; Maier, Simon J; Nickel, Kathrin; Hellwig, Sabine; Rausch, Jördis; Ziegler, Christiane; Domschke, Katharina; Doerr, John P; Egger, Karl; Tebartz van Elst, Ludger

2018-01-01

Background: Schizophreniform syndromes can be divided into primary idiopathic forms as well as different secondary organic subgroups (e.g., paraepileptic, epileptic, immunological, or degenerative). Secondary epileptic explanatory approaches have often been discussed in the past, due to the high rates of electroencephalography (EEG) alterations in patients with schizophrenia. In particular, temporal lobe epilepsy is known to be associated with schizophreniform symptoms in well-described constellations. In the literature, juvenile myoclonic epilepsy has been linked to emotionally unstable personality traits, depression, anxiety, and executive dysfunction; however, the association with schizophrenia is largely unclear. Case presentation: We present the case of a 28-year-old male student suffering from mild myoclonic jerks, mainly of the upper limbs, as well as a predominant paranoid-hallucinatory syndrome with attention deficits, problems with working memory, depressive-flat mood, reduced energy, fast stimulus satiation, delusional and audible thoughts, tactile hallucinations, thought inspirations, and severe sleep disturbances. Cerebral magnetic resonance imaging and cerebrospinal fluid analyses revealed no relevant abnormalities. The routine EEG and the first EEG after sleep deprivation (under treatment with oxazepam) also returned normal findings. Video telemetry over one night, which included a partial sleep-deprivation EEG, displayed short generalized spike-wave complexes and polyspikes, associated with myoclonic jerks, after waking in the morning. Video-EEG monitoring over 5 days showed over 100 myoclonic jerks of the upper limbs, frequently with generalized spike-wave complexes with left or right accentuation. Therefore, we diagnosed juvenile myoclonic epilepsy. Discussion: This case report illustrates the importance of extended EEG diagnostics in patients with schizophreniform syndromes and myoclonic jerks. The schizophreniform symptoms in the framework of epileptiform EEG activity can be interpreted as a (para)epileptic mechanism due to local area network inhibition (LANI). Following the LANI hypothesis, paranoid hallucinatory symptoms are not due to primary excitatory activity (as myoclonic jerks are) but rather to the secondary process of hyperinhibition triggered by epileptic activity. Identifying subgroups of schizophreniform patients with comorbid epilepsy is important because of the potential benefits of optimized pharmacological treatment.

An 8-year old boy with continuous spikes and waves during slow sleep presenting with positive onconeuronal antibodies.

PubMed

Hu, Lin-Yan; Shi, Xiu-Yu; Feng, Chen; Wang, Jian-Wen; Yang, Guan; Lammers, Stephen H T; Yang, Xiao Fan; Ebrahimi-Fakhari, Darius; Zou, Li-Ping

2015-03-01

To determine the etiology of epilepsy with continuous spikes and waves during slow sleep (CSWS)/electrical status epilepticus during sleep (ESES) in an 8-year old boy with a history of neuroblastoma and opsoclonus-myoclonus. A combination of clinical characterization and follow-up, video EEG and laboratory investigations. We report the case of an 8-year old boy with a history of neuroblastoma and opsoclonus-myoclonus, who presented with intellectual disability, pharmacotherapy-resistant epilepsy and CSWS/ESES. Although the patient's neuroblastoma had been successfully treated 8 years prior to presentation and an extensive workup did not show a tumor reoccurrence, testing for onconeuronal antibodies was positive for anti-Ma2 and anti-CV2/CRMP5 antibodies. High-dose intravenous methylprednisolone and a taper of oral methylprednisolone were given, leading to a significant clinical improvement. During the taper the patient's condition and EEG manifestations deteriorated again necessitating another cycle of steroid therapy, which lead to a stable improvement. During a 6-month follow-up no CSWS/ESES was seen on EEG and anti-Ma2 and anti-CV2/CRMP5 antibodies remained undetectable. This case suggests that onconeuronal antibodies may be involved in the pathogenesis of CSWS/ESES. Copyright © 2015 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.

Transient reduction in theta power caused by interictal spikes in human temporal lobe epilepsy.

PubMed

Manling Ge; Jundan Guo; Yangyang Xing; Zhiguo Feng; Weide Lu; Xinxin Ma; Yuehua Geng; Xin Zhang

2017-07-01

The inhibitory impacts of spikes on LFP theta rhythms(4-8Hz) are investigated around sporadic spikes(SSs) based on intracerebral EEG of 4 REM sleep patients with temporal lobe epilepsy(TLE) under the pre-surgical monitoring. Sequential interictal spikes in both genesis area and extended propagation pathway are collected, that, SSs genesis only in anterior hippocampus(aH)(possible propagation pathway in Entorhinal cortex(EC)), only in EC(possible propagation pathway in aH), and in both aH and EC synchronously. Instantaneous theta power was estimated by using Gabor wavelet transform, and theta power level was estimated by averaged over time and frequency before SSs(350ms pre-spike) and after SSs(350ms post-spike). The inhibitory effect around spikes was evaluated by the ratio of theta power level difference between pre-spike and post-spike to pre-spike theta power level. The findings were that theta power level was reduced across SSs, and the effects were more sever in the case of SSs in both aH and EC synchronously than either SSs only in EC or SSs only in aH. It is concluded that interictal spikes impair LFP theta rhythms transiently and directly. The work suggests that the reduction of theta power after the interictal spike might be an evaluation indicator of damage of epilepsy to human cognitive rhythms.

Paradoxical ictal EEG lateralization in children with unilateral encephaloclastic lesions.

PubMed

Garzon, Eliana; Gupta, Ajay; Bingaman, William; Sakamoto, Americo C; Lüders, Hans

2009-09-01

Describe an ictal EEG pattern of paradoxical lateralization in children with unilateral encephaloclastic hemispheric lesion acquired early in life. Of 68 children who underwent hemispherectomy during 2003-2005, scalp video-EEG and brain MRI of six children with an ictal scalp EEG pattern discordant to the clinical and imaging data were reanalyzed. Medical charts were reviewed for clinical findings and seizure outcome. Age of seizure onset was 1 day-4 years. The destructive MRI lesion was an ischemic stroke in 2, a post-infectious encephalomalacia in 2, and a perinatal trauma and hemiconvulsive-hemiplegic syndrome in one patient each. Ictal EEG pattern was characterized by prominent ictal rhythms with either 3-7 Hz spike and wave complexes or beta frequency sharp waves (paroxysmal fast) over the unaffected (contralesional) hemisphere. Scalp video-EEG was discordant, however, other findings of motor deficits (hemiparesis; five severe, one mild), seizure semiology (4/6), interictal EEG abnormalities (3/6), and unilateral burden of MRI lesion guided the decision for hemispherectomy. After 12-39 months of post-surgery follow up, five of six patients were seizure free and one has brief staring spells. We describe a paradoxical lateralization of the EEG to the "good" hemisphere in children with unihemispheric encephaloclastic lesions. This EEG pattern is compatible with seizure free outcome after surgery, provided other clinical findings and tests are concordant with origin from the abnormal hemisphere.

Long-term follow-up of cognitive functions in patients with continuous spike-waves during sleep (CSWS).

PubMed

Maltoni, Lucia; Posar, Annio; Parmeggiani, Antonia

2016-07-01

Continuous spike-waves during sleep (CSWS) are associated with several cognitive, neurological, and psychiatric disorders, which sometimes persist after CSWS disappearance. The purpose of this retrospective study was to investigate the correlation between general (clinical and instrumental) and neuropsychological findings in CSWS, to identify variables that predispose patients to a poorer long-term neuropsychological outcome. Patients with spikes and waves during sleep with a frequency ≥25/min (spikes and waves frequency index - SWFI) were enrolled. There were patients presenting abnormal EEG activity corresponding to the classic CSWS and patients with paroxysmal abnormalities during sleep <85% with SWFI ≥25/min that was defined as excessive spike-waves during sleep (ESWS). Clinical and instrumental features and neuropsychological findings during and after the spike and wave active phase period were considered. A statistical analysis was performed utilizing the Spearman correlation test and multivariate analysis. The study included 61 patients; the mean follow-up (i.e., the period between SWFI ≥25 first recording and last observation) was 7years and 4months. The SWFI correlated inversely with full and performance IQ during CSWS/ESWS. Longer-lasting SWFI ≥25 was related to worse results in verbal IQ and performance IQ after CSWS/ESWS disappearance. Other variables may influence the neuropsychological outcome, like age at SWFI ≥25 first recording, perinatal distress, pathologic neurologic examination, and antiepileptic drug resistance. This confirms that CSWS/ESWS are a complex pathology and that many variables contribute to its outcome. The SWFI value above all during CSWS/ESWS and long-lasting SWFI ≥25 after CSWS/ESWS disappearance are the most significant indexes that appear mostly to determine cognitive evolution. This finding underscores the importance of EEG recordings during sleep in children with a developmental disorder, even if seizures are not reported, as well as the importance of using therapy with an early efficacy. Copyright © 2016 Elsevier Inc. All rights reserved.

Analysis of intracerebral EEG recordings of epileptic spikes: insights from a neural network model

PubMed Central

Demont-Guignard, Sophie; Benquet, Pascal; Gerber, Urs; Wendling, Fabrice

2009-01-01

The pathophysiological interpretation of EEG signals recorded with depth electrodes (i.e. local field potentials, LFPs) during interictal (between seizures) or ictal (during seizures) periods is fundamental in the pre-surgical evaluation of patients with drug-resistant epilepsy. Our objective was to explain specific shape features of interictal spikes in the hippocampus (observed in LFPs) in terms of cell and network-related parameters of neuronal circuits that generate these events. We developed a neural network model based on “minimal” but biologically-relevant neuron models interconnected through GABAergic and glutamatergic synapses that reproduces the main physiological features of the CA1 subfield. Simulated LFPs were obtained by solving the forward problem (dipole theory) from networks including a large number (~3000) of cells. Insertion of appropriate parameters allowed the model to simulate events that closely resemble actual epileptic spikes. Moreover, the shape of the early fast component (‘spike’) and the late slow component (‘negative wave’) was linked to the relative contribution of glutamatergic and GABAergic synaptic currents in pyramidal cells. In addition, the model provides insights about the sensitivity of electrode localization with respect to recorded tissue volume and about the relationship between the LFP and the intracellular activity of principal cells and interneurons represented in the network. PMID:19651549

Neurocognitive and neurobehavioral disabilities in Epilepsy with Electrical Status Epilepticus in slow sleep (ESES) and related syndromes.

PubMed

Raha, Sarbani; Shah, Urvashi; Udani, Vrajesh

2012-11-01

The aims of this study were to assess the cognitive and behavioral problems of patients with Epilepsy with Electrical Status Epilepticus in slow sleep (ESES) and related syndromes and to review their EEG (electroencephalography) findings and treatment options. Fourteen patients with ESES were evaluated and treated in 2010. Nine children had continuous spike and wave during slow-wave sleep (CSWS)/ESES syndrome, 3 had Atypical BECTS (benign epilepsy with centrotemporal spikes), 1 had Opercular syndrome, and 1 had Landau-Kleffner syndrome. The duration of ESES ranged from 6 to 52 months. Eleven (91%) children had behavioral issues, most prominent being hyperactivity. Seven of the 13 children (53%) showed evidence of borderline to moderate cognitive impairment. A total of 28 EEG findings of ESES were analyzed for SWI (spike-wave index). Antiepileptic drugs received by the patients included valproate, clobazam, levetiracetam, and others. Eleven patients had been treated with oral steroids and it was found to be efficacious in seven (63%). Disabilities caused by ESES affect multiple domains. Patients with an SWI>50% should be followed up frequently with neuropsychological assessments. Steroids appear to be effective, although there is a need to standardize the dose and duration of treatment. Copyright © 2012 Elsevier Inc. All rights reserved.

A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings

PubMed Central

Magri, Cesare; Whittingstall, Kevin; Singh, Vanessa; Logothetis, Nikos K; Panzeri, Stefano

2009-01-01

Background Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Results Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. Conclusion The new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code. PMID:19607698

A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings.

PubMed

Magri, Cesare; Whittingstall, Kevin; Singh, Vanessa; Logothetis, Nikos K; Panzeri, Stefano

2009-07-16

Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. The new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code.

Wireless system for long-term EEG monitoring of absence epilepsy

NASA Astrophysics Data System (ADS)

Whitchurch, Ashwin K.; Ashok, B. H.; Kumaar, R. V.; Saurkesi, K.; Varadan, Vijay K.

2002-11-01

Absence epilepsy is a form of epilepsy common mostly in children. The most common manifestations of Absence epilepsy are staring and transient loss of responsiveness. Also, subtle motor activities may occur. Due to the subtle nature of these symptoms, episodes of absence epilepsy may often go unrecognized for long periods of time or be mistakenly attributed to attention deficit disorder or daydreaming. Spells of absence epilepsy may last about 10 seconds and occur hundreds of times each day. Patients have no recollections of the events that occurred during those seizures and will resume normal activity without any postictal symptoms. The EEG during such episodes of Absence epilepsy shows intermittent activity of 3 Hz generalized spike and wave complexes. As EEG is the only way of detecting such symptoms, it is required to monitor the EEG of the patient for a long time and thus remain only in bed. So, effectively the EEG is being monitored only when the patient is stationary. The wireless monitoring sys tem described in this paper aims at eliminating this constraint and enables the physicial to monitor the EEG when the patient resumes his normal activities. This approach could even help the doctor identify possible triggers of absence epilepsy.

A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

PubMed Central

Wagatsuma, Hiroaki

2017-01-01

EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies. PMID:28194221

BOLD responses related to focal spikes and widespread bilateral synchronous discharges generated in the frontal lobe.

PubMed

An, Dongmei; Dubeau, François; Gotman, Jean

2015-03-01

To investigate whether specific frontal regions have a tendency to generate widespread bilateral synchronous discharges (WBSDs) and others focal spikes and to determine the regions most involved when WBSDs occur; to assess the relationships between the extent of electroencephalography (EEG) discharges and the extent of metabolic changes measured by EEG/functional magnetic resonance imaging (fMRI). Thirty-seven patients with interictal epileptic discharges (IEDs) with frontocentral predominance underwent EEG/fMRI. Patients were divided into a Focal (20 patients) group with focal frontal spikes and a WBSD group (17 patients). Maps of hemodynamic responses related to IEDs were compared between the two groups. The mean number ± SD of IEDs in the Focal group was 137.5 ± 38.1 and in the WBSD group, 73.5 ± 16.6 (p = 0.07). The volume of hemodynamic responses in the WBSD group was significantly larger than in the Focal group (mean, 243.3 ± 41.1 versus 114.8 ± 27.4 cm(3), p = 0.01). Maximum hemodynamic responses occurred in both groups in the following regions: dorsolateral prefrontal, mesial prefrontal, cingulate, and supplementary motor cortices. Maxima in premotor and motor cortex, frontal operculum, frontopolar, and orbitofrontal regions were found only in the Focal group, and maxima in thalamus and caudate only occurred in the WBSD group. Thalamic responses were significantly more common in the WBSD group (14/17) than in the Focal group (7/20), p = 0.004. Deactivation in the default mode network was significantly more common in the WBSD group (14/17) than in the Focal group (10/20), p = 0.04. The spatial distribution and extent of blood oxygen level-dependent (BOLD) responses correlate well with electrophysiologic changes. Focal frontal spikes and WBSDs are not region specific in the frontal lobe, and the same frontal region can generate focal and generalized discharges. This suggests that widespread discharges reflect widespread epileptogenicity rather than a focal discharge located in a region favorable to spreading. The thalamus plays an important role in bilateral synchronization. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

PubMed

Guo, Lilin; Wang, Zhenzhong; Cabrerizo, Mercedes; Adjouadi, Malek

2017-05-01

This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

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

PubMed

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

2011-01-01

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

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

PubMed Central

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

2011-01-01

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

Variability of the hemodynamic response as a function of age and frequency of epileptic discharge in children with epilepsy.

PubMed

Jacobs, Julia; Hawco, Colin; Kobayashi, Eliane; Boor, Rainer; LeVan, Pierre; Stephani, Ulrich; Siniatchkin, Michael; Gotman, Jean

2008-04-01

EEG-fMRI is a non-invasive tool to investigate epileptogenic networks in patients with epilepsy. Different patterns of BOLD responses have been observed in children as compared to adults. A high intra- and intersubject variability of the hemodynamic response function (HRF) to epileptic discharges has been observed in adults. The actual HRF to epileptic discharges in children and its dependence on age are unknown. We analyzed 64 EEG-fMRI event types in 37 children (3 months to 18 years), 92% showing a significant BOLD response. HRFs were calculated for each BOLD cluster using a Fourier basis set. After excluding HRFs with a low signal-to-noise ratio, 126 positive and 98 negative HRFs were analyzed. We evaluated age-dependent changes as well as the effect of increasing numbers of spikes. Peak time, amplitude and signal-to-noise ratio of the HRF and the t-statistic score of the cluster were used as dependent variables. We observed significantly longer peak times of the HRF in the youngest children (0 to 2 years), suggesting that the use of multiple HRFs might be important in this group. A different coupling between neuronal activity and metabolism or blood flow in young children may cause this phenomenon. Even if the t-value increased with frequent spikes, the amplitude of the HRF decreased significantly with spike frequency. This reflects a violation of the assumptions of the General Linear Model and therefore the use of alternative analysis techniques may be more appropriate with high spiking rates, a common situation in children.

Variability of the hemodynamic response as a function of age and frequency of epileptic discharge in children with epilepsy

PubMed Central

Jacobs, Julia; Hawco, Colin; Kobayashi, Eliane; Boor, Rainer; LeVan, Pierre; Stephani, Ulrich; Siniatchkin, Michael; Gotman, Jean

2013-01-01

EEG-fMRI is a non-invasive tool to investigate epileptogenic networks in patients with epilepsy. Different patterns of BOLD responses have been observed in children as compared to adults. A high intra- and intersubject variability of the hemodynamic response function (HRF) to epileptic discharges has been observed in adults. The actual HRF to epileptic discharges in children and its dependence on age are unknown. We analyzed 64 EEG-fMRI event types in 37 children (3 months to 18 years), 92% showing a significant BOLD response. HRFs were calculated for each BOLD cluster using a Fourier basis set. After excluding HRFs with a low signal-to-noise ratio, 126 positive and 98 negative HRFs were analyzed. We evaluated age-dependent changes as well as the effect of increasing numbers of spikes. Peak time, amplitude and signal-to-noise ratio of the HRF and the t-statistic score of the cluster were used as dependent variables. We observed significantly longer peak times of the HRF in the youngest children (0 to 2 years), suggesting that the use of multiple HRFs might be important in this group. A different coupling between neuronal activity and metabolism or blood flow in young children may cause this phenomenon. Even if the t-value increased with frequent spikes, the amplitude of the HRF decreased significantly with spike frequency. This reflects a violation of the assumptions of the General Linear Model and therefore the use of alternative analysis techniques may be more appropriate with high spiking rates, a common situation in children. PMID:18221891

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

A method for the topographical identification and quantification of high frequency oscillations in intracranial electroencephalography recordings

PubMed Central

Waldman, Zachary J.; Shimamoto, Shoichi; Song, Inkyung; Orosz, Iren; Bragin, Anatol; Fried, Itzhak; Engel, Jerome; Staba, Richard; Sperling, Michael R.; Weiss, Shennan A.

2018-01-01

Objective To develop a reliable software method using a topographic analysis of time-frequency plots to distinguish ripple (80–200 Hz) oscillations that are often associated with EEG sharp waves or spikes (RonS) from sinusoid-like waveforms that appear as ripples but correspond with digital filtering of sharp transients contained in the wide bandwidth EEG. Methods A custom algorithm distinguished true from false ripples in one second intracranial EEG (iEEG) recordings using wavelet convolution, identifying contours of isopower, and categorizing these contours into sets of open or closed loop groups. The spectral and temporal features of candidate groups were used to classify the ripple, and determine its duration, frequency, and power. Verification of detector accuracy was performed on the basis of simulations, and visual inspection of the original and band-pass filtered signals. Results The detector could distinguish simulated true from false ripple on spikes (RonS). Among 2934 visually verified trials of iEEG recordings and spectrograms exhibiting RonS the accuracy of the detector was 88.5% with a sensitivity of 81.8% and a specificity of 95.2%. The precision was 94.5% and the negative predictive value was 84.0% (N = 12). Among, 1,370 trials of iEEG recording exhibiting RonS that were reviewed blindly without spectrograms the accuracy of the detector was 68.0%, with kappa equal to 0.01 ± 0.03. The detector successfully distinguished ripple from high spectral frequency ‘fast ripple’ oscillations (200–600 Hz), and characterize ripple duration and spectral frequency and power. The detector was confounded by brief bursts of gamma (30–80 Hz) activity in 7.31 ± 6.09% of trials, and in 30.2 ± 14.4% of the true RonS detections ripple duration was underestimated. Conclusions Characterizing the topographic features of a time-frequency plot generated by wavelet convolution is useful for distinguishing true oscillations from false oscillations generated by filter ringing. Significance Categorizing ripple oscillations and characterizing their properties can improve the clinical utility of the biomarker. PMID:29122445

Benign childhood epilepsy with centro-temporal spikes: quantitative EEG and the Wechsler intelligence scale for children (WISC-III).

PubMed

Tedrus, Gloria M A S; Fonseca, Lineu C; Tonelotto, Josiane M F; Costa, Rebeca M; Chiodi, Marcelo G

2006-07-01

Benign childhood epilepsy with centro-temporal spikes (BECTS) is a form of focal idiopathic epilepsy, with seizure remission by the age of 18. Recent studies have suggested that some children with BECTS can suffer from deficits of memory, attention and learning ability and in auditory-verbal and performance sub-tests. On the other hand, alterations in the baseline brain electrical activity determined by using the quantitative electroencephalogram (qEEG) have been described. The objective of this study was to evaluate the absolute and relative powers in the delta, theta, alpha and beta bands of the qEEG in children with BECTS, and their relation to IQ measurements (WISC-III). Twenty-six 8 to 11-year-old children with BECTS were studied, paired with a control group of healthy children according to age and gender. It was shown that the absolute delta and theta powers were statistically greater in the children with BECTS than in the control group, at almost all the electrodes. In the children with BECTS, a negative correlation (Pearson's correlation test) was observed at various electrodes between the absolute delta and theta powers and the performance IQ. These data indicate a possible relationship between maturational disturbance in the brain electrical activity development and the tendency for inferior cognitive performance in children with BECTS.

Encephalopathy with status epilepticus during sleep (ESES) induced by oxcarbazepine in idiopathic focal epilepsy in childhood

PubMed Central

Pavlidis, Elena; Rubboli, Guido; Nikanorova, Marina; Kölmel, Margarethe Sophie; Gardella, Elena

2015-01-01

Summary Encephalopathy with status epilepticus during sleep (ESES) is an age-related disorder characterized by neuropsychological regression, epilepsy and a typical EEG pattern of continuous epileptiform activity (> 85%) during NREM sleep. Cases of worsening or induction of ESES with phenytoin, carbamazepine and phenobarbital have been reported. We describe a child with benign epilepsy with centrotemporal spikes (BECTS) in whom treatment with oxcarbazepine (OXC) induced ESES. The patient was studied through repeated clinical-neuropsychological evaluations and 24-hour EEG recordings. He was treated with OXC two months after epilepsy onset. One month after starting OXC, he developed an abrupt and severe cognitive deterioration. A 24-hour EEG and neuropsychological tests showed an electro-clinical picture compatible with ESES. Withdrawal of OXC and introduction of other drugs were followed by a prompt improvement. Five months after ESES onset, a 24-hour EEG was normal. Our report indicates that OXC can induce ESES in BECTS. PMID:26415787

Postictal psychosis and its electrophysiological correlates in invasive EEG: a case report study and literature review.

PubMed

Kuba, Robert; Brázdil, Milan; Rektor, Ivan

2012-04-01

We identified two patients with medically refractory temporal lobe epilepsy, from whom intracranial EEG recordings were obtained at the time of postictal psychosis. Both patients had mesial temporal epilepsy associated with hippocampal sclerosis. In both patients, the postictal psychosis was associated with a continual "epileptiform" EEG pattern that differed from their interictal and ictal EEG findings (rhythmical slow wave and "abortive" spike-slow wave complex activity in the right hippocampus and lateral temporal cortex in case 1 and a periodic pattern of triphasic waves in the contacts recording activity from the left anterior cingulate gyrus). Some cases of postictal psychosis might be caused by the transient impairment of several limbic system structures due to the "continual epileptiform discharge" in some brain regions. Case 2 is the first report of a patient with TLE in whom psychotic symptoms were associated with the epileptiform impairment of the anterior cingulate gyrus. Copyright © 2012 Elsevier Inc. All rights reserved.

A variational Bayes spatiotemporal model for electromagnetic brain mapping.

PubMed

Nathoo, F S; Babul, A; Moiseev, A; Virji-Babul, N; Beg, M F

2014-03-01

In this article, we present a new variational Bayes approach for solving the neuroelectromagnetic inverse problem arising in studies involving electroencephalography (EEG) and magnetoencephalography (MEG). This high-dimensional spatiotemporal estimation problem involves the recovery of time-varying neural activity at a large number of locations within the brain, from electromagnetic signals recorded at a relatively small number of external locations on or near the scalp. Framing this problem within the context of spatial variable selection for an underdetermined functional linear model, we propose a spatial mixture formulation where the profile of electrical activity within the brain is represented through location-specific spike-and-slab priors based on a spatial logistic specification. The prior specification accommodates spatial clustering in brain activation, while also allowing for the inclusion of auxiliary information derived from alternative imaging modalities, such as functional magnetic resonance imaging (fMRI). We develop a variational Bayes approach for computing estimates of neural source activity, and incorporate a nonparametric bootstrap for interval estimation. The proposed methodology is compared with several alternative approaches through simulation studies, and is applied to the analysis of a multimodal neuroimaging study examining the neural response to face perception using EEG, MEG, and fMRI. © 2013, The International Biometric Society.

[Clinical and electrophysiologic studies on epileptic negative myoclonus in atypical benign partial epilepsy of childhood].

PubMed

Yang, Zhi-xian; Liu, Xiao-yan; Qin, Jiong; Zhang, Yue-hua; Bao, Xin-hua; Chang, Xing-zhi; Wu, Ye; Xiong, Hui

2008-12-01

To investigate the clinical, neurophysiologic characteristics and therapeutic considerations of epileptic negative myoclonus (ENM) in atypical benign partial epilepsy of childhood (ABPE). Video-EEG monitoring with outstretched arm tests were carried out in 17 patients, and 9 of them were examined with simultaneous electromyography (EMG). The ENM manifestations, electrophysiologic features and responses to antiepileptic drugs (AED) were analyzed. Seventeen patients were diagnosed as having benign childhood epilepsy with centrotemporal spikes (BECT) during the early course of the disease and were treated with AED. During the course of the disease, hand trembling, objects dropping, head nodding and instability during standing might be clues for ENM occurrence. ENM had been confirmed in our patients by outstretched arm tests during video-EEG recording. The ictal EEG showed that high-amplitude spikes followed by a slow wave over the contralateral motor areas. This was further confirmed by time-locked silent EMG in 9 patients. During ENM occurrence or recurrence, the habitual seizures and interictal discharges were exaggerated. Atypical absence seizures also occurred in 6 patients. The alteration of therapeutic options of AED relating to ENM appearance in some patients included the add-on therapy with carbamazepine (CBZ), oxcarbazepine, phenobarbital, or withdrawal of valproate (VPA). ENM was controlled in most cases by using VPA, clonazepam (CZP) and corticosteroid with different combination. ENM could occur during the course of ABPE. Outstretching arm tests during video-EEG monitoring in combination with EMG was essential to confirm ENM. The ENM occurrence was always associated with the frequency increasing of habitual seizures and the aggravation of interictal discharges. Some AED such as CBZ might induce ENM. VPA, benzodiazepines and corticosteroid with different combination were relatively effective in treatment of ENM.

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

PubMed Central

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

2016-01-01

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

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

PubMed

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

2010-02-15

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

Acquired auditory agnosia in childhood and normal sleep electroencephalography subsequently diagnosed as Landau-Kleffner syndrome: a report of three cases.

PubMed

van Bogaert, Patrick; King, Mary D; Paquier, Philippe; Wetzburger, Catherine; Labasse, Catherine; Dubru, Jean-Marie; Deonna, Thierry

2013-06-01

  We report three cases of Landau-Kleffner syndrome (LKS) in children (two females, one male) in whom diagnosis was delayed because the sleep electroencephalography (EEG) was initially normal.   Case histories including EEG, positron emission tomography findings, and long-term outcome were reviewed.   Auditory agnosia occurred between the age of 2 years and 3 years 6 months, after a period of normal language development. Initial awake and sleep EEG, recorded weeks to months after the onset of language regression, during a nap period in two cases and during a full night of sleep in the third case, was normal. Repeat EEG between 2 months and 2 years later showed epileptiform discharges during wakefulness and strongly activated by sleep, with a pattern of continuous spike-waves during slow-wave sleep in two patients. Patients were diagnosed with LKS and treated with various antiepileptic regimens, including corticosteroids. One patient in whom EEG became normal on hydrocortisone is making significant recovery. The other two patients did not exhibit a sustained response to treatment and remained severely impaired.   Sleep EEG may be normal in the early phase of acquired auditory agnosia. EEG should be repeated frequently in individuals in whom a firm clinical diagnosis is made to facilitate early treatment. © The Authors. Developmental Medicine & Child Neurology © 2012 Mac Keith Press.

Daytime outpatient versus inpatient video-EEG monitoring for presurgical evaluation in temporal lobe epilepsy.

PubMed

Guerreiro, Carlos A M; Montenegro, Maria Augusta; Kobayashi, Eliane; Noronha, Ana Lúcia A; Guerreiro, Marilisa M; Cendes, Fernando

2002-06-01

Video-EEG monitoring documentation of seizure localization is one of the most important aspects of a presurgical investigation in refractory temporal lobe epilepsy (TLE) patients. The objective of this study was to evaluate the efficacy of inpatient versus daytime outpatient telemetry. The authors evaluated prospectively 73 patients with medically intractable TLE. Ninety-one telemetry sessions were performed: 35 as inpatients and 56 as outpatients. Outpatient monitoring was performed in the EEG laboratory. They used 18-channel digital EEG. Medications were not changed in the outpatient group. For analysis of the data, time was counted in periods (12 hours = 1 period). Statistical analyses were performed using Student's t-test and the chi2 test. There were no differences between the two groups (outpatient versus inpatient) with respect to age and mean seizure frequency before monitoring, mean time to record the first seizure (1.1 versus 1.4 periods), mean number of seizures per period (0.6 for both groups), lateralization by interictal spiking (46% versus 57%), and lateralization by ictal EEG (59% versus 77%). Daytime outpatient video-EEG monitoring for presurgical evaluation is efficient and comparable with inpatient monitoring. Therefore, the improved cost benefit of outpatient monitoring may increase the access to surgery for individuals with intractable TLE.

Rectal diazepam in the treatment of absence status: a pharmacodynamic study

PubMed Central

Milligan, Norman; Dhillon, Soraya; Richens, Alan; Oxley, Jolyon

1981-01-01

Rectal administration of diazepam is highly effective in terminating absence status as judged by reduction of spike-wave activity in the EEG. Pharmacokinetic studies indicate that diazepam can have antiepileptic properties at serum levels well below those previously reported as being necessary to achieve a therapeutic effect. PMID:7310409

Cyclic alternating pattern and interictal epileptiform discharges during morning sleep after sleep deprivation in temporal lobe epilepsy.

PubMed

Giorgi, Filippo Sean; Maestri, Michelangelo; Guida, Melania; Carnicelli, Luca; Caciagli, Lorenzo; Ferri, Raffaele; Bonuccelli, Ubaldo; Bonanni, Enrica

2017-08-01

Sleep deprivation (SD) increases the occurrence of interictal epileptiform discharges (IED) compared to basal EEG in temporal lobe epilepsy (TLE). In adults, EEG after SD is usually performed in the morning after SD. We aimed to evaluate whether morning sleep after SD bears additional IED-inducing effects compared with nocturnal physiological sleep, and whether changes in sleep stability (described by the cyclic alternating pattern-CAP) play a significant role. Adult patients with TLE underwent in-lab night polysomnography (n-PSG) and, within 7days from n-PSG, they underwent also a morning EEG after night SD (SD-EEG). We included only TLE patients in which both recordings showed IED. SD-EEG consisted of waking up patients at 2:00 AM and performing video EEG at 8:00 AM. For both recordings, we obtained the following markers for the first sleep cycle: IED/h (Spike Index, SI), sleep macrostructure, microstructure (NREM CAP rate; A1, A2 and A3 Indices), and SI association with CAP variables. The macrostructure of the first sleep cycle was similar in n-PSG and morning SD-EEG, whereas CAP rate and SI were significantly higher in SD-EEG. SI increase was selectively associated with CAP phases. SD increases the instability of morning recovery sleep compared with n-PSG, and particularly enhances CAP A1 phases, which are associated with the majority of IED. Thus, higher instability of morning recovery sleep may account at least in part for the increased IED yield in SD-EEG in TLE patients. Copyright © 2017 Elsevier Inc. All rights reserved.

Micropower CMOS Integrated Low-Noise Amplification, Filtering, and Digitization of Multimodal Neuropotentials

PubMed Central

Mollazadeh, Mohsen; Murari, Kartikeya; Cauwenberghs, Gert; Thakor, Nitish

2009-01-01

Electrical activity in the brain spans a wide range of spatial and temporal scales, requiring simultaneous recording of multiple modalities of neurophysiological signals in order to capture various aspects of brain state dynamics. Here, we present a 16-channel neural interface integrated circuit fabricated in a 0.5 μm 3M2P CMOS process for selective digital acquisition of biopotentials across the spectrum of neural signal modalities in the brain, ranging from single spike action potentials to local field potentials (LFP), electrocorticograms (ECoG), and electroencephalograms (EEG). Each channel is composed of a tunable bandwidth, fixed gain front-end amplifier and a programmable gain/resolution continuous-time incremental ΔΣ analog-to-digital converter (ADC). A two-stage topology for the front-end voltage amplifier with capacitive feedback offers independent tuning of the amplifier bandpass frequency corners, and attains a noise efficiency factor (NEF) of 2.9 at 8.2 kHz bandwidth for spike recording, and a NEF of 3.2 at 140 Hz bandwidth for EEG recording. The amplifier has a measured midband gain of 39.6 dB, frequency response from 0.2 Hz to 8.2 kHz, and an input-referred noise of 1.94 μVrms while drawing 12.2 μA of current from a 3.3 V supply. The lower and higher cutoff frequencies of the bandpass filter are adjustable from 0.2 to 94 Hz and 140 Hz to 8.2 kHz, respectively. At 10-bit resolution, the ADC has an SNDR of 56 dB while consuming 76 μW power. Time-modulation feedback in the ADC offers programmable digital gain (1–4096) for auto-ranging, further improving the dynamic range and linearity of the ADC. Experimental recordings with the system show spike signals in rat somatosensory cortex as well as alpha EEG activity in a human subject. PMID:20046962

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

NASA Astrophysics Data System (ADS)

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

2007-03-01

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

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

PubMed

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

2009-07-01

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

Wavelet-based localization of oscillatory sources from magnetoencephalography data.

PubMed

Lina, J M; Chowdhury, R; Lemay, E; Kobayashi, E; Grova, C

2014-08-01

Transient brain oscillatory activities recorded with Eelectroencephalography (EEG) or magnetoencephalography (MEG) are characteristic features in physiological and pathological processes. This study is aimed at describing, evaluating, and illustrating with clinical data a new method for localizing the sources of oscillatory cortical activity recorded by MEG. The method combines time-frequency representation and an entropic regularization technique in a common framework, assuming that brain activity is sparse in time and space. Spatial sparsity relies on the assumption that brain activity is organized among cortical parcels. Sparsity in time is achieved by transposing the inverse problem in the wavelet representation, for both data and sources. We propose an estimator of the wavelet coefficients of the sources based on the maximum entropy on the mean (MEM) principle. The full dynamics of the sources is obtained from the inverse wavelet transform, and principal component analysis of the reconstructed time courses is applied to extract oscillatory components. This methodology is evaluated using realistic simulations of single-trial signals, combining fast and sudden discharges (spike) along with bursts of oscillating activity. The method is finally illustrated with a clinical application using MEG data acquired on a patient with a right orbitofrontal epilepsy.

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.

Removal of EOG Artifacts from EEG Recordings Using Stationary Subspace Analysis

PubMed Central

Zeng, Hong; Song, Aiguo

2014-01-01

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

Inferring oscillatory modulation in neural spike trains

PubMed Central

Arai, Kensuke; Kass, Robert E.

2017-01-01

Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram (EEG) and local field potential (LFP) in bulk brain matter, and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation. Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals, and independently from the spike train alone, but behavior or stimulus triggered firing-rate modulation, spiking sparseness, presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods, present challenges to searching for temporal structures present in the spike train. In order to study oscillatory modulation in real data collected under a variety of experimental conditions, we describe a flexible point-process framework we call the Latent Oscillatory Spike Train (LOST) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness, event-locked firing rate non-stationarity, and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation. We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment, and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm. Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect oscillations when the firing rate is low, the modulation is weak, and when the modulating oscillation has a broad spectral peak. PMID:28985231

The Landau-Kleffner Syndrome

PubMed Central

Pearl, Phillip L.; Carrazana, Enrique J.; Holmes, Gregory L.

2001-01-01

Landau-Kleffner syndrome (LKS), or acquired epileptiform aphasia, is an epilepsy syndrome involving progressive neuropsychological impairment related to the appearance of paroxysmal electroencephalograph (EEG) activity. LKS appears to share a common pathophysiologic mechanism with continuous spike-wave of sleep (CSWS), acquired epileptic opercular syndrome (AEOS), and even benign childhood epilepsy with centrotemporal spikes (BECTS), with differentiating factors including age of onset, area of primary epileptogenicity, and severity of clinical presentation. This article covers the clinical, diagnostic, therapeutic, and prognostic features of LKS. In a child with autistic spectrum disorder, the presence of a fluctuating clinical course or regression should raise suspicion for the presence of associated epilepsy. PMID:15309183

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

PubMed

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

2016-01-01

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

Correlation of invasive EEG and scalp EEG.

PubMed

Ramantani, Georgia; Maillard, Louis; Koessler, Laurent

2016-10-01

Ever since the implementation of invasive EEG recordings in the clinical setting, it has been perceived that a considerable proportion of epileptic discharges present at a cortical level are missed by routine scalp EEG recordings. Several in vitro, in vivo, and simulation studies have been performed in the past decades aiming to clarify the interrelations of cortical sources with their scalp and invasive EEG correlates. The amplitude ratio of cortical potentials to their scalp EEG correlates, the extent of the cortical area involved in the discharge, as well as the localization of the cortical source and its geometry have been each independently linked to the recording of the cortical discharge with scalp electrodes. The need to elucidate these interrelations has been particularly imperative in the field of epilepsy surgery with its rapidly growing EEG-based localization technologies. Simultaneous multiscale EEG recordings with scalp, subdural and/or depth electrodes, applied in presurgical epilepsy workup, offer an excellent opportunity to shed some light to this fundamental issue. Whereas past studies have considered predominantly neocortical sources in the context of temporal lobe epilepsy, current investigations have included deep sources, as in mesial temporal epilepsy, as well as extratemporal sources. Novel computational tools may serve to provide surrogates for the shortcomings of EEG recording methodology and facilitate further developments in modern electrophysiology. Copyright © 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Clustering-Constrained ICA for Ballistocardiogram Artifacts Removal in Simultaneous EEG-fMRI

PubMed Central

Wang, Kai; Li, Wenjie; Dong, Li; Zou, Ling; Wang, Changming

2018-01-01

Combination of electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) plays a potential role in neuroimaging due to its high spatial and temporal resolution. However, EEG is easily influenced by ballistocardiogram (BCG) artifacts and may cause false identification of the related EEG features, such as epileptic spikes. There are many related methods to remove them, however, they do not consider the time-varying features of BCG artifacts. In this paper, a novel method using clustering algorithm to catch the BCG artifacts' features and together with the constrained ICA (ccICA) is proposed to remove the BCG artifacts. We first applied this method to the simulated data, which was constructed by adding the BCG artifacts to the EEG signal obtained from the conventional environment. Then, our method was tested to demonstrate the effectiveness during EEG and fMRI experiments on 10 healthy subjects. In simulated data analysis, the value of error in signal amplitude (Er) computed by ccICA method was lower than those from other methods including AAS, OBS, and cICA (p < 0.005). In vivo data analysis, the Improvement of Normalized Power Spectrum (INPS) calculated by ccICA method in all electrodes was much higher than AAS, OBS, and cICA methods (p < 0.005). We also used other evaluation index (e.g., power analysis) to compare our method with other traditional methods. In conclusion, our novel method successfully and effectively removed BCG artifacts in both simulated and vivo EEG data tests, showing the potentials of removing artifacts in EEG-fMRI applications. PMID:29487499

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

PubMed

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

2018-01-01

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

Continuous EEG-fMRI in Pre-Surgical Evaluation of a Patient with Symptomatic Seizures: Bold Activation Linked to Interictal Epileptic Discharges Caused by Cavernoma.

PubMed

Avesani, M; Formaggio, E; Milanese, F; Baraldo, A; Gasparini, A; Cerini, R; Bongiovanni, L G; Pozzi Mucelli, R; Fiaschi, A; Manganotti, P

2008-04-07

We used continuous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) to identify the linkage between the "epileptogenic" and the "irritative" area in a patient with symptomatic epilepsy (cavernoma, previously diagnosed and surgically treated), i.e. a patient with a well known "epileptogenic area", and to increase the possibility of a non invasive pre-surgical evaluation of drug-resistant epilepsies. A compatible MRI system was used (EEG with 29 scalp electrodes and two electrodes for ECG and EMG) and signals were recorded with a 1.5 Tesla MRI scanner. After the recording session and MRI artifact removal, EEG data were analyzed offline and used as paradigms in fMRI study. Activation (EEG sequences with interictal slow-spiked-wave activity) and rest (sequences of normal EEG) conditions were compared to identify the potential resulting focal increase in BOLD signal and to consider if this is spatially linked to the interictal focus used as a paradigm and to the lesion. We noted an increase in the BOLD signal in the left neocortical temporal region, laterally and posteriorly to the poro-encephalic cavity (residual of cavernoma previously removed), that is around the "epileptogenic area". In our study "epileptogenic" and "irritative" areas were connected with each other. Combined EEG-fMRI may become routine in clinical practice for a better identification of an irritative and lesional focus in patients with symptomatic drug-resistant epilepsy.

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

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

PubMed

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

2014-01-01

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

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

PubMed Central

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

2014-01-01

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

[A case of non-photosensitive, self-induced epileptic seizures with pacygyria].

PubMed

Nagai, H; Shikata, A; Sato, N; Takeuchi, Y; Sawada, T

1998-09-01

We report an 11-year-old boy with a non-photosensitive epileptic self-induced seizures, pacygyria and familial ataxia. His grandmother and aunts had dysarthria, and his mother had developed progressive ataxia and myoclonus since 40 years old. His older sister had ataxia, mental retardation and epilepsy. As for the boy, motor developmental delay with muscle hypertonicity of left extremities was recognized at the age of 5 months. Mental retardation and ataxia was recognized at the age of 3 years and slight mental regression is recognized at the age of 11 years. No special findings were detected in an examination of his blood and cerebrospinal fluid, including amino acids, lysosomal enzymes activity and genetic analysis for dentatorubralpallidoluysian atrophy. Brain magnetic resonance imaging revealed pachygyria of the right cerebral cortecies. At the age of two, he began to induce seizures with impairment of consciousness in himself by waving his right hand over his face which was directed toward a source of bright light. At the age of seven, he developed spontaneous seizures with impairment of consciousness. An EEG showed frequent spikes in the occipital areas, on the right and left sides occurring either independently or synchronously. Intermittent photic stimulation and pattern stimulation did not induce a paroxysmal discharge in EEG. Ictal EEG suggested that the origin of the seizures was the occipital lobe. Treatment with valporate and zonisamide was effective in reducing the seizures. The findings of our case imply the pathogenesis of self-induced seizures and the relationship between PME and neuronal migration disorders.

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

PubMed

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

2017-09-01

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

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

PubMed

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

2015-11-01

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

Huperzine A prophylaxis against pentylenetetrazole-induced seizures in rats is associated with increased cortical inhibition.

PubMed

Gersner, R; Ekstein, D; Dhamne, S C; Schachter, S C; Rotenberg, A

2015-11-01

Huperzine A (HupA) is a naturally occurring compound found in the firmoss Huperzia serrata. While HupA is a potent acetylcholinesterase inhibitor, its full pharmacologic profile is incompletely described. Since previous works suggested a capacity for HupA to prophylax against seizures, we tested the HupA antiepileptic potential in pentylenetetrazole (PTZ) rat epilepsy model and explored its mechanism of action by spectral EEG analysis and by paired-pulse transcranial magnetic stimulation (ppTMS), a measure of GABA-mediated intracortical inhibition. We tested whether HupA suppresses seizures in the rat PTZ acute seizure model, and quantified latency to first myoclonus and to generalized tonic-clonic seizure, and spike frequency on EEG. Additionally, we measured power in the EEG gamma frequency band which is associated with GABAergic cortical interneuron activation. Then, as a step toward further examining the HupA antiepileptic mechanism of action, we tested long-interval intracortical inhibition (LICI) using ppTMS coupled with electromyography to assess whether HupA augments GABA-mediated paired-pulse inhibition of the motor evoked potential. We also tested whether the HupA effect on paired-pulse inhibition was central or peripheral by comparison of outcomes following administration of HupA or the peripheral acetylcholinesterase inhibitor pyridostigmine. We also tested whether the HupA effect was dependent on central muscarinic or GABAA receptors by co-administration of HupA and atropine or PTZ, respectively. In tests of antiepileptic potential, HupA suppressed seizures and epileptic spikes on EEG. Spectral EEG analysis also revealed enhanced gamma frequency band power with HupA treatment. By ppTMS we found that HupA increases intracortical inhibition and blocks PTZ-induced cortical excitation. Atropine co-administration with HupA did not alter HupA-induced intracortical inhibition suggesting independent of muscarinic acetylcholine receptors mechanism in this model. Last, pyridostigmine did not affect the ppTMS-measured cortical inhibition suggesting that HupA-induced effect is centrally-mediated. Our data support antiepileptic HupA applications, and suggest that such activity may be via enhancement of GABAergic intracortical inhibition. Copyright © 2015 Elsevier B.V. All rights reserved.

Left hemibody myoclonus due to anomalous right vertebral artery.

PubMed

Coelho, Miguel; Marti, Maria J; Valls-Solé, Josep; Pujol, Teresa; Tolosa, Eduardo

2005-01-01

A 43-year-old man presented with sporadic, sudden, brief, and involuntary jerks of his left limbs and trunk muscles. The electromyographic recordings showed short-lasting highly synchronized bursts, compatible with myoclonus limited to the left hemibody. Blink reflex, masseter silent period, cortical and spinal magnetic stimulation, somatosensory cortical evoked potentials, and electroencephalogram (EEG) were normal; the EEG back-averaging showed no spikes preceding the myoclonus. Magnetic resonance imaging and magnetic resonance angiography showed the presence of an anomalous nonectasic right vertebral artery compressing the right side of ventral medulla oblongata. We hypothesize that the aberrant right vertebral artery induced abnormal activation of descending motor tracts responsible for the myoclonus. (c) 2004 Movement Disorder Society.

Integrating EEG and fMRI in epilepsy.

PubMed

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

2011-02-14

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

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.

Safety and EEG data quality of concurrent high-density EEG and high-speed fMRI at 3 Tesla.

PubMed

Foged, Mette Thrane; Lindberg, Ulrich; Vakamudi, Kishore; Larsson, Henrik B W; Pinborg, Lars H; Kjær, Troels W; Fabricius, Martin; Svarer, Claus; Ozenne, Brice; Thomsen, Carsten; Beniczky, Sándor; Paulson, Olaf B; Posse, Stefan

2017-01-01

Concurrent EEG and fMRI is increasingly used to characterize the spatial-temporal dynamics of brain activity. However, most studies to date have been limited to conventional echo-planar imaging (EPI). There is considerable interest in integrating recently developed high-speed fMRI methods with high-density EEG to increase temporal resolution and sensitivity for task-based and resting state fMRI, and for detecting interictal spikes in epilepsy. In the present study using concurrent high-density EEG and recently developed high-speed fMRI methods, we investigate safety of radiofrequency (RF) related heating, the effect of EEG on cortical signal-to-noise ratio (SNR) in fMRI, and assess EEG data quality. The study compared EPI, multi-echo EPI, multi-band EPI and multi-slab echo-volumar imaging pulse sequences, using clinical 3 Tesla MR scanners from two different vendors that were equipped with 64- and 256-channel MR-compatible EEG systems, respectively, and receive only array head coils. Data were collected in 11 healthy controls (3 males, age range 18-70 years) and 13 patients with epilepsy (8 males, age range 21-67 years). Three of the healthy controls were scanned with the 256-channel EEG system, the other subjects were scanned with the 64-channel EEG system. Scalp surface temperature, SNR in occipital cortex and head movement were measured with and without the EEG cap. The degree of artifacts and the ability to identify background activity was assessed by visual analysis by a trained expert in the 64 channel EEG data (7 healthy controls, 13 patients). RF induced heating at the surface of the EEG electrodes during a 30-minute scan period with stable temperature prior to scanning did not exceed 1.0° C with either EEG system and any of the pulse sequences used in this study. There was no significant decrease in cortical SNR due to the presence of the EEG cap (p > 0.05). No significant differences in the visually analyzed EEG data quality were found between EEG recorded during high-speed fMRI and during conventional EPI (p = 0.78). Residual ballistocardiographic artifacts resulted in 58% of EEG data being rated as poor quality. This study demonstrates that high-density EEG can be safely implemented in conjunction with high-speed fMRI and that high-speed fMRI does not adversely affect EEG data quality. However, the deterioration of the EEG quality due to residual ballistocardiographic artifacts remains a significant constraint for routine clinical applications of concurrent EEG-fMRI.

Reflex reading epilepsy: effect of linguistic characteristics on spike frequency.

PubMed

Safi, Dima; Lassonde, Maryse; Nguyen, Dang Khoa; Denault, Carole; Macoir, Joël; Rouleau, Isabelle; Béland, Renée

2011-04-01

Reading epilepsy is a rare reflex epilepsy in which seizures are provoked by reading. Several cases have been described in the literature, but the pathophysiological processes vary widely and remain unclear. We describe a 42-year-old male patient with reading epilepsy evaluated using clinical assessments and continuous video/EEG recordings. We administered verbal, nonverbal, and reading tasks to determine factors precipitating seizures. Linguistic characteristics of the words were manipulated. Results indicated that reading-induced seizures were significantly more numerous than those observed during verbal and nonverbal tasks. In reading tasks, spike frequency significantly increased with involvement of the phonological reading route. Spikes were recorded predominantly in left parasagittal regions. Future cerebral imaging studies will enable us to visualize the spatial localization and temporal course of reading-induced seizures and brain activity involved in reading. A better understanding of reading epilepsy is crucial for reading rehabilitation in these patients. Copyright © 2011 Elsevier Inc. All rights reserved.

Serotonergic modulation of hippocampal pyramidal cells in euthermic, cold-acclimated, and hibernating hamsters

NASA Technical Reports Server (NTRS)

Horrigan, D. J.; Horwitz, B. A.; Horowitz, J. M.

1997-01-01

Serotonergic fibers project to the hippocampus, a brain area previously shown to have distinctive changes in electroencephalograph (EEG) activity during entrance into and arousal from hibernation. The EEG activity is generated by pyramidal cells in both hibernating and nonhibernating species. Using the brain slice preparation, we characterized serotonergic responses of these CA1 pyramidal cells in euthermic, cold-acclimated, and hibernating Syrian hamsters. Stimulation of Shaffer-collateral/commissural fibers evoked fast synaptic excitation of CA1 pyramidal cells, a response monitored by recording population spikes (the synchronous generation of action potentials). Neuromodulation by serotonin (5-HT) decreased population spike amplitude by 54% in cold-acclimated animals, 80% in hibernating hamsters, and 63% in euthermic animals. The depression was significantly greater in slices from hibernators than from cold-acclimated animals. In slices from euthermic animals, changes in extracellular K+ concentration between 2.5 and 5.0 mM did not significantly alter serotonergic responses. The 5-HT1A agonist 8-hydroxy-2(di-n-propylamino)tetralin mimicked serotonergic inhibition in euthermic hamsters. Results show that 5-HT is a robust neuromodulator not only in euthermic animals but also in cold-acclimated and hibernating hamsters.

Determining the degree of synchronism for intermittent phase synchronization in human electroencephalography data

NASA Astrophysics Data System (ADS)

Koloskova, A. D.; Moskalenko, O. I.

2017-05-01

The phenomenon of intermittent phase synchronization during development of epileptic activity in human beings has been discovered based on EEG data. The presence of synchronous behavior phases has been detected both during spike-wave discharges and in the regions of background activity of the brain. The degree of synchronism in the intermittent phase-synchronization regime in both cases has been determined, and it has been established that spike-wave discharges are characterized by a higher degree of synchronism than exists in the regions of background activity of the brain. To determine the degree of synchronism, a modified method of evaluating zero conditional Lyapunov exponents from time series is proposed.

[Electroencephalography and epileptology in the 20th century].

PubMed

Karbowski, K

1995-12-05

In 1875, Caton was already able to detect cerebral electric currents during experimental studies in animals. In 1914, Cybulski and Jeleńska-Macieszyna reported on the increase of current-intensity during a focal motor epileptic seizure. In 1929 in Jena, Berger revolutionized the study of epilepsy with his paper on the human electroencephalogram 'Uber das Elektrenkephalogramm des Menschen'. His discovery and further publications as well as later works of numerous researchers, especially F. and E. Gibbs, Lennox, Penfield and Jasper, made it possible to distinguish different forms of 'little' epileptic seizures and to separate them from nonepileptic paroxysmal disorders. New epileptic syndromes could be singled out, as e.g. the symptomatic epilepsy of childhood with variable clinical manifestations of seizures and slow spike-wave complexes in the EEG (Lennox-Gastaut syndrome) or the benign partial epilepsy of childhood with centrotemporal EEG spikes. In these fields, as well as for the epileptic seizures in newborns and babies and for the differentiation between epileptic and nonepileptic twilight states in later stages of life, the EEG remains an indispensable tool in the CT and MRI era. It also contributes largely to the diagnosis of nonepileptic cerebral illness such as herpes simplex encephalitis, subacute sclerosing panencephalitis van Bogaert and Creutzfeldt-Jakob disease. Since the introduction of phenobarbital by Hauptmann in 1912, the palet of effective drugs against epilepsy, such as phenytoin, carbamazepine, valproate and benzodiazepines used for status-epilepticus treatment, became essentially larger. The value of newer substances (vigabatrin, progabide, gabapentin, lamotrigin) can't be estimated actually.(ABSTRACT TRUNCATED AT 250 WORDS)

Effects of Marijuana on Ictal and Interictal EEG Activities in Idiopathic Generalized Epilepsy.

PubMed

Sivakumar, Sanjeev; Zutshi, Deepti; Seraji-Bozorgzad, Navid; Shah, Aashit K

2017-01-01

Marijuana-based treatment for refractory epilepsy shows promise in surveys, case series, and clinical trials. However, literature on their EEG effects is sparse. Our objective is to analyze the effect of marijuana on EEG in a 24-year-old patient with idiopathic generalized epilepsy treated with cannabis. We blindly reviewed 3 long-term EEGs-a 24-hour study while only on antiepileptic drugs, a 72-hour EEG with Cannabis indica smoked on days 1 and 3 in addition to antiepileptic drugs, and a 48-hour EEG with combination C indica/sativa smoked on day 1 plus antiepileptic drugs. Generalized spike-wave discharges and diffuse paroxysmal fast activity were categorized as interictal and ictal, based on duration of less than 10 seconds or greater, respectively. Data from three studies concatenated into contiguous time series, with usage of marijuana modeled as time-dependent discrete variable while interictal and ictal events constituted dependent variables. Analysis of variance as initial test for significance followed by time series analysis using Generalized Autoregressive Conditional Heteroscedasticity model was performed. Statistical significance for lower interictal events (analysis of variance P = 0.001) was seen during C indica use, but not for C indica/sativa mixture (P = 0.629) or ictal events (P = 0.087). However, time series analysis revealed a significant inverse correlation between marijuana use, with interictal (P < 0.0004) and ictal (P = 0.002) event rates. Using a novel approach to EEG data, we demonstrate a decrease in interictal and ictal electrographic events during marijuana use. Larger samples of patients and EEG, with standardized cannabinoid formulation and dosing, are needed to validate our findings.

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

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.

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.

Screening EEG in Aircrew Selection: Clinical Aerospace Neurology Perspective

NASA Technical Reports Server (NTRS)

Clark, Jonathan B.; Riley, Terrence

2001-01-01

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

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

PubMed Central

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

2015-01-01

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

EEG source analysis of data from paralysed subjects

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Advantages of Repeated Low Dose against Single High Dose of Kainate in C57BL/6J Mouse Model of Status Epilepticus: Behavioral and Electroencephalographic Studies

PubMed Central

Beamer, Edward; Sills, Graeme J.; Thippeswamy, Thimmasettappa

2014-01-01

A refined kainate (KA) C57BL/6J mouse model of status epilepticus (SE) using a repeated low dose (RLD) of KA (5 mg/kg, intraperitoneal; at 30 min intervals) was compared with the established single high dose (SHD) of KA (20 mg/kg, intraperitoneal) model. In the RLD group, increased duration of convulsive motor seizures (CMS, Racine scale stage ≥3) with a significant reduction in mortality from 21% to 6% and decreased variability in seizure severity between animals/batches were observed when compared to the SHD group. There was a significant increase in the percentage of animals that reached stage-5 seizures (65% versus 96%) in the RLD group. Integrated real-time video-EEG analysis of both groups, using NeuroScore software, revealed stage-specific spikes and power spectral density characteristics. When the seizures progressed from non-convulsive seizures (NCS, stage 1–2) to CMS (stage 3–5), the delta power decreased which was followed by an increase in gamma and beta power. A transient increase in alpha and sigma power marked the transition from NCS to CMS with characteristic ‘high frequency trigger’ spikes on the EEG, which had no behavioral expression. During SE the spike rate was higher in the RLD group than in the SHD group. Overall these results confirm that RLD of KA is a more robust and consistent mouse model of SE than the SHD of KA mouse model. PMID:24802808

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

PubMed

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

2012-01-01

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

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.

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

Microsensors and wireless system for monitoring epilepsy

NASA Astrophysics Data System (ADS)

Whitchurch, Ashwin K.; Ashok, B. H.; Kumaar, Raman V.; Sarukesi, K.; Jose, K. A.; Varadan, Vijay K.

2003-07-01

Epilepsy is a form of brain disorder caused by abnormal discharges of neurons. The most common manifestations of epilepsy are seizures which could affect visual, aural and motor abilities of a person. Absence epilepsy is a form of epilepsy common mostly in children. The most common manifestations of absence epilepsy are staring and transient loss of responsiveness. Also, subtle motor activities may occur. Due to the subtle nature of these symptoms, episodes of absence epilepsy may often go unrecognized for long periods of time or be mistakenly attributed to attention deficit disorder or daydreaming. Spells of absence epilepsy may last about 10 seconds and occur hundreds of times each day. Patients have no recollections of the events occurred during those seizures and will resume normal activity without any postictal symptoms. The EEG during such episodes of Absence epilepsy shows intermittent activity of 3 Hz generalized spike and wave complexes. As EEG is the only way of detecting such symptoms, it is required to monitor the EEG of the patient for a long time, usually the whole day. This requires that the patient be connected to the EEG recorder all the time and thus remain only in the bed. So, effectively the EEG is being monitored only when the patient is stationary. The wireless monitoring system described in this paper aims at eliminating this constraint and enables the physician to monitor the EEG when the patient resumes his normal activities. This approach could even help the doctor identify possible triggers of absence epilepsy.

Magnetoencephalographic Mapping of Epileptic Spike Population Using Distributed Source Analysis: Comparison With Intracranial Electroencephalographic Spikes.

PubMed

Tanaka, Naoaki; Papadelis, Christos; Tamilia, Eleonora; Madsen, Joseph R; Pearl, Phillip L; Stufflebeam, Steven M

2018-04-27

This study evaluates magnetoencephalographic (MEG) spike population as compared with intracranial electroencephalographic (IEEG) spikes using a quantitative method based on distributed source analysis. We retrospectively studied eight patients with medically intractable epilepsy who had an MEG and subsequent IEEG monitoring. Fifty MEG spikes were analyzed in each patient using minimum norm estimate. For individual spikes, each vertex in the source space was considered activated when its source amplitude at the peak latency was higher than a threshold, which was set at 50% of the maximum amplitude over all vertices. We mapped the total count of activation at each vertex. We also analyzed 50 IEEG spikes in the same manner over the intracranial electrodes and created the activation count map. The location of the electrodes was obtained in the MEG source space by coregistering postimplantation computed tomography to MRI. We estimated the MEG- and IEEG-active regions associated with the spike populations using the vertices/electrodes with a count over 25. The activation count maps of MEG spikes demonstrated the localization associated with the spike population by variable count values at each vertex. The MEG-active region overlapped with 65 to 85% of the IEEG-active region in our patient group. Mapping the MEG spike population is valid for demonstrating the trend of spikes clustering in patients with epilepsy. In addition, comparison of MEG and IEEG spikes quantitatively may be informative for understanding their relationship.

A case of temporal lobe epilepsy with improvement of clinical symptoms and single photon emission computed tomography findings after treatment with clonazepam.

PubMed

Ide, M; Mizukami, K; Suzuki, T; Shiraishi, H

2000-10-01

A 26-year-old female presented psychomotor seizures, deja vu and amnestic syndrome after meningitis at the age of 14 years. Repeated electroencephalograms (EEG) demonstrated occasional spikes localized in the right temporal region in addition to a considerable amount of theta waves mainly in the right fronto-temporal region. Single photon emission computed tomography (SPECT) showed a marked hypoperfusion corresponding to the region in which the EEG showed abnormal findings, although magnetic resonance imaging (MRI) demonstrated no abnormal findings associated with the clinical features. Treatment with clonazepam in addition to sodium valproate resulted in a remarkable improvement of clinical symptoms (i.e. psychomotor seizures and deja vu), as well as of the EEG and SPECT findings. The present study suggests that SPECT is a useful method not only to determine the localization of regions associated with temporal lobe epilepsy but also to evaluate the effect of treatment in temporal lobe epilepsy.

Neurobehavioral consequences of continuous spike and waves during slow sleep (CSWS) in a pediatric population: A pattern of developmental hindrance.

PubMed

De Giorgis, Valentina; Filippini, Melissa; Macasaet, Joyce Ann; Masnada, Silvia; Veggiotti, Pierangelo

2017-09-01

Continuous spike and waves during slow sleep (CSWS) is a typical EEG pattern defined as diffuse, bilateral and recently also unilateral or focal localization spike-wave occurring in slow sleep or non-rapid eye movement sleep. Literature results so far point out a progressive deterioration and decline of intellectual functioning in CSWS patients, i.e. a loss of previously normally acquired skills, as well as persistent neurobehavioral disorders, beyond seizure and EEG control. The objective of this study was to shed light on the neurobehavioral impact of CSWS and to identify the potential clinical risk factors for development. We conducted a retrospective study involving a series of 16 CSWS idiopathic patients age 3-16years, considering the entire duration of epilepsy from the onset to the outcome, i.e. remission of CSWS pattern. All patients were longitudinally assessed taking into account clinical (sex, age at onset, lateralization and localization of epileptiform abnormalities, spike wave index, number of antiepileptic drugs) and behavioral features. Intelligent Quotient (IQ) was measured in the whole sample, whereas visuo-spatial attention, visuo-motor skills, short term memory and academic abilities (reading and writing) were tested in 6 out of 16 patients. Our results showed that the most vulnerable from an intellectual point of view were those children who had an early-onset of CSWS whereas those with later onset resulted less affected (p=0.004). Neuropsychological outcome was better than the behavioral one and the lexical-semantic route in reading and writing resulted more severely affected compared to the phonological route. Cognitive deterioration is one but not the only consequence of CSWS. Especially with respect to verbal skills, CSWS is responsible of a pattern of consequences in terms of developmental hindrance, including slowing of development and stagnation, whereas deterioration is rare. Behavioral and academic problems tend to persist beyond epilepsy resolution. Copyright © 2017 Elsevier Inc. All rights reserved.

Principal dynamic mode analysis of neural mass model for the identification of epileptic states

NASA Astrophysics Data System (ADS)

Cao, Yuzhen; Jin, Liu; Su, Fei; Wang, Jiang; Deng, Bin

2016-11-01

The detection of epileptic seizures in Electroencephalography (EEG) signals is significant for the diagnosis and treatment of epilepsy. In this paper, in order to obtain characteristics of various epileptiform EEGs that may differentiate different states of epilepsy, the concept of Principal Dynamic Modes (PDMs) was incorporated to an autoregressive model framework. First, the neural mass model was used to simulate the required intracerebral EEG signals of various epileptiform activities. Then, the PDMs estimated from the nonlinear autoregressive Volterra models, as well as the corresponding Associated Nonlinear Functions (ANFs), were used for the modeling of epileptic EEGs. The efficient PDM modeling approach provided physiological interpretation of the system. Results revealed that the ANFs of the 1st and 2nd PDMs for the auto-regressive input exhibited evident differences among different states of epilepsy, where the ANFs of the sustained spikes' activity encountered at seizure onset or during a seizure were the most differentiable from that of the normal state. Therefore, the ANFs may be characteristics for the classification of normal and seizure states in the clinical detection of seizures and thus provide assistance for the diagnosis of epilepsy.

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

PubMed

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

2013-10-01

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

Safety and EEG data quality of concurrent high-density EEG and high-speed fMRI at 3 Tesla

PubMed Central

Foged, Mette Thrane; Lindberg, Ulrich; Vakamudi, Kishore; Larsson, Henrik B. W.; Pinborg, Lars H.; Kjær, Troels W.; Fabricius, Martin; Svarer, Claus; Ozenne, Brice; Thomsen, Carsten; Beniczky, Sándor; Posse, Stefan

2017-01-01

Purpose Concurrent EEG and fMRI is increasingly used to characterize the spatial-temporal dynamics of brain activity. However, most studies to date have been limited to conventional echo-planar imaging (EPI). There is considerable interest in integrating recently developed high-speed fMRI methods with high-density EEG to increase temporal resolution and sensitivity for task-based and resting state fMRI, and for detecting interictal spikes in epilepsy. In the present study using concurrent high-density EEG and recently developed high-speed fMRI methods, we investigate safety of radiofrequency (RF) related heating, the effect of EEG on cortical signal-to-noise ratio (SNR) in fMRI, and assess EEG data quality. Materials and methods The study compared EPI, multi-echo EPI, multi-band EPI and multi-slab echo-volumar imaging pulse sequences, using clinical 3 Tesla MR scanners from two different vendors that were equipped with 64- and 256-channel MR-compatible EEG systems, respectively, and receive only array head coils. Data were collected in 11 healthy controls (3 males, age range 18–70 years) and 13 patients with epilepsy (8 males, age range 21–67 years). Three of the healthy controls were scanned with the 256-channel EEG system, the other subjects were scanned with the 64-channel EEG system. Scalp surface temperature, SNR in occipital cortex and head movement were measured with and without the EEG cap. The degree of artifacts and the ability to identify background activity was assessed by visual analysis by a trained expert in the 64 channel EEG data (7 healthy controls, 13 patients). Results RF induced heating at the surface of the EEG electrodes during a 30-minute scan period with stable temperature prior to scanning did not exceed 1.0° C with either EEG system and any of the pulse sequences used in this study. There was no significant decrease in cortical SNR due to the presence of the EEG cap (p > 0.05). No significant differences in the visually analyzed EEG data quality were found between EEG recorded during high-speed fMRI and during conventional EPI (p = 0.78). Residual ballistocardiographic artifacts resulted in 58% of EEG data being rated as poor quality. Conclusion This study demonstrates that high-density EEG can be safely implemented in conjunction with high-speed fMRI and that high-speed fMRI does not adversely affect EEG data quality. However, the deterioration of the EEG quality due to residual ballistocardiographic artifacts remains a significant constraint for routine clinical applications of concurrent EEG-fMRI. PMID:28552957

Age-Dependency of Location of Epileptic Foci in "Continuous Spike-and-Waves during Sleep": A Parallel to the Posterior-Anterior Trajectory of Slow Wave Activity.

PubMed

Bölsterli Heinzle, Bigna Katrin; Bast, Thomas; Critelli, Hanne; Huber, Reto; Schmitt, Bernhard

2017-02-01

Epileptic encephalopathy with continuous spike-and-waves during sleep (CSWS) occurs during childhood and is characterized by an activation of spike wave complexes during slow wave sleep. The location of epileptic foci is variable, as is etiology. A relationship between the epileptic focus and age has been shown in various focal epilepsies following a posterior-anterior trajectory, and a link to brain maturation has been proposed. We hypothesize that in CSWS, maximal spike wave activity, corresponding to the epileptic focus, is related to age and shows a posterior-anterior evolution. In a retrospective cross-sectional study on CSWS (22 EEGs of 22 patients aged 3.1–13.5 years), the location of the epileptic focus is related to age and follows a posterior-anterior course. Younger patients are more likely to have posterior foci than older ones. We propose that the posterior-anterior trajectory of maximal spike waves in CSWS might reflect maturational changes of maximal expression of sleep slow waves, which follow a comparable course. Epileptic spike waves, that is, “hyper-synchronized slow waves” may occur at the place where the highest and therefore most synchronized slow waves meet brain tissue with an increased susceptibility to synchronization. Georg Thieme Verlag KG Stuttgart · New York.

Absolute spike frequency as a predictor of surgical outcome in temporal lobe epilepsy.

PubMed

Ngo, Ly; Sperling, Michael R; Skidmore, Christopher; Mintzer, Scott; Nei, Maromi

2017-04-01

Frequent interictal epileptiform abnormalities may correlate with poor prognosis after temporal lobe resection for refractory epilepsy. To date, studies have focused on limited resections such as selective amygdalohippocampectomy and apical temporal lobectomy without hippocampectomy. However, it is unclear whether the frequency of spikes predicts outcome after standard anterior temporal lobectomy. Preoperative scalp video-EEG monitoring data from patients who subsequently underwent anterior temporal lobectomy over a three year period and were followed for at least one year were reviewed for the frequency of interictal epileptiform abnormalities. Surgical outcome for those patients with frequent spikes (>60/h) was compared with those with less frequent spikes. Additionally, spike frequency was evaluated as a continuous variable and correlated with outcome to determine if increased spike frequency correlated with worse outcome, as assessed by modified Engel Class outcome. Forty-seven patients (18 men, 29 women; mean age 40 years at surgery) were included. Forty-six patients had standard anterior temporal lobectomy (24 right, 22 left) and one had a modified left temporal lobectomy. There was no significant difference in seizure outcome between those with frequent (57% Class I) vs. those with less frequent (58% Class I) spikes. Increased spike frequency did not correlate with worse outcome. Greater than 20 complex partial seizures/month and generalized tonic-clonic seizures within one year of surgery correlated with worse outcome. This study suggests that absolute spike frequency does not predict seizure outcome after anterior temporal lobectomy unlike in selective procedures, and should not be used as a prognostic factor in this population. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Recovering TMS-evoked EEG responses masked by muscle artifacts.

PubMed

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

2016-10-01

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

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

PubMed Central

Stenroos, Matti; Hauk, Olaf

2013-01-01

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

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

PubMed Central

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

2014-01-01

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

Altered brain functional connectivity induced by physical exercise may improve neuropsychological functions in patients with benign epilepsy.

PubMed

Koirala, Gyan Raj; Lee, Dongpyo; Eom, Soyong; Kim, Nam-Young; Kim, Heung Dong

2017-11-01

The objective of this study was to elucidate alteration in functional connectivity (FC) in patients with benign epilepsy with centrotemporal spikes (BECTS) as induced by physical exercise therapy and their correlation to the neuropsychological (NP) functions. We analyzed 115 artifact- and spike-free 2-second epochs extracted from resting state EEG recordings before and after 5weeks of physical exercise in eight patients with BECTS. The exact Low Resolution Electromagnetic Tomography (eLORETA) was used for source reconstruction. We evaluated the cortical current source density (CSD) power across five different frequency bands (delta, theta, alpha, beta, and gamma). Altered FC between 34 regions of interests (ROIs) was then examined using lagged phase synchronization (LPS) method. We further investigated the correlation between the altered FC measures and the changes in NP test scores. We observed changes in CSD power following the exercise for all frequency bands and statistically significant increases in the right temporal region for the alpha band. There were a number of altered FC between the cortical ROIs in all frequency bands of interest. Furthermore, significant correlations were observed between FC measures and NP test scores at theta and alpha bands. The increased localization power at alpha band may be an indication of the positive impact of exercise in patients with BECTS. Frequency band-specific alterations in FC among cortical regions were associated with the modulation of cognitive and NP functions. The significant correlation between FC and NP tests suggests that physical exercise may mitigate the severity of BECTS, thereby enhancing NP function. Copyright © 2017 Elsevier Inc. All rights reserved.

A study of 43 patients with panayiotopoulos syndrome, a common and benign childhood seizure susceptibility.

PubMed

Lada, Christina; Skiadas, Konstantinos; Theodorou, Virginia; Loli, Nomiki; Covanis, Athanasios

2003-01-01

To determine prevalence, clinical, EEG features, and prognosis of Panayiotopoulos syndrome and to examine the proposition that clinical manifestations are more important than EEG findings. We analyzed retrospectively the clinical and EEG records of 1,340 children with one or more focal seizures seen in the last 18 years, supplemented with a prospective study from 1998. Panayiotopoulos syndrome was defined by clinical criteria, mainly ictal emesis, irrespective of EEG findings. We analyzed 43 of 90 patients with Panayiotopoulos syndrome who were seizure free >2 years. Girls predominated. Mean age at first seizure was 5 years. Seizures consisted mainly of autonomic manifestations; ictal emesis was often the first symptom, culminating in vomiting in 86%. Of nonautonomic manifestations, lateral eye deviation was the most common; visual symptoms were exceptional. Impairment of consciousness ensued in all seizures, half of which ended with hemi or generalized convulsions. Nearly 46.5% of cases had at least one seizure >30 min, constituting autonomic status epilepticus. Seizures during sleep (84%) were more common than those in wakefulness. EEG showed occipital spikes in 29 patients. Of the other 14 cases, five had extraoccipital abnormalities or brief generalized discharges, and nine had normal awake and sleep EEG. Prognosis was excellent. All 43 children have been free of seizures for > or =2 years, 53% having a single seizure, and 47%, an average two to three seizures. Panayiotopoulos syndrome is common and needs wider recognition. EEG shows occipital or extraoccipital abnormalities, is normal in one third of patients, and does not determine clinical manifestations or prognosis, which is excellent despite the high prevalence of lengthy seizures.

Variable outcome for epilepsy after neonatal hypoglycaemia.

PubMed

Fong, Choong Yi; Harvey, A Simon

2014-11-01

To evaluate the electroclinical features of epilepsy secondary to neonatal hypoglycaemia. This was a retrospective study of children who had seizures beyond infancy after neonatal hypoglycaemia treated at The Royal Children's Hospital, Melbourne between 1996 and 2012. Patients with perinatal asphyxia were excluded. Clinical details were obtained from medical records. Digital electroencephalography (EEG) and brain magnetic resonance imaging (MRI) were reviewed. Eleven patients met the inclusion criteria (six males, five females; mean age 10y 5mo, range 4-18y at the time of review). Age at seizure onset ranged from 4 months to 5 years. Seizures were focal occipital in nine and generalized tonic in two patients. MRI showed gliosis with or without cortical atrophy in the occipital lobe with or without parietal lobe in all. Predominant EEG findings were stereotyped occipital sharp-slow discharges in five, polymorphic occipital spike-wave or paroxysmal fast activity in three, and generalized slow spike-wave and fast activity in two. Seizures were infrequent or remitted in six of the nine children with focal occipital seizures, and frequent and refractory in both children with generalized seizures. Despite the common antecedent and bilateral occipital lobe injury, the seizure manifestations and course of epilepsy after neonatal hypoglycaemia were variable, with mild occipital, refractory occipital, and symptomatic generalized epilepsy recognized. © 2014 Mac Keith Press.

Does arousal interfere with operant conditioning of spike-wave discharges in genetic epileptic rats?

PubMed

Osterhagen, Lasse; Breteler, Marinus; van Luijtelaar, Gilles

2010-06-01

One of the ways in which brain computer interfaces can be used is neurofeedback (NF). Subjects use their brain activation to control an external device, and with this technique it is also possible to learn to control aspects of the brain activity by operant conditioning. Beneficial effects of NF training on seizure occurrence have been described in epileptic patients. Little research has been done about differentiating NF effectiveness by type of epilepsy, particularly, whether idiopathic generalized seizures are susceptible to NF. In this experiment, seizures that manifest themselves as spike-wave discharges (SWDs) in the EEG were reinforced during 10 sessions in 6 rats of the WAG/Rij strain, an animal model for absence epilepsy. EEG's were recorded before and after the training sessions. Reinforcing SWDs let to decreased SWD occurrences during training; however, the changes during training were not persistent in the post-training sessions. Because behavioural states are known to have an influence on the occurrence of SWDs, it is proposed that the reinforcement situation increased arousal which resulted in fewer SWDs. Additional tests supported this hypothesis. The outcomes have implications for the possibility to train SWDs with operant learning techniques. Copyright (c) 2010 Elsevier B.V. All rights reserved.

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

A study on epileptic negative myoclonus in atypical benign partial epilepsy of childhood.

PubMed

Yang, Zhixian; Liu, Xiaoyan; Qin, Jiong; Zhang, Yuehua; Bao, Xinhua; Chang, Xingzhi; Wang, Shuang; Wu, Ye; Xiong, Hui

2009-04-01

To investigate the clinical and neurophysiological characteristics, particularly therapeutic considerations, of epileptic negative myoclonus (ENM) in atypical benign partial epilepsy (ABPE) of childhood. From 1998 to 2006, 14/242 patients with benign children epilepsy with centrotemporal spikes (BECTS) were diagnosed as having ABPE with ENM. In all 14 patients, we performed video-EEG monitoring along with tests with the patient's arms outstretched; 6/14 patients were also simultaneously underwent surface electromyogram (EMG). ENM manifestations, electrophysiological features, and responses to antiepileptic drugs were analyzed. In all cases, ENM developed after the onset of epilepsy and during antiepileptic drug therapy, and the appearance of ENM were corresponding to EEG findings of high-amplitude spikes followed by a slow wave in the contralateral motor areas with secondary generalization. This was further confirmed by time-locked silent EMG. During ENM occurrence or recurrence, habitual seizures and interictal discharges were exaggerated. In some patients, the changes in antiepileptic drug regimens in relation to ENM appearance included add-on therapy with carbamazepine, oxcarbazepine, and phenobarbital or withdrawal of valproate. ENM was controlled in most cases by administration of various combinations of valproate, clonazepam, and corticosteroids. The incidence of ENM or ABPE in our center was approximately 5.79%. A combination of video-EEG monitoring with the patient's arms outstretched and EMG is essential to identify ENM. The aggravation of habitual seizures and interictal discharges indicate ENM. Some antiepileptic drugs, such as carbamazepine, oxcarbazepine, and phenobarbital, may be related to ENM occurrence during spontaneous aggravation of ABPE. Various combinations of valproate, benzodiazepines, and corticosteroids are relatively effective for treating ENM that occurs in ABPE.

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

PubMed

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

2017-08-15

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

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

PubMed Central

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

2018-01-01

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

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.

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

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2016-08-01

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

Randomized open-label trial of dextromethorphan in Rett syndrome.

PubMed

Smith-Hicks, Constance L; Gupta, Siddharth; Ewen, Joshua B; Hong, Manisha; Kratz, Lisa; Kelley, Richard; Tierney, Elaine; Vaurio, Rebecca; Bibat, Genila; Sanyal, Abanti; Yenokyan, Gayane; Brereton, Nga; Johnston, Michael V; Naidu, Sakkubai

2017-10-17

To determine safety and perform a preliminary assessment of dose-dependent efficacy of dextromethorphan in normalizing electrographic spikes, clinical seizures, and behavioral and cognitive functions in girls with Rett syndrome. We used a prospective randomized, open-label trial in fast metabolizers of dextromethorphan to examine the effect of dextromethorphan on core clinical features of Rett syndrome. Interictal spike activity and clinical seizures were determined using EEG and parent reporting. Cognitive data were obtained using the Mullen Scales of Early Learning and Vineland Adaptive Behavior Scales, while behavioral data were obtained from parent-completed checklists, the Aberrant Behavior Checklist-Community Version, and the Screen for Social Interaction. Anthropometric data were obtained according to the National Health and Nutrition Examination Survey. The Rett Syndrome Severity Scale provided a clinical global impression of the effect of dextromethorphan on clinical severity. Dextromethorphan is safe for use in 3- to 15-year-old girls with Rett syndrome. Thirty-five girls were treated with 1 of 3 doses of dextromethorphan over a period of 6 months. Statistically significant dose-dependent improvements were seen in clinical seizures, receptive language, and behavioral hyperactivity. There was no significant improvement in global clinical severity as measured by the Rett Syndrome Severity Scale. Dextromethorphan is a potent noncompetitive antagonist of the NMDA receptor channel that is safe for use in young girls with Rett syndrome. Preliminary evidence suggests that dextromethorphan may improve some core features of Rett syndrome. This study provides Class IV evidence that dextromethorphan at various doses does not change EEG spike counts over 6 months, though precision was limited to exclude an important effect. © 2017 American Academy of Neurology.

Long term effects on epileptiform activity with vagus nerve stimulation in children.

PubMed

Hallböök, Tove; Lundgren, Johan; Blennow, Gösta; Strömblad, Lars-Göran; Rosén, Ingmar

2005-12-01

We report long-term effects of vagus nerve stimulation (VNS) on epileptiform activity in 15 children, and how these changes are related to activity stage and to clinical effects on seizure reduction, seizure severity (NHS3) and quality of life (QOL). Initially, and after 3 and 9 months of VNS-treatment, 15 children were investigated with 24 h ambulatory EEG monitoring for spike detection. The number of interictal epileptiform discharges (IEDs) and the inter spike intervals (ISIs) were analysed during 2 h in the awake state, and 1h of rapid eye movement (REM)-, spindle- and delta-sleep, respectively. Total number and duration of electrographic seizure episodes were also analysed. At 9 months the total number of IEDs was significantly reduced (p=0.04). There was a tendency of reduction in all activity stages, and significantly so in delta-sleep (p=0.008). Total electrographic seizure number was significantly reduced in the 24 h EEG at 3 and 9 months (p=0.03, 0.05). There was a significant concordance in direction of changes in epileptiform activity and electrographic seizures at 9 months (p=0.04). Concordance in direction of changes was seen in 9 of 15 children between clinical seizures and IED (p>0.3), in 10 of 15 children between QOL and IED (p=0.3) and in 8 of 15 children between NHS3 and IED (p>0.3). There was no direct correlation between the extent of improvement in these clinical data and the degree of spike reduction. This study shows that VNS reduces IEDs especially in REM and delta sleep, as well as the number of electrographic seizures. It also shows a concordance between reduction in IEDs and electrographic seizures.

Development of spike-wave seizures in C3H/HeJ mice

PubMed Central

Ellens, Damien J.; Hong, Ellie; Giblin, Kathryn; Singleton, Matthew J.; Bashyal, Chhitij; Englot, Dario J.; Mishra, Asht M.; Blumenfeld, Hal

2012-01-01

Summary C3H/HeJ mice have been reported to have relatively early onset of spike-wave discharges (SWD), and a defective AMPA receptor subunit Gria4 as the genetic cause. We investigated the time course of SWD development through serial EEG recordings in C3H/HeJ mice to better characterize this model. We found that at immature postnatal ages of 5–15 days, rare SWD-like events were observed at an average rate of 3 per hour, and with relatively broad spikes, irregular rhythm, slow frequency (5–6 Hz), and short duration (mean 1.75 s). This was followed by a transitional period of increasing SWD incidence, which then stabilized in mature animals at age 26–62 days, with SWD at an average rate of 45 per hour, narrower spike morphology, regular rhythm, higher frequency (7–8 Hz), and longer duration (mean 3.40 s). This sequence of maturational changes in SWD development suggests that effects of early intervention could be tested in C3H/HeJ mice over the course of a few weeks, rather than a few months as in rats, greatly facilitating future research on anti-epileptogenesis. PMID:19409755

Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks

PubMed Central

Stepp, Nigel; Plenz, Dietmar; Srinivasa, Narayan

2015-01-01

During rest, the mammalian cortex displays spontaneous neural activity. Spiking of single neurons during rest has been described as irregular and asynchronous. In contrast, recent in vivo and in vitro population measures of spontaneous activity, using the LFP, EEG, MEG or fMRI suggest that the default state of the cortex is critical, manifested by spontaneous, scale-invariant, cascades of activity known as neuronal avalanches. Criticality keeps a network poised for optimal information processing, but this view seems to be difficult to reconcile with apparently irregular single neuron spiking. Here, we simulate a 10,000 neuron, deterministic, plastic network of spiking neurons. We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic, i.e. self-sustained, asynchronous spiking. Brief external perturbations lead to adaptive, long-term modification of intrinsic network connectivity through long-term excitatory plasticity, whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state. The critical state is characterized by a branching parameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0.5 and 1. PMID:25590427

A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies.

PubMed

Puce, Aina; Hämäläinen, Matti S

2017-05-31

Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed.

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.

Enabling computer decisions based on EEG input.

PubMed

Culpepper, Benjamin J; Keller, Robert M

2003-12-01

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

Enabling computer decisions based on EEG input

NASA Technical Reports Server (NTRS)

Culpepper, Benjamin J.; Keller, Robert M.

2003-01-01

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

Monitoring alert and drowsy states by modeling EEG source nonstationarity

NASA Astrophysics Data System (ADS)

Hsu, Sheng-Hsiou; Jung, Tzyy-Ping

2017-10-01

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

Electroencephalographic features of benign adult familial myoclonic epilepsy.

PubMed

Toyota, Tomoko; Akamatsu, Naoki; Tanaka, Akihiro; Tsuji, Sadatoshi; Uozumi, Takenori

2014-02-01

To investigate electroencephalographic (EEG) features of benign adult familial myoclonic epilepsy (BAFME). We reviewed interictal EEG features in patients with BAFME treated between April 2005 and November 2012 at a tertiary referral center. The diagnostic criteria for BAFME were the presence of infrequent generalized tonic-clonic seizures, myoclonus or myoclonic seizures, and autosomal dominant inheritance. Interictal EEG findings of epilepsy with generalized tonic-clonic seizure only (EGTCS) were reviewed for comparison. We randomly selected 10 generalized spike/polyspike and wave complexes (GSW) for each BAFME patient and measured the duration of them. Photic stimulation and hyperventilation were performed in all. Nineteen (eight men, 11 women) patients with BAFME were included in this study. The mean frequency of GSW was 4.3±1.0Hz (mean±SD, n=14) in BAFME and 3.2±0.8Hz (n=10) in EGTCS. There was a statistically significant difference (p=0.008) between the two. Photoparoxysmal responses (PPR) were noted in 18 (95%) patients with BAFME but 1 (10%) with EGTCS. Faster frequency of GSW, compared with that in EGTCS, accompanied by PPR may be characteristic EEG features of BAFME. These findings may lead the diagnosis of BAFME. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Information-Theoretical Analysis of EEG Microstate Sequences in Python.

PubMed

von Wegner, Frederic; Laufs, Helmut

2018-01-01

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

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

PubMed

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

2015-01-01

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

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

PubMed Central

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

2015-01-01

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

Enhanced Burst-Suppression and Disruption of Local Field Potential Synchrony in a Mouse Model of Focal Cortical Dysplasia Exhibiting Spike-Wave Seizures.

PubMed

Williams, Anthony J; Zhou, Chen; Sun, Qian-Quan

2016-01-01

Focal cortical dysplasias (FCDs) are a common cause of brain seizures and are often associated with intractable epilepsy. Here we evaluated aberrant brain neurophysiology in an in vivo mouse model of FCD induced by neonatal freeze lesions (FLs) to the right cortical hemisphere (near S1). Linear multi-electrode arrays were used to record extracellular potentials from cortical and subcortical brain regions near the FL in anesthetized mice (5-13 months old) followed by 24 h cortical electroencephalogram (EEG) recordings. Results indicated that FL animals exhibit a high prevalence of spontaneous spike-wave discharges (SWDs), predominately during sleep (EEG), and an increase in the incidence of hyper-excitable burst/suppression activity under general anesthesia (extracellular recordings, 0.5%-3.0% isoflurane). Brief periods of burst activity in the local field potential (LFP) typically presented as an arrhythmic pattern of increased theta-alpha spectral peaks (4-12 Hz) on a background of low-amplitude delta activity (1-4 Hz), were associated with an increase in spontaneous spiking of cortical neurons, and were highly synchronized in control animals across recording sites in both cortical and subcortical layers (average cross-correlation values ranging from +0.73 to +1.0) with minimal phase shift between electrodes. However, in FL animals, cortical vs. subcortical burst activity was strongly out of phase with significantly lower cross-correlation values compared to controls (average values of -0.1 to +0.5, P < 0.05 between groups). In particular, a marked reduction in the level of synchronous burst activity was observed, the closer the recording electrodes were to the malformation (Pearson's Correlation = 0.525, P < 0.05). In a subset of FL animals (3/9), burst activity also included a spike or spike-wave pattern similar to the SWDs observed in unanesthetized animals. In summary, neonatal FLs increased the hyperexcitable pattern of burst activity induced by anesthesia and disrupted field potential synchrony between cortical and subcortical brain regions near the site of the cortical malformation. Monitoring the altered electrophysiology of burst activity under general anesthesia with multi-dimensional micro-electrode arrays may serve to define distinct neurophysiological biomarkers of epileptogenesis in human brain and improve techniques for surgical resection of epileptogenic malformed brain tissue.

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

PubMed

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

2016-10-01

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

Evaluation of nonlinear properties of epileptic activity using largest Lyapunov exponent

NASA Astrophysics Data System (ADS)

Medvedeva, Tatiana M.; Lüttjohann, Annika; van Luijtelaar, Gilles; Sysoev, Ilya V.

2016-04-01

Absence seizures are known to be highly non-linear large amplitude oscillations with a well pronounced main time scale. Whilst the appearance of the main frequency is usually considered as a transition from noisy complex dynamics of baseline EEG to more regular absence activity, the dynamical properties of this type of epileptiformic activity in genetic absence models was not studied precisely. Here, the estimation of the largest Lyapunov exponent from intracranial EEGs of 10 WAG/Rij rats (genetic model of absence epilepsy) was performed. Fragments of 10 seizures and 10 episodes of on-going EEG each of 4 s length were used for each animal, 3 cortical and 2 thalamic channels were analysed. The method adapted for short noisy data was implemented. The positive values of the largest Lyapunov exponent were found as for baseline as for spike wave discharges (SWDs), with values for SWDs being significantly less than for on-going activity. Current findings may indicate that SWD is a chaotic process with a well pronounced main timescale rather than a periodic regime. Also, the absence activity was shown to be less chaotic than the baseline one.

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

PubMed Central

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

2011-01-01

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

A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies

PubMed Central

Puce, Aina; Hämäläinen, Matti S.

2017-01-01

Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed. PMID:28561761

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

PubMed Central

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

2008-01-01

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

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

PubMed Central

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

2018-01-01

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

Acute CT perfusion changes in seizure patients presenting to the emergency department with stroke-like symptoms: correlation with clinical and electroencephalography findings.

PubMed

Payabvash, S; Oswood, M C; Truwit, C L; McKinney, A M

2015-10-01

To determine acute computed tomography perfusion (CTP) changes in seizure patients presenting with stroke-like symptoms and to correlate those changes with clinical presentation and electroencephalography (EEG). The medical records of all patients who presented to the emergency department with acute stroke-like symptoms and underwent CTP (n=1085) over a 5.5-year period were reviewed. Patients were included who had primary seizure as the final diagnosis, and underwent CTP within 3 hours of symptom onset. A subset of patients had a follow-up EEG within 7 days. The perfusion changes and EEG findings were compared between different clinical presentations. Eighteen of 1085 patients (1.7%) who underwent CTP following an acute stroke-like presentation were included. The abnormality on CTP was usually focal, unilateral hyperperfusion - increased relative cerebral blood flow (rCBF) and volume (rCBV) (n=14/18), which most often affected the temporal lobe. Those patients who presented with a motor or speech deficit (n=12) had a higher temporal lobe rCBV, and rCBF, and lower relative mean transit time (rMTT) compared to those with non-focal neurological deficit at presentation. Early EEG was available in 13 patients; a sharp-spike epileptiform EEG discharge pattern (n=5) was associated with higher temporal lobe ipsilateral rCBF and rCBV, and lower rMTT on admission CTP examination. Seizure patients who present with a unilateral motor or speech deficit most commonly have contralateral hyperperfusion in the corresponding eloquent brain regions on the acute-stage CTP examination. In such patients, epileptiform discharges on the early follow-up EEG are associated with ipsilateral hyperperfusion on the admission CTP. Copyright © 2015 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Clinical and electrographic features of sunflower syndrome.

PubMed

Baumer, Fiona M; Porter, Brenda E

2018-05-01

Sunflower Syndrome describes reflex seizures - typically eyelid myoclonia with or without absence seizures - triggered when patients wave their hands in front of the sun. While valproate has been recognized as the best treatment for photosensitive epilepsy, many clinicians now initially treat with newer medications; the efficacy of these medications in Sunflower Syndrome has not been investigated. We reviewed all cases of Sunflower Syndrome seen at our institution over 15 years to describe the clinical course, electroencephalogram (EEG), and treatment response in these patients. Search of the electronic medical record and EEG database, as well as survey of epilepsy providers at our institution, yielded 13 cases of Sunflower Syndrome between 2002 and 2017. We reviewed the records and EEG tracings. Patients were mostly young females, with an average age of onset of 5.5 years. Seven had intellectual, attentional or academic problems. Self-induced seizures were predominantly eyelid myoclonia ± absences and 6 subjects also had spontaneous seizures. EEG demonstrated a normal background with 3-4 Hz spike waves ± polyspike waves as well as a photoparoxysmal response. Based on both clinical and EEG response, valproate was the most effective treatment for reducing or eliminating seizures and improving the EEG; 9 patients tried valproate and 66% had significant improvement or resolution of seizures. None of the nine patients on levetiracetam or seven patients on lamotrigine monotherapy achieved seizure control, though three patients had improvement with polypharmacy. Valproate monotherapy continues to be the most effective treatment for Sunflower Syndrome and should be considered early. For patients who cannot tolerate valproate, higher doses of lamotrigine or polypharmacy should be considered. Levetiracetam monotherapy, even at high doses, is unlikely to be effective. Copyright © 2018 Elsevier B.V. All rights reserved.

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

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

PubMed

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

2004-08-01

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

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

PubMed Central

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

2016-01-01

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

Mechanism and Therapy for the Shared Susceptibility to Migraine and Epilepsy After Traumatic Brain Injury

DTIC Science & Technology

2014-10-01

developed status epilepticus (Figure 1) which continued for 3 days resulting in the animal’s death. Spontaneous recurrent seizures were recorded in 3...recording of a CCI-injured mouse in status epilepticus . Upper trace is an EEG recording of 4 h of status epilepticus while the lower traces...seizure (b) and the continuous spiking evident of status epilepticus (c). * represents movement artifact from the seizure motor activity. Although we

Interictal epileptic discharge correlates with global and frontal cognitive dysfunction in temporal lobe epilepsy.

PubMed

Dinkelacker, Vera; Xin, Xu; Baulac, Michel; Samson, Séverine; Dupont, Sophie

2016-09-01

Temporal lobe epilepsy (TLE) with hippocampal sclerosis has widespread effects on structural and functional connectivity and often entails cognitive dysfunction. EEG is mandatory to disentangle interactions in epileptic and physiological networks which underlie these cognitive comorbidities. Here, we examined how interictal epileptic discharges (IEDs) affect cognitive performance. Thirty-four patients (right TLE=17, left TLE=17) were examined with 24-hour video-EEG and a battery of neuropsychological tests to measure intelligence quotient and separate frontal and temporal lobe functions. Hippocampal segmentation of high-resolution T1-weighted imaging was performed with FreeSurfer. Partial correlations were used to compare the number and distribution of clinical interictal spikes and sharp waves with data from imagery and psychological tests. The number of IEDs was negatively correlated with executive functions, including verbal fluency and intelligence quotient (IQ). Interictal epileptic discharge affected cognitive function in patients with left and right TLE differentially, with verbal fluency strongly related to temporofrontal spiking. In contrast, IEDs had no clear effects on memory functions after corrections with partial correlations for age, age at disease onset, disease duration, and hippocampal volume. In patients with TLE of long duration, IED occurrence was strongly related to cognitive deficits, most pronounced for frontal lobe function. These data suggest that IEDs reflect dysfunctional brain circuitry and may serve as an independent biomarker for cognitive comorbidity. Copyright © 2016. Published by Elsevier Inc.

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.

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

PubMed

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

2016-01-01

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

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

PubMed Central

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

2016-01-01

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

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

PubMed

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

2017-02-01

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

Inferring multi-scale neural mechanisms with brain network modelling

PubMed Central

Schirner, Michael; McIntosh, Anthony Randal; Jirsa, Viktor; Deco, Gustavo

2018-01-01

The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies. PMID:29308767

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

PubMed Central

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

2017-01-01

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

Incorporating an ERP Project into Undergraduate Instruction

PubMed Central

Nyhus, Erika; Curtis, Nancy

2016-01-01

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

The inverse electroencephalography pipeline

NASA Astrophysics Data System (ADS)

Weinstein, David Michael

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

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.

Seizures versus dystonia in encephalopathic crisis of glutaric aciduria type I.

PubMed

Cerisola, Alfredo; Campistol, Jaume; Pérez-Dueñas, Belén; Poo, Pilar; Pineda, Mercé; García-Cazorla, Angels; Sanmartí, Francesc X; Ribes, Antonia; Vilaseca, María Antonia

2009-06-01

In more than two thirds of cases, glutaric aciduria type I begins in the first 3 years of life with an acute encephalopathic crisis with hypotonia or generalized rigidity, neurologic depression, irritability, seizures, and dystonia. The clinical histories were reviewed for 13 glutaric aciduria type I patients (9 male, 4 female; mean age, 8.7 months; range, 3-15 months) with encephalopathic crisis seen at Sant Joan de Déu Hospital, to describe the clinical features and the initial electroencephalographic (EEG) findings. Twelve of the patients (92%) had paroxysmal episodes at onset. Other clinical features included irritability (12/13), neurologic depression (11/13), and hypotonia (7/13). All patients evolved to dystonic tetraparesis. Thirty-five EEGs were recorded in the acute stage and during the first year of follow-up. Spike discharges on EEG were observed in only 2 of the 13 patients, and 8 had slow background activity. No patient developed seizures during follow-up. Seizures may be part of the symptomatology at the onset of glutaric aciduria type I, but most paroxysmal movements appear to be dystonic episodes. This hypothesis is supported by four facts: seizures do not occur after dystonic tetraparesis is noticed, EEG paroxysms are infrequent in the acute stage, antiepileptic drugs are not needed in the long term, and epilepsy is rare in the follow-up.

EEG data reduction by means of autoregressive representation and discriminant analysis procedures.

PubMed

Blinowska, K J; Czerwosz, L T; Drabik, W; Franaszczuk, P J; Ekiert, H

1981-06-01

A program for automatic evaluation of EEG spectra, providing considerable reduction of data, was devised. Artefacts were eliminated in two steps: first, the longer duration eye movement artefacts were removed by a fast and simple 'moving integral' methods, then occasional spikes were identified by means of a detection function defined in the formalism of the autoregressive (AR) model. The evaluation of power spectra was performed by means of an FFT and autoregressive representation, which made possible the comparison of both methods. The spectra obtained by means of the AR model had much smaller statistical fluctuations and better resolution, enabling us to follow the time changes of the EEG pattern. Another advantage of the autoregressive approach was the parametric description of the signal. This last property appeared to be essential in distinguishing the changes in the EEG pattern. In a drug study the application of the coefficients of the AR model as input parameters in the discriminant analysis, instead of arbitrary chosen frequency bands, brought a significant improvement in distinguishing the effects of the medication. The favourable properties of the AR model are connected with the fact that the above approach fulfils the maximum entropy principle. This means that the method describes in a maximally consistent way the available information and is free from additional assumptions, which is not the case for the FFT estimate.

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

PubMed

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

2017-02-15

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

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

PubMed

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

2018-06-01

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

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.

Reprint of “Non-causal spike filtering improves decoding of movement intention for intracortical BCIs”☆

PubMed Central

Masse, Nicolas Y.; Jarosiewicz, Beata; Simeral, John D.; Bacher, Daniel; Stavisky, Sergey D.; Cash, Sydney S.; Oakley, Erin M.; Berhanu, Etsub; Eskandar, Emad; Friehs, Gerhard; Hochberg, Leigh R.; Donoghue, John P.

2015-01-01

Background Multiple types of neural signals are available for controlling assistive devices through brain–computer interfaces (BCIs). Intracortically recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on “sorting” action potentials. New method We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4 ms lag between recording and filtering neural signals. Results Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant’s intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study. PMID:25681017

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

PubMed

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

2009-04-01

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

EEG source localization: Sensor density and head surface coverage.

PubMed

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

2015-12-30

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

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.

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

Do solar decimetric spikes originate in coronal X-ray sources?

NASA Astrophysics Data System (ADS)

Battaglia, M.; Benz, A. O.

2009-06-01

Context: In the standard solar flare scenario, a large number of particles are accelerated in the corona. Nonthermal electrons emit both X-rays and radio waves. Thus, correlated signatures of the acceleration process are predicted at both wavelengths, coinciding either close to the footpoints of a magnetic loop or near the coronal X-ray source. Aims: We attempt to study the spatial connection between coronal X-ray emission and decimetric radio spikes to determine the site and geometry of the acceleration process. Methods: The positions of radio-spike sources and coronal X-ray sources are determined and analyzed in a well-observed limb event. Radio spikes are identified in observations from the Phoenix-2 spectrometer. Data from the Nançay radioheliograph are used to determine the position of the radio spikes. RHESSI images in soft and hard X-ray wavelengths are used to determine the X-ray flare geometry. Those observations are complemented by images from GOES/SXI. Results: We find that the radio emission originates at altitudes much higher than the coronal X-ray source, having an offset from the coronal X-ray source amounting to 90´´ and to 113´´ and 131´´ from the two footpoints, averaged over time and frequency. Conclusions: Decimetric spikes do not originate from coronal X-ray flare sources contrary to previous expectations. However, the observations suggest a causal link between the coronal X-ray source, related to the major energy release site, and simultaneous activity in the higher corona.

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

NASA Astrophysics Data System (ADS)

Lindgren, Jussi T.

2018-02-01

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

EEG and MEG data analysis in SPM8.

PubMed

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

2011-01-01

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

EEG and MEG Data Analysis in SPM8

PubMed Central

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

2011-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG

PubMed Central

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

2015-01-01

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

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

PubMed

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

2016-03-01

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

Neuronal Networks during Burst Suppression as Revealed by Source Analysis

PubMed Central

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

2015-01-01

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

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.

Diffusion spectral imaging modules correlate with EEG LORETA neuroimaging modules.

PubMed

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

2012-05-01

The purpose of this study was to test the hypothesis that the highest temporal correlations between 3-dimensional EEG current source density corresponds to anatomical Modules of high synaptic connectivity. Eyes closed and eyes open EEG was recorded from 19 scalp locations with a linked ears reference from 71 subjects age 13-42 years. LORETA was computed from 1 to 30 Hz in 2,394 cortical gray matter voxels that were grouped into six anatomical Modules corresponding to the ROIs in the Hagmann et al.'s [2008] diffusion spectral imaging (DSI) study. All possible cross-correlations between voxels within a DSI Module were compared with the correlations between Modules. The Hagmann et al. [ 2008] Module correlation structure was replicated in the correlation structure of EEG three-dimensional current source density. EEG Temporal correlation between brain regions is related to synaptic density as measured by diffusion spectral imaging. Copyright © 2011 Wiley-Liss, Inc.

Neuromagnetic Evidence of Spatially Distributed Sources Underlying Epileptiform Spikes in the Human Brain

NASA Astrophysics Data System (ADS)

Barth, Daniel S.; Sutherling, William; Engle, Jerome; Beatty, Jackson

1984-01-01

Neuromagnetic measurements were performed on 17 subjects with focal seizure disorders. In all of the subjects, the interictal spike in the scalp electroencephalogram was associated with an orderly extracranial magnetic field pattern. In eight of these subjects, multiple current sources underlay the magnetic spike complex. The multiple sources within a given subject displayed a fixed chronological sequence of discharge, demonstrating a high degree of spatial and temporal organization within the interictal focus.

Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture.

PubMed

Gholami Doborjeh, Zohreh; Kasabov, Nikola; Gholami Doborjeh, Maryam; Sumich, Alexander

2018-06-11

Familiarity of marketing stimuli may affect consumer behaviour at a peri-perceptual processing level. The current study introduces a method for deep learning of electroencephalogram (EEG) data using a spiking neural network (SNN) approach that reveals the complexity of peri-perceptual processes of familiarity. The method is applied to data from 20 participants viewing familiar and unfamiliar logos. The results support the potential of SNN models as novel tools in the exploration of peri-perceptual mechanisms that respond differentially to familiar and unfamiliar stimuli. Specifically, the activation pattern of the time-locked response identified by the proposed SNN model at approximately 200 milliseconds post-stimulus suggests greater connectivity and more widespread dynamic spatio-temporal patterns for familiar than unfamiliar logos. The proposed SNN approach can be applied to study other peri-perceptual or perceptual brain processes in cognitive and computational neuroscience.

Intraoperative neurophysiological responses in epileptic patients submitted to hippocampal and thalamic deep brain stimulation.

PubMed

Cukiert, Arthur; Cukiert, Cristine Mella; Argentoni-Baldochi, Meire; Baise, Carla; Forster, Cássio Roberto; Mello, Valeria Antakli; Burattini, José Augusto; Lima, Alessandra Moura

2011-12-01

Deep brain stimulation (DBS) has been used in an increasing frequency for treatment of refractory epilepsy. Acute deep brain macrostimulation intraoperative findings were sparsely published in the literature. We report on our intraoperative macrostimulation findings during thalamic and hippocampal DBS implantation. Eighteen patients were studied. All patients underwent routine pre-operative evaluation that included clinical history, neurological examination, interictal and ictal EEG, high resolution 1.5T MRI and neuropsychological testing. Six patients with temporal lobe epilepsy were submitted to hippocampal DBS (Hip-DBS); 6 patients with focal epilepsy were submitted to anterior thalamic nucleus DBS (AN-DBS) and 6 patients with generalized epilepsy were submitted to centro-median thalamic nucleus DBS (CM-DBS). Age ranged from 9 to 40 years (11 males). All patients were submitted to bilateral quadripolar DBS electrode implantation in a single procedure, under general anesthesia, and intraoperative scalp EEG monitoring. Final electrode's position was checked postoperatively using volumetric CT scanning. Bipolar stimulation using the more proximal and distal electrodes was performed. Final standard stimulation parameters were 6Hz, 4V, 300μs (low frequency range: LF) or 130Hz, 4V, 300μs (high frequency range: HF). Bilateral recruiting response (RR) was obtained after unilateral stimulation in all patients submitted to AN and CM-DBS using LF stimulation. RR was widespread but prevailed over the fronto-temporal region bilaterally, and over the stimulated hemisphere. HF stimulation led to background slowing and a DC shift. The mean voltage for the appearance of RR was 4V (CM) and 3V (AN). CM and AN-DBS did not alter inter-ictal spiking frequency or morphology. RR obtained after LF Hip-DBS was restricted to the stimulated temporal lobe and no contralateral activation was noted. HF stimulation yielded no visually recognizable EEG modification. Mean intensity for initial appearance of RR was 3V. In 5 of the 6 patients submitted to Hip-DBS, an increase in inter-ictal spiking was noted unilaterally immediately after electrode insertion. Intraoperative LF stimulation did not modify temporal lobe spiking; on the other hand, HF was effective in abolishing inter-ictal spiking in 4 of the 6 patients studied. There was no immediate morbidity or mortality in this series. Macrostimulation might be used to confirm that the hardware was working properly. There was no typical RR derived from each studied thalamic nuclei after LF stimulation. On the other hand, absence of such RRs was highly suggestive of hardware malfunction or inadequate targeting. Thalamic-DBS (Th-DBS) RR was always bilateral after unilateral stimulation, although they somehow prevailed over the stimulated hemisphere. Contrary to Th-DBS, Hip-DBS gave rise to localized RR over the ipsolateral temporal neocortex, and absence of this response might very likely be related to inadequate targeting or hardware failure. Increased spiking was seen over temporal neocortex during hippocampal electrode insertion; this might point to the more epileptogenic hippocampal region in each individual patient. We did not notice any intraoperative response difference among patients with temporal lobe epilepsy with or without MTS. The relationship between these intraoperative findings and seizure outcome is not yet clear and should be further evaluated. 2011 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Regularized two-step brain activity reconstruction from spatiotemporal EEG data

NASA Astrophysics Data System (ADS)

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

2004-10-01

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

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

NASA Astrophysics Data System (ADS)

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

2005-10-01

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

Neurodevelopmental Correlates of Theory of Mind in Preschool Children

ERIC Educational Resources Information Center

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

2009-01-01

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

[Expressive language disorder and focal paroxysmal activity].

PubMed

Valdizán, José R; Rodríguez-Mena, Diego; Díaz-Sardi, Mauricio

2011-03-01

In cases of expressive language disorder (ELD), the child is unable to put his or her thoughts into words. Comorbidity is present with difficulties in repeating, imitating or naming. There are no problems with pronunciation, as occurs in phonological disorder, it may present before the age of three years and is crucial between four and seven years of age. Electroencephalogram (EEG) studies have been carried out not only in ELD, but also in clinical pictures where the language disorder was the main symptom or was associated to another neurodevelopmental pathology. We conducted a retrospective study involving a review of 100 patient records, with patients (25 girls and 75 boys) aged between two and six years old who had been diagnosed with ELD (according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revised) and were free of seizures and not receiving treatment. They were submitted to an EEG and received treatment with valproic acid if EEG findings were positive. Only six patients (males) presented localised spike-wave paroxysmal EEG activity in the frontotemporal region. This 6% is a percentage that is higher than the one found in the normal children's population (2%), but lower than the value indicated in the literature for language disorders, which ranges between 20% and 50%. These patients responded positively to the treatment and both expressive language and EEG findings improved. It is possible that in ELD without paroxysms there may be a dysfunction in the circuit made up of the motor cortex-neostriatum prior to grammatical learning, whereas if there are paroxysms then this would point to neuronal hyperactivity, perhaps associated to this dysfunction or not, in cortical areas. In our cases valproic acid, together with speech therapy, helped the children to recover their language abilities.

The convulsive and electroencephalographic changes produced by nonpeptidic delta-opioid agonists in rats: comparison with pentylenetetrazol.

PubMed

Jutkiewicz, Emily M; Baladi, Michelle G; Folk, John E; Rice, Kenner C; Woods, James H

2006-06-01

delta-Opioid agonists produce convulsions and antidepressant-like effects in rats. It has been suggested that the antidepressant-like effects are produced through a convulsant mechanism of action either through overt convulsions or nonconvulsive seizures. This study evaluated the convulsive and seizurogenic effects of nonpeptidic delta-opioid agonists at doses that previously were reported to produce antidepressant-like effects. In addition, delta-opioid agonist-induced electroencephalographic (EEG) and behavioral changes were compared with those produced by the chemical convulsant pentylenetetrazol (PTZ). For these studies, EEG changes were recorded using a telemetry system before and after injections of the delta-opioid agonists [(+)-4-[(alphaR)-alpha-[(2S,5R)-2,5-dimethyl-4-(2-propenyl)-1-piperazinyl]-(3-methoxyphenyl)methyl]-N,N-diethylbenz (SNC80) and [(+)-4-[alpha(R)-alpha-[(2S,5R)-2,5-dimethyl-4-(2-propenyl)-1-piperazinyl]-(3-hydroxyphenyl)methyl]-N,N-diethylbenzamide [(+)-BW373U86]. Acute administration of nonpeptidic delta-opioid agonists produced bilateral ictal and paroxysmal spike and/or sharp wave discharges. delta-Opioid agonists produced brief changes in EEG recordings, and tolerance rapidly developed to these effects; however, PTZ produced longer-lasting EEG changes that were exacerbated after repeated administration. Studies with antiepileptic drugs demonstrated that compounds used to treat absence epilepsy blocked the convulsive effects of nonpeptidic delta-opioid agonists. Overall, these data suggest that delta-opioid agonist-induced EEG changes are not required for the antidepressant-like effects of these compounds and that neural circuitry involved in absence epilepsy may be related to delta-opioid agonist-induced convulsions. In terms of therapeutic development, these data suggest that it may be possible to develop delta-opioid agonists devoid of convulsive properties.

Analysis of EEG-fMRI data in focal epilepsy based on automated spike classification and Signal Space Projection.

PubMed

Liston, Adam D; De Munck, Jan C; Hamandi, Khalid; Laufs, Helmut; Ossenblok, Pauly; Duncan, John S; Lemieux, Louis

2006-07-01

Simultaneous acquisition of EEG and fMRI data enables the investigation of the hemodynamic correlates of interictal epileptiform discharges (IEDs) during the resting state in patients with epilepsy. This paper addresses two issues: (1) the semi-automation of IED classification in statistical modelling for fMRI analysis and (2) the improvement of IED detection to increase experimental fMRI efficiency. For patients with multiple IED generators, sensitivity to IED-correlated BOLD signal changes can be improved when the fMRI analysis model distinguishes between IEDs of differing morphology and field. In an attempt to reduce the subjectivity of visual IED classification, we implemented a semi-automated system, based on the spatio-temporal clustering of EEG events. We illustrate the technique's usefulness using EEG-fMRI data from a subject with focal epilepsy in whom 202 IEDs were visually identified and then clustered semi-automatically into four clusters. Each cluster of IEDs was modelled separately for the purpose of fMRI analysis. This revealed IED-correlated BOLD activations in distinct regions corresponding to three different IED categories. In a second step, Signal Space Projection (SSP) was used to project the scalp EEG onto the dipoles corresponding to each IED cluster. This resulted in 123 previously unrecognised IEDs, the inclusion of which, in the General Linear Model (GLM), increased the experimental efficiency as reflected by significant BOLD activations. We have also shown that the detection of extra IEDs is robust in the face of fluctuations in the set of visually detected IEDs. We conclude that automated IED classification can result in more objective fMRI models of IEDs and significantly increased sensitivity.

Identifying stereotypic evolving micro-scale seizures (SEMS) in the hypoxic-ischemic EEG of the pre-term fetal sheep with a wavelet type-II fuzzy classifier.

PubMed

Abbasi, Hamid; Bennet, Laura; Gunn, Alistair J; Unsworth, Charles P

2016-08-01

Perinatal hypoxic-ischemic encephalopathy (HIE) around the time of birth due to lack of oxygen can lead to debilitating neurological conditions such as epilepsy and cerebral palsy. Experimental data have shown that brain injury evolves over time, but during the first 6-8 hours after HIE the brain has recovered oxidative metabolism in a latent phase, and brain injury is reversible. Treatments such as therapeutic cerebral hypothermia (brain cooling) are effective when started during the latent phase, and continued for several days. Effectiveness of hypothermia is lost if started after the latent phase. Post occlusion monitoring of particular micro-scale transients in the hypoxic-ischemic (HI) Electroencephalogram (EEG), from an asphyxiated fetal sheep model in utero, could provide precursory evidence to identify potential biomarkers of injury when brain damage is still treatable. In our studies, we have reported how it is possible to automatically detect HI EEG transients in the form of spikes and sharp waves during the latent phase of the HI EEG of the preterm fetal sheep. This paper describes how to identify stereotypic evolving micro-scale seizures (SEMS) which have a relatively abrupt onset and termination in a frequency range of 1.8-3Hz (Delta waves) superimposed on a suppressed EEG amplitude background post occlusion. This research demonstrates how a Wavelet Type-II Fuzzy Logic System (WT-Type-II-FLS) can be used to automatically identify subtle abnormal SEMS that occur during the latent phase with a preliminary average validation overall performance of 78.71%±6.63 over the 390 minutes of the latent phase, post insult, using in utero pre-term hypoxic fetal sheep models.

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

PubMed

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

2012-03-01

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

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.

Sparse EEG/MEG source estimation via a group lasso

PubMed Central

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

2017-01-01

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

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.

High density scalp EEG in frontal lobe epilepsy.

PubMed

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

2017-01-01

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

Propagating Neural Source Revealed by Doppler Shift of Population Spiking Frequency

PubMed Central

Zhang, Mingming; Shivacharan, Rajat S.; Chiang, Chia-Chu; Gonzalez-Reyes, Luis E.

2016-01-01

Electrical activity in the brain during normal and abnormal function is associated with propagating waves of various speeds and directions. It is unclear how both fast and slow traveling waves with sometime opposite directions can coexist in the same neural tissue. By recording population spikes simultaneously throughout the unfolded rodent hippocampus with a penetrating microelectrode array, we have shown that fast and slow waves are causally related, so a slowly moving neural source generates fast-propagating waves at ∼0.12 m/s. The source of the fast population spikes is limited in space and moving at ∼0.016 m/s based on both direct and Doppler measurements among 36 different spiking trains among eight different hippocampi. The fact that the source is itself moving can account for the surprising direction reversal of the wave. Therefore, these results indicate that a small neural focus can move and that this phenomenon could explain the apparent wave reflection at tissue edges or multiple foci observed at different locations in neural tissue. SIGNIFICANCE STATEMENT The use of novel techniques with an unfolded hippocampus and penetrating microelectrode array to record and analyze neural activity has revealed the existence of a source of neural signals that propagates throughout the hippocampus. The source itself is electrically silent, but its location can be inferred by building isochrone maps of population spikes that the source generates. The movement of the source can also be tracked by observing the Doppler frequency shift of these spikes. These results have general implications for how neural signals are generated and propagated in the hippocampus; moreover, they have important implications for the understanding of seizure generation and foci localization. PMID:27013678

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

PubMed Central

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

2018-01-01

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

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

PubMed

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

2017-08-01

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

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

PubMed

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

2016-09-01

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

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

PubMed

Gramfort, Alexandre; Kowalski, Matthieu; Hämäläinen, Matti

2012-04-07

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

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

PubMed Central

Gramfort, Alexandre; Kowalski, Matthieu; Hämäläinen, Matti

2012-01-01

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

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

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

PubMed

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

2016-06-01

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

A simple method for EEG guided transcranial electrical stimulation without models

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

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

PubMed

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

2006-01-01

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

Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods.

PubMed

Mejia Tobar, Alejandra; Hyoudou, Rikiya; Kita, Kahori; Nakamura, Tatsuhiro; Kambara, Hiroyuki; Ogata, Yousuke; Hanakawa, Takashi; Koike, Yasuharu; Yoshimura, Natsue

2017-01-01

The classification of ankle movements from non-invasive brain recordings can be applied to a brain-computer interface (BCI) to control exoskeletons, prosthesis, and functional electrical stimulators for the benefit of patients with walking impairments. In this research, ankle flexion and extension tasks at two force levels in both legs, were classified from cortical current sources estimated by a hierarchical variational Bayesian method, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The hierarchical prior for the current source estimation from EEG was obtained from activated brain areas and their intensities from an fMRI group (second-level) analysis. The fMRI group analysis was performed on regions of interest defined over the primary motor cortex, the supplementary motor area, and the somatosensory area, which are well-known to contribute to movement control. A sparse logistic regression method was applied for a nine-class classification (eight active tasks and a resting control task) obtaining a mean accuracy of 65.64% for time series of current sources, estimated from the EEG and the fMRI signals using a variational Bayesian method, and a mean accuracy of 22.19% for the classification of the pre-processed of EEG sensor signals, with a chance level of 11.11%. The higher classification accuracy of current sources, when compared to EEG classification accuracy, was attributed to the high number of sources and the different signal patterns obtained in the same vertex for different motor tasks. Since the inverse filter estimation for current sources can be done offline with the present method, the present method is applicable to real-time BCIs. Finally, due to the highly enhanced spatial distribution of current sources over the brain cortex, this method has the potential to identify activation patterns to design BCIs for the control of an affected limb in patients with stroke, or BCIs from motor imagery in patients with spinal cord injury.

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

PubMed

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

2017-01-01

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

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

PubMed Central

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

2017-01-01

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

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.

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

PubMed

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

2016-08-01

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

Magnetoencephalography signals are influenced by skull defects.

PubMed

Lau, S; Flemming, L; Haueisen, J

2014-08-01

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

Suppression of seizures based on the multi-coupled neural mass model.

PubMed

Cao, Yuzhen; Ren, Kaili; Su, Fei; Deng, Bin; Wei, Xile; Wang, Jiang

2015-10-01

Epilepsy is one of the most common serious neurological disorders, which affects approximately 1% of population in the world. In order to effectively control the seizures, we propose a novel control methodology, which combines the feedback linearization control (FLC) with the underlying mechanism of epilepsy, to achieve the suppression of seizures. The three coupled neural mass model is constructed to study the property of the electroencephalographs (EEGs). Meanwhile, with the model we research on the propagation of epileptiform waves and the synchronization of populations, which are taken as the foundation of our control method. Results show that the proposed approach not only yields excellent performances in clamping the pathological spiking patterns to the reference signals derived under the normal state but also achieves the normalization of the pathological parameter, where the parameters are estimated from EEGs with Unscented Kalman Filter. The specific contribution of this paper is to treat the epilepsy from its pathogenesis with the FLC, which provides critical theoretical basis for the clinical treatment of neurological disorders.

Clinical features of benign epilepsy of childhood with centrotemporal spikes in chinese children

PubMed Central

Liu, Meng-Jia; Su, Xiao-jun; MD, Xiu-Yu Shi; Wu, Ge-fei; Zhang, Yu-qin; Gao, Li; Wang, Wei; Liao, Jian-xiang; Wang, Hua; Mai, Jian-ning; Gao, Jing-yun; Shu, Xiao-mei; Huang, Shao-ping; Zhang, Li; Zou, Li-Ping

2017-01-01

Abstract This multicenter clinical trial was conducted to examine current practice of benign epilepsy with centrotemporal spikes and especially address the question that in what circumstances 1 antiepileptic drug (AED) should be preferred. Twenty-five medical centers participate in this clinical trial. The general information, clinical information, and treatment status were collected under the guidance of clinicians and then analyzed. Difference between different treatment groups was compared, and usefulness of the most commonly used AEDs was evaluated. A total of 1817 subjects were collected. The average age of the subject was 8.81 years. The average age of onset is 6.85 years (1–14 years). Male-to-female ratio is 1.13:1. A total of 62.9% of the patients are receiving monotherapies, and 10.6% are receiving multidrug therapy. Both age and course of disease of treated rolandic epilepsy (RE) patients are significantly different from those of untreated patients. Bilateral findings on electroencephalography (EEG) are less seen in patients with monotherapy compared with patients with multidrug therapy. Except for 25.4% patients not taking any AEDs, oxcarbazepine (OXC), sodium valproate (VPA), and levetiracetam (LEV) are the most commonly used 3 AEDs. VPA and LEV are commonly used in add-on therapy. OXC and LEV are more effective as monotherapy than VPA. Age of onset of Chinese RE patients is 6.85 years. Bilateral findings on EEG could be a risk factor to require multidrug therapy. In Chinese patients, OXC, VPA, and LEV are most commonly used AEDs as monotherapy and OXC and LEV are more effective than VPA. PMID:28121917

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.

Mapping interictal epileptic discharges using mutual information between concurrent EEG and fMRI.

PubMed

Caballero-Gaudes, César; Van de Ville, Dimitri; Grouiller, Frédéric; Thornton, Rachel; Lemieux, Louis; Seeck, Margitta; Lazeyras, François; Vulliemoz, Serge

2013-03-01

The mapping of haemodynamic changes related to interictal epileptic discharges (IED) in simultaneous electroencephalography (EEG) and functional MRI (fMRI) studies is usually carried out by means of EEG-correlated fMRI analyses where the EEG information specifies the model to test on the fMRI signal. The sensitivity and specificity critically depend on the accuracy of EEG detection and the validity of the haemodynamic model. In this study we investigated whether an information theoretic analysis based on the mutual information (MI) between the presence of epileptic activity on EEG and the fMRI data can provide further insights into the haemodynamic changes related to interictal epileptic activity. The important features of MI are that: 1) both recording modalities are treated symmetrically; 2) no requirement for a-priori models for the haemodynamic response function, or assumption of a linear relationship between the spiking activity and BOLD responses, and 3) no parametric model for the type of noise or its probability distribution is necessary for the computation of MI. Fourteen patients with pharmaco-resistant focal epilepsy underwent EEG-fMRI and intracranial EEG and/or surgical resection with positive postoperative outcome (seizure freedom or considerable reduction in seizure frequency) was available in 7/14 patients. We used nonparametric statistical assessment of the MI maps based on a four-dimensional wavelet packet resampling method. The results of MI were compared to the statistical parametric maps obtained with two conventional General Linear Model (GLM) analyses based on the informed basis set (canonical HRF and its temporal and dispersion derivatives) and the Finite Impulse Response (FIR) models. The MI results were concordant with the electro-clinically or surgically defined epileptogenic area in 8/14 patients and showed the same degree of concordance as the results obtained with the GLM-based methods in 12 patients (7 concordant and 5 discordant). In one patient, the information theoretic analysis improved the delineation of the irritative zone compared with the GLM-based methods. Our findings suggest that an information theoretic analysis can provide clinically relevant information about the BOLD signal changes associated with the generation and propagation of interictal epileptic discharges. The concordance between the MI, GLM and FIR maps support the validity of the assumptions adopted in GLM-based analyses of interictal epileptic activity with EEG-fMRI in such a manner that they do not significantly constrain the localization of the epileptogenic zone. Copyright © 2012 Elsevier Inc. All rights reserved.

Distinguishing mechanisms of gamma frequency oscillations in human current source signals using a computational model of a laminar neocortical network

PubMed Central

Lee, Shane; Jones, Stephanie R.

2013-01-01

Gamma frequency rhythms have been implicated in numerous studies for their role in healthy and abnormal brain function. The frequency band has been described to encompass as broad a range as 30–150 Hz. Crucial to understanding the role of gamma in brain function is an identification of the underlying neural mechanisms, which is particularly difficult in the absence of invasive recordings in macroscopic human signals such as those from magnetoencephalography (MEG) and electroencephalography (EEG). Here, we studied features of current dipole (CD) signals from two distinct mechanisms of gamma generation, using a computational model of a laminar cortical circuit designed specifically to simulate CDs in a biophysically principled manner (Jones et al., 2007, 2009). We simulated spiking pyramidal interneuronal gamma (PING) whose period is regulated by the decay time constant of GABAA-mediated synaptic inhibition and also subthreshold gamma driven by gamma-periodic exogenous excitatory synaptic drive. Our model predicts distinguishable CD features created by spiking PING compared to subthreshold driven gamma that can help to disambiguate mechanisms of gamma oscillations in human signals. We found that gamma rhythms in neocortical layer 5 can obscure a simultaneous, independent gamma in layer 2/3. Further, we arrived at a novel interpretation of the origin of high gamma frequency rhythms (100–150 Hz), showing that they emerged from a specific temporal feature of CDs associated with single cycles of PING activity and did not reflect a separate rhythmic process. Last we show that the emergence of observable subthreshold gamma required highly coherent exogenous drive. Our results are the first to demonstrate features of gamma oscillations in human current source signals that distinguish cellular and circuit level mechanisms of these rhythms and may help to guide understanding of their functional role. PMID:24385958

Attention-deficit/hyperactivity disorder and attention impairment in children with benign childhood epilepsy with centrotemporal spikes.

PubMed

Kim, Eun-Hee; Yum, Mi-Sun; Kim, Hyo-Won; Ko, Tae-Sung

2014-08-01

Attention-deficit/hyperactivity disorder (ADHD) is a common comorbidity in children with epilepsy and has a negative impact on behavior and learning. The purposes of this study were to quantify the prevalence of ADHD in benign childhood epilepsy with centrotemporal spikes (BCECTS) and to identify clinical factors that affect ADHD or attention impairment in patients with BCECTS. The medical records of 74 children (44 males) with neuropsychological examination from a total of 198 children diagnosed with BCECTS at Asan Medical Center were retrospectively reviewed. Electroclinical factors were compared across patients with ADHD and those without ADHD. Mean T-scores of the continuous performance test were compared across patients grouped according to various epilepsy characteristics. Forty-eight (64.9%) patients had ADHD. A history of febrile convulsion was more common in patients with ADHD than in patients without ADHD (p=0.049). Bilateral centrotemporal spikes on electroencephalogram were more common in patients receiving ADHD medication than in patients with untreated ADHD (p=0.004). Male patients, patients with frequent seizures prior to diagnosis, and patients with a high spike index (≥40/min) on sleep EEG at diagnosis had significantly lower visual selective attention (p<0.05). Children with BCECTS had a high prevalence of ADHD, and frequent seizures or interictal epileptiform abnormalities were closely related to impairment of visual selective attention in children with BCECTS, indicating the need for ADHD or attention impairment screening in children with BCECTS. Copyright © 2014 Elsevier Inc. All rights reserved.

Non-causal spike filtering improves decoding of movement intention for intracortical BCIs

PubMed Central

Masse, Nicolas Y.; Jarosiewicz, Beata; Simeral, John D.; Bacher, Daniel; Stavisky, Sergey D.; Cash, Sydney S.; Oakley, Erin M.; Berhanu, Etsub; Eskandar, Emad; Friehs, Gerhard; Hochberg, Leigh R.; Donoghue, John P.

2014-01-01

Background Multiple types of neural signals are available for controlling assistive devices through brain-computer interfaces (BCIs). Intracortically-recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on “sorting” action potentials. New method We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4 ms lag between recording and filtering neural signals. Results Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant’s intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study. Conclusions Non-causally filtering neural signals prior to extracting threshold crossing events may be a simple yet effective way to condition intracortically recorded neural activity for direct control of external devices through BCIs. PMID:25128256

ACTH has beneficial effects on stuttering in ADHD and ASD patients with ESES: A retrospective study.

PubMed

Altunel, Attila; Sever, Ali; Altunel, Emine Özlem

2017-02-01

Etiology of stuttering remains unknown and no pharmacologic intervention has been approved for treatment. We aimed to evaluate EEG parameters and the effect of adrenocorticotropic hormone (ACTH) therapy in stuttering. In this retrospective study, 25 patients with attention deficit and hyperactivity (ADHD) or autism spectrum disorder (ASD), and comorbid stuttering were followed and treated with ACTH for electrical status epilepticus in sleep (ESES). Sleep EEGs were recorded at referral and follow-up visits and short courses of ACTH were administered when spike-wave index (SWI) was ⩾15%. The assessment of treatment effectiveness was based on reduction in SWI, and the clinician-reported improvement in stuttering, and ADHD or ASD. Statistical analyses were conducted in order to investigate the relationship between the clinical and EEG parameters. Following treatment with ACTH, a reduction in SWI in all the patients was accompanied by a 72% improvement in ADHD or ASD, and 83.8% improvement in stuttering. Twelve of the 25 patients with stuttering showed complete treatment response. Linear regressions established that SWI at final visit significantly predicted improvement in ADHD or ASD, and in stuttering. If symptoms had recurred, improvement was once again achieved with repeated ACTH therapies. Stuttering always improved prior to, and recurred following ADHD or ASD. The underlying etiology leading to ESES may play a significant role in the pathophysiology of stuttering and connect stuttering to other developmental disorders. ACTH therapy has beneficial effects on stuttering and improves EEG parameters. Copyright © 2016 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

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

PubMed

Song, C; Zhuang, T; Wu, Q

2005-01-01

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

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

PubMed Central

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

2014-01-01

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

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

Generalized onset seizures with focal evolution (GOFE) - A unique seizure type in the setting of generalized epilepsy.

PubMed

Linane, Avriel; Lagrange, Andre H; Fu, Cary; Abou-Khalil, Bassel

2016-01-01

We report clinical and electrographic features of generalized onset seizures with focal evolution (GOFE) and present arguments for the inclusion of this seizure type in the seizure classification. The adult and pediatric Epilepsy Monitoring Unit databases at Vanderbilt Medical Center and Children's Hospital were screened to identify generalized onset seizures with focal evolution. We reviewed medical records for epilepsy characteristics, epilepsy risk factors, MRI abnormalities, neurologic examination, antiepileptic medications before and after diagnosis, and response to medications. We also reviewed ictal and interictal EEG tracings, as well as video-recorded semiology. Ten patients were identified, 7 males and 3 females. All of the patients developed generalized epilepsy in childhood or adolescence (ages 3-15years). Generalized onset seizures with focal evolution developed years after onset in 9 patients, with a semiology concerning for focal seizures or nonepileptic events. Ictal discharges had a generalized onset on EEG, described as either generalized spike-and-wave and/or polyspike-and-wave discharges, or generalized fast activity. This electrographic activity then evolved to focal rhythmic activity most commonly localized to one temporal or frontal region; five patients had multiple seizures evolving to focal activity in different regions of both hemispheres. The predominant interictal epileptiform activity included generalized spike-and-wave and/or polyspike-and-wave discharges in all patients. Taking into consideration all clinical and EEG data, six patients were classified with genetic (idiopathic) generalized epilepsy, and four were classified with structural/metabolic (symptomatic) generalized epilepsy. All of the patients had modifications to their medications following discharge, with three becoming seizure-free and five responding with >50% reduction in seizure frequency. Generalized onset seizures may occasionally have focal evolution with semiology suggestive of focal seizures, leading to a misdiagnosis of focal onset. This unique seizure type may occur with genetic as well as structural/metabolic forms of epilepsy. The identification of this seizure type may help clinicians choose appropriate medications, avoiding narrow spectrum agents known to aggravate generalized onset seizures. Copyright © 2015 Elsevier Inc. All rights reserved.

Automatic and Direct Identification of Blink Components from Scalp EEG

PubMed Central

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

2013-01-01

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

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

PubMed

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

1999-01-01

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

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

PubMed

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

2008-12-01

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

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

Spike-timing-dependent plasticity in the human dorso-lateral prefrontal cortex.

PubMed

Casula, Elias Paolo; Pellicciari, Maria Concetta; Picazio, Silvia; Caltagirone, Carlo; Koch, Giacomo

2016-12-01

Changes in the synaptic strength of neural connections are induced by repeated coupling of activity of interconnected neurons with precise timing, a phenomenon known as spike-timing-dependent plasticity (STDP). It is debated if this mechanism exists in large-scale cortical networks in humans. We combined transcranial magnetic stimulation (TMS) with concurrent electroencephalography (EEG) to directly investigate the effects of two paired associative stimulation (PAS) protocols (fronto-parietal and parieto-frontal) of pre and post-synaptic inputs within the human fronto-parietal network. We found evidence that the dorsolateral prefrontal cortex (DLPFC) has the potential to form robust STDP. Long-term potentiation/depression of TMS-evoked cortical activity is prompted after that DLPFC stimulation is followed/preceded by posterior parietal stimulation. Such bidirectional changes are paralleled by sustained increase/decrease of high-frequency oscillatory activity, likely reflecting STDP responsivity. The current findings could be important to drive plasticity of damaged cortical circuits in patients with cognitive or psychiatric disorders. Copyright © 2016 Elsevier Inc. All rights reserved.

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

The Effect of Neural Noise on Spike Time Precision in a Detailed CA3 Neuron Model

PubMed Central

Kuriscak, Eduard; Marsalek, Petr; Stroffek, Julius; Wünsch, Zdenek

2012-01-01

Experimental and computational studies emphasize the role of the millisecond precision of neuronal spike times as an important coding mechanism for transmitting and representing information in the central nervous system. We investigate the spike time precision of a multicompartmental pyramidal neuron model of the CA3 region of the hippocampus under the influence of various sources of neuronal noise. We describe differences in the contribution to noise originating from voltage-gated ion channels, synaptic vesicle release, and vesicle quantal size. We analyze the effect of interspike intervals and the voltage course preceding the firing of spikes on the spike-timing jitter. The main finding of this study is the ranking of different noise sources according to their contribution to spike time precision. The most influential is synaptic vesicle release noise, causing the spike jitter to vary from 1 ms to 7 ms of a mean value 2.5 ms. Of second importance was the noise incurred by vesicle quantal size variation causing the spike time jitter to vary from 0.03 ms to 0.6 ms. Least influential was the voltage-gated channel noise generating spike jitter from 0.02 ms to 0.15 ms. PMID:22778784

Millisecond Microwave Spikes: Statistical Study and Application for Plasma Diagnostics

NASA Astrophysics Data System (ADS)

Rozhansky, I. V.; Fleishman, G. D.; Huang, G.-L.

2008-07-01

We analyze a dense cluster of solar radio spikes registered at 4.5-6 GHz by the Purple Mountain Observatory spectrometer (Nanjing, China), operating in the 4.5-7.5 GHz range with 5 ms temporal resolution. To handle the data from the spectrometer, we developed a new technique that uses a nonlinear multi-Gaussian spectral fit based on χ2 criteria to extract individual spikes from the originally recorded spectra. Applying this method to the experimental raw data, we eventually identified about 3000 spikes for this event, which allows us to make a detailed statistical analysis. Various statistical characteristics of the spikes have been evaluated, including the intensity distributions, the spectral bandwidth distributions, and the distribution of the spike mean frequencies. The most striking finding of this analysis is the distributions of the spike bandwidth, which are remarkably asymmetric. To reveal the underlaying microphysics, we explore the local-trap model with the renormalized theory of spectral profiles of the electron cyclotron maser (ECM) emission peak in a source with random magnetic irregularities. The distribution of the solar spike relative bandwidths calculated within the local-trap model represents an excellent fit to the experimental data. Accordingly, the developed technique may offer a new tool with which to study very low levels of magnetic turbulence in the spike sources, when the ECM mechanism of the spike cluster is confirmed.

PySpike-A Python library for analyzing spike train synchrony

NASA Astrophysics Data System (ADS)

Mulansky, Mario; Kreuz, Thomas

Understanding how the brain functions is one of the biggest challenges of our time. The analysis of experimentally recorded neural firing patterns (spike trains) plays a crucial role in addressing this problem. Here, the PySpike library is introduced, a Python package for spike train analysis providing parameter-free and time-scale independent measures of spike train synchrony. It allows to compute similarity and dissimilarity profiles, averaged values and distance matrices. Although mainly focusing on neuroscience, PySpike can also be applied in other contexts like climate research or social sciences. The package is available as Open Source on Github and PyPI.

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

PubMed

Cichy, Radoslaw Martin; Pantazis, Dimitrios

2017-09-01

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

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

PubMed

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

2006-01-01

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

An alternative subspace approach to EEG dipole source localization

NASA Astrophysics Data System (ADS)

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

2004-01-01

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

Underlying neurological dysfunction in children with language, speech or learning difficulties and a verbal IQ--performance IQ discrepancy.

PubMed

Meulemans, J; Goeleven, A; Zink, I; Loyez, L; Lagae, L; Debruyne, F

2012-01-01

We investigated the relationship between possible underlying neurological dysfunction and a significant discrepancy between verbal IQ/performance IQ (VIQ-PIQ) in children with language, speech or learning difficulties. In a retrospective study, we analysed data obtained from intelligence testing and neurological evaluation in 49 children with a significant VIQ-PIQ discrepancy (> or = 25 points) who were referred because of language, speech or learning difficulties to the Multidisciplinary University Centre for Logopedics and Audiology (MUCLA) of the University Hospitals of Leuven, Belgium. The group of children broke down into a group of 35 children with PIQ > VIQ and a group of 14 children with VIQ > PIQ. In the first group, neurological data were present for 24 children. The neurological history and clinical neurological examination were normal in all cases. Brain MRI was performed in 15 cases and proved to be normal in all children. Brain activity was assessed with long-term video EEG monitoring in ten children. In two children, the EEG results were abnormal: there was an epileptic focus in one child and a manifest alteration in the EEG typical of Landau-Kleffner syndrome in the other. In the second group of 14 children whose VIQ was higher than the PIQ, neurological data were available for ten children. Neurological history and clinical neurological examination were normal in all cases. Brain MRI was performed in five cases and was normal in all children. EEG monitoring was performed in one child. This revealed benign childhood epilepsy with centrotemporal spikes. In a small number of children (9%) with speech, language and learning difficulties and a discrepancy between VIQ and PIQ, an underlying neurological abnormality is present. We recommend referring children with a significant VIQ-PIQ mismatch to a paediatric neurologist. As an epileptic disorder seems to be the most common underlying neurological pathology in this specific group of children, EEG monitoring should be recommended in these children. Neuro-imaging should only be used in selected patients.

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

PubMed

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

2014-01-01

We study the use of nonparametric multicompare statistical tests on the performance of simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), when used for electroencephalographic (EEG) source localization. Such task can be posed as an optimization problem for which the referred metaheuristic methods are well suited. Hence, we evaluate the localization's performance in terms of metaheuristics' operational parameters and for a fixed number of evaluations of the objective function. In this way, we are able to link the efficiency of the metaheuristics with a common measure of computational cost. Our results did not show significant differences in the metaheuristics' performance for the case of single source localization. In case of localizing two correlated sources, we found that PSO (ring and tree topologies) and DE performed the worst, then they should not be considered in large-scale EEG source localization problems. Overall, the multicompare tests allowed to demonstrate the little effect that the selection of a particular metaheuristic and the variations in their operational parameters have in this optimization problem.

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

PubMed

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

2016-10-01

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

The cortical focus in childhood absence epilepsy; evidence from nonlinear analysis of scalp EEG recordings.

PubMed

Sarrigiannis, Ptolemaios G; Zhao, Yifan; He, Fei; Billings, Stephen A; Baster, Kathleen; Rittey, Chris; Yianni, John; Zis, Panagiotis; Wei, Hualiang; Hadjivassiliou, Marios; Grünewald, Richard

2018-03-01

To determine the origin and dynamic characteristics of the generalised hyper-synchronous spike and wave (SW) discharges in childhood absence epilepsy (CAE). We applied nonlinear methods, the error reduction ratio (ERR) causality test and cross-frequency analysis, with a nonlinear autoregressive exogenous (NARX) model, to electroencephalograms (EEGs) from CAE, selected with stringent electro-clinical criteria (17 cases, 42 absences). We analysed the pre-ictal and ictal strength of association between homologous and heterologous EEG derivations and estimated the direction of synchronisation and corresponding time lags. A frontal/fronto-central onset of the absences is detected in 13 of the 17 cases with the highest ictal strength of association between homologous frontal followed by centro-temporal and fronto-central areas. Delays consistently in excess of 4 ms occur at the very onset between these regions, swiftly followed by the emergence of "isochronous" (0-2 ms) synchronisation but dynamic time lag changes occur during SW discharges. In absences an initial cortico-cortical spread leads to dynamic lag changes to include periods of isochronous interhemispheric synchronisation, which we hypothesize is mediated by the thalamus. Absences from CAE show ictal epileptic network dynamics remarkably similar to those observed in WAG/Rij rats which guided the formulation of the cortical focus theory. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

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.

Analysis of EEG Related Saccadic Eye Movement

NASA Astrophysics Data System (ADS)

Funase, Arao; Kuno, Yoshiaki; Okuma, Shigeru; Yagi, Tohru

Our final goal is to establish the model for saccadic eye movement that connects the saccade and the electroencephalogram(EEG). As the first step toward this goal, we recorded and analyzed the saccade-related EEG. In the study recorded in this paper, we tried detecting a certain EEG that is peculiar to the eye movement. In these experiments, each subject was instructed to point their eyes toward visual targets (LEDs) or the direction of the sound sources (buzzers). In the control cases, the EEG was recorded in the case of no eye movemens. As results, in the visual experiments, we found that the potential of EEG changed sharply on the occipital lobe just before eye movement. Furthermore, in the case of the auditory experiments, similar results were observed. In the case of the visual experiments and auditory experiments without eye movement, we could not observed the EEG changed sharply. Moreover, when the subject moved his/her eyes toward a right-side target, a change in EEG potential was found on the right occipital lobe. On the contrary, when the subject moved his/her eyes toward a left-side target, a sharp change in EEG potential was found on the left occipital lobe.

EEG signatures of arm isometric exertions in preparation, planning and execution.

PubMed

Nasseroleslami, Bahman; Lakany, Heba; Conway, Bernard A

2014-04-15

The electroencephalographic (EEG) activity patterns in humans during motor behaviour provide insight into normal motor control processes and for diagnostic and rehabilitation applications. While the patterns preceding brisk voluntary movements, and especially movement execution, are well described, there are few EEG studies that address the cortical activation patterns seen in isometric exertions and their planning. In this paper, we report on time and time-frequency EEG signatures in experiments in normal subjects (n=8), using multichannel EEG during motor preparation, planning and execution of directional centre-out arm isometric exertions performed at the wrist in the horizontal plane, in response to instruction-delay visual cues. Our observations suggest that isometric force exertions are accompanied by transient and sustained event-related potentials (ERP) and event-related (de-)synchronisations (ERD/ERS), comparable to those of a movement task. Furthermore, the ERPs and ERD/ERS are also observed during preparation and planning of the isometric task. Comparison of ear-lobe-referenced and surface Laplacian ERPs indicates the contribution of superficial sources in supplementary and pre-motor (FC(z)), parietal (CP(z)) and primary motor cortical areas (C₁ and FC₁) to ERPs (primarily negative peaks in frontal and positive peaks in parietal areas), but contribution of deep sources to sustained time-domain potentials (negativity in planning and positivity in execution). Transient and sustained ERD patterns in μ and β frequency bands of ear-lobe-referenced and surface Laplacian EEG indicate the contribution of both superficial and deep sources to ERD/ERS. As no physical displacement happens during the task, we can infer that the underlying mechanisms of motor-related ERPs and ERD/ERS patterns do not only depend on change in limb coordinate or muscle-length-dependent ascending sensory information and are primary generated by motor preparation, direction-dependent planning and execution of isometric motor tasks. The results contribute to our understanding of the functions of different brain regions during voluntary motor tasks and their activity signatures in EEG can shed light on the relationships between large-scale recordings such as EEG and other recordings such as single unit activity and fMRI in this context. Copyright © 2013 Elsevier Inc. All rights reserved.

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

PubMed

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

2014-01-01

The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy-an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm. © 2013. Published by Elsevier Inc. All rights reserved.

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

PubMed

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

2014-10-01

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

Epilepsy surgery in the elderly: an unusual case of a 75-year-old man with recurrent status epilepticus.

PubMed

Tellez-Zenteno, Jose F; Sadanand, Venkatraman; Riesberry, Martha; Robinson, Christopher A; Ogieglo, Lissa; Masiowski, Paul; Vrbancic, Mirna

2009-06-01

Epilepsy surgery is increasingly well-supported as an effective treatment for patients with intractable epilepsy. It is most often performed on younger patients and the safety and efficacy of epilepsy surgery in elderly patients are not frequently described. We report a case of a 75-year-old right-handed man who underwent a left fronto-temporal craniotomy for resection of a suprasellar meningioma in 2002. Immediately following hospital discharge, he began to experience complex partial seizures. He continued to have frequent seizures despite treatment with multiple combinations of antiepileptic medications. He presented with status epilepticus every two or three months, and required long periods of hospitalization on each occasion for post-ictal confusion and aphasia. Scalp EEG showed continuous spikes and polyspikes and persistent slowing in the left temporal area, as well as spikes in the left frontal area. EEG telemetry recorded multiple seizures, all with a clear focus in the left temporal area. MRI scan showed an area of encephalomalacia in the left temporal lobe, as well as post-surgical changes in the left frontal area. Neuropsychological testing showed bilateral memory impairment with no significant cognitive decline expected after unilateral temporal lobe resection. A left anteromesial temporal lobectomy was performed with intraoperative electrocorticography. Since surgery, the patient was not seizure-free (Engel class II-b), but had no further episodes of status epilepticus in one year and two months of follow-up. This is one of the oldest patients reported in the literature with epilepsy surgery and supports the possibility of epilepsy surgery in elderly patients for particular cases. In addition, few cases with such a malignant evolution of temporal lobe epilepsy have been described in this age group.

Low Activity Microstates During Sleep.

PubMed

Miyawaki, Hiroyuki; Billeh, Yazan N; Diba, Kamran

2017-06-01

To better understand the distinct activity patterns of the brain during sleep, we observed and investigated periods of diminished oscillatory and population spiking activity lasting for seconds during non-rapid eye movement (non-REM) sleep, which we call "LOW" activity sleep. We analyzed spiking and local field potential (LFP) activity of hippocampal CA1 region alongside neocortical electroencephalogram (EEG) and electromyogram (EMG) in 19 sessions from four male Long-Evans rats (260-360 g) during natural wake/sleep across the 24-hr cycle as well as data from other brain regions obtained from http://crcns.org.1,2. LOW states lasted longer than OFF/DOWN states and were distinguished by a subset of "LOW-active" cells. LOW activity sleep was preceded and followed by increased sharp-wave ripple activity. We also observed decreased slow-wave activity and sleep spindles in the hippocampal LFP and neocortical EEG upon LOW onset, with a partial rebound immediately after LOW. LOW states demonstrated activity patterns consistent with sleep but frequently transitioned into microarousals and showed EMG and LFP differences from small-amplitude irregular activity during quiet waking. Their likelihood decreased within individual non-REM epochs yet increased over the course of sleep. By analyzing data from the entorhinal cortex of rats,1 as well as the hippocampus, the medial prefrontal cortex, the postsubiculum, and the anterior thalamus of mice,2 obtained from http://crcns.org, we confirmed that LOW states corresponded to markedly diminished activity simultaneously in all of these regions. We propose that LOW states are an important microstate within non-REM sleep that provide respite from high-activity sleep and may serve a restorative function. © Sleep Research Society 2017. Published by Oxford University Press [on behalf of the Sleep Research Society].

Phase shift in the 24-hour rhythm of hippocampal EEG spiking activity in a rat model of temporal lobe epilepsy

PubMed Central

Stanley, David A.; Talathi, Sachin S.; Parekh, Mansi B.; Cordiner, Daniel J.; Zhou, Junli; Mareci, Thomas H.; Ditto, William L.

2013-01-01

For over a century epileptic seizures have been known to cluster at specific times of the day. Recent studies have suggested that the circadian regulatory system may become permanently altered in epilepsy, but little is known about how this affects neural activity and the daily pattern of seizures. To investigate, we tracked long-term changes in the rate of spontaneous hippocampal EEG spikes (SPKs) in a rat model of temporal lobe epilepsy. In healthy animals, SPKs oscillated with near 24-h period; however, after injury by status epilepticus, a persistent phase shift of ∼12 h emerged in animals that later went on to develop chronic spontaneous seizures. Additional measurements showed that global 24-h rhythms, including core body temperature and theta state transitions, did not phase shift. Instead, we hypothesized that locally impaired circadian input to the hippocampus might be responsible for the SPK phase shift. This was investigated with a biophysical computer model in which we showed that subtle changes in the relative strengths of circadian input could produce a phase shift in hippocampal neural activity. MRI provided evidence that the medial septum, a putative circadian relay center for the hippocampus, exhibits signs of damage and therefore could contribute to local circadian impairment. Our results suggest that balanced circadian input is critical to maintaining natural circadian phase in the hippocampus and that damage to circadian relay centers, such as the medial septum, may disrupt this balance. We conclude by discussing how abnormal circadian regulation may contribute to the daily rhythms of epileptic seizures and related cognitive dysfunction. PMID:23678009

EVALUATION OF SPIKING PROCEDURES FOR RECOVERY OF CRYPTOSPORIDIUM IN STREAM WATERS USING USEPA METHOD 1623

EPA Science Inventory

U.S. Environmental Protection Agency Method 1623 is widely used to monitor source waters and drinking water supplies for Cryptosporidium oocysts. Analyzing matrix spikes is an important component of Method 1623. Matrix spikes are used to determine the effect of the environmental...

Estimation of effective brain connectivity with dual Kalman filter and EEG source localization methods.

PubMed

Rajabioun, Mehdi; Nasrabadi, Ali Motie; Shamsollahi, Mohammad Bagher

2017-09-01

Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions.

EEG-Informed fMRI: A Review of Data Analysis Methods

PubMed Central

Abreu, Rodolfo; Leal, Alberto; Figueiredo, Patrícia

2018-01-01

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

Assessment of seizure liability of Org 306039, a 5-HT2c agonist, using hippocampal brain slice and rodent EEG telemetry.

PubMed

Markgraf, Carrie G; DeBoer, Erik; Zhai, Jin; Cornelius, Lara; Zhou, Ying Ying; MacSweeney, Cliona

2014-01-01

Evaluation of the seizure potential for a CNS-targeted pharmaceutical compound before it is administered to humans is an important part of development. The current in vitro and in vivo studies were undertaken to characterize the seizure potential of the potent and selective 5-HT2c agonist Org 306039. Rat hippocampal slices (n=5) were prepared and Org 306039 was applied over a concentration range of 0-1000μM. Male Sprague-Dawley rats, implanted with telemetry EEG recording electrodes received either vehicle (n=4) or 100mg/kg Org 306039 (n=4) by oral gavage daily for 10days. EEG was recorded continuously for 22±1h post-dose each day. Post-dose behavior observations were conducted daily for 2h. Body temperature was measured at 1 and 2h post-dose. On Day 7, blood samples were drawn for pharmacokinetic analysis of Org 306039. In hippocampal slice, Org 306039 elicited a concentration-dependent increase in population spike area and number recorded from CA1 area, indicating seizure-genic potential. In telemetered rats, Org 306039 was associated with a decrease in body weight, a decrease in body temperature and the appearance of seizure-related behaviors and pre-seizure waveforms on EEG. One rat exhibited an overt seizure. Plasma concentrations of Org 306039 were similar among the 4 rats in the Org-treated group. Small group size made it difficult to determine a PK-PD relationship. These results indicate that the in vitro and in vivo models complement each other in the characterization of the seizure potential of CNS-targeted compounds such as the 5-HT2c agonist Org 306039. Copyright © 2014 Elsevier Inc. All rights reserved.

Lennox-Gastaut syndrome of unknown cause: phenotypic characteristics of patients in the Epilepsy Phenome/Genome Project.

PubMed

Widdess-Walsh, Peter; Dlugos, Dennis; Fahlstrom, Robyn; Joshi, Sucheta; Shellhaas, Renée; Boro, Alex; Sullivan, Joseph; Geller, Eric

2013-11-01

Lennox-Gastaut syndrome (LGS) is a devastating childhood-onset epilepsy syndrome. The cause is unknown in 25% of cases. Little has been described about the specific clinical or electroencephalography (EEG) features of LGS of unknown or genetic cause (LGS(u)). The Epilepsy Phenome/Genome Project (EPGP) aims to characterize LGS(u) by phenotypic analysis of patients with LGS(u) and their parents. One hundred thirty-five patients with LGS with no known etiology and their parents were enrolled from 19 EPGP centers in the United States and Australia. Clinical data from medical records, standardized questionnaires, imaging, and EEG were collected with use of online informatics systems developed for EPGP. LGS(u) in the EPGP cohort had a broad range of onset of epilepsy from 1 to 13 years, was male predominant (p < 0.0002), and was associated with normal development prior to seizure onset in 59.2% of patients. Despite the diagnosis, almost half of the adult patients with LGS(u) completed secondary school. Parents were cognitively normal. All subjects had EEG recordings with generalized epileptiform abnormalities with a spike wave frequency range of 1-5 Hz (median 2 Hz), whereas 8.1% of subjects had EEG studies with a normal posterior dominant rhythm. Almost 12% of patients evolved from West syndrome. LGS(u) has distinctive characteristics including a broad age range of onset, male predominance, and often normal development prior to the onset of seizures. Cognitive achievements such as completion of secondary school were possible in half of adult patients. Our phenotypic description of LGS(u) coupled with future genetic studies will advance our understanding of this epilepsy syndrome. Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.

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.

Bio-amplifier with Driven Shield Inputs to Reduce Electrical Noise and its Application to Laboratory Teaching of Electrophysiology

PubMed Central

Matsuzaka, Yoshiya; Ichihara, Toshiaki; Abe, Toshihiko; Mushiake, Hajime

2012-01-01

We describe a custom-designed bio-amplifier and its use in teaching neurophysiology to undergraduate students. The amplifier has the following features: 1) differential amplification with driven shield inputs, which makes it workable even in electrically unshielded environments, 2) high input impedance to allow recordings of small signals through high signal source impedance, 3) dual fixed frequency bandpass filters (1–340Hz for surface EMG, EEG, local field potential etc and 320Hz – 3.4kHz for neuronal action potential recording) and independent gain controllers (up to x107,000) to allow the recording of different signals from the same source (e.g., local field potential and spiking activity of neurons), and 4) printed circuit board technology for easy replication with consistent quality. We compared its performance with a commercial amplifier in an electrically noisy environment. Even without any electrostatic shield, it recorded clear electromyographic activity with little interference from other electric appliances. In contrast, the commercial amplifier’s performance severely deteriorated under the same condition. We used this amplifier to build a computer-controlled stimulation and measurement system for electroencephalographic recordings by undergraduate students. The students successfully recorded various sensory evoked potentials with clarity that otherwise would have required costly instruments. This amplifier is a low-cost yet reliable instrument for electro-physiological recording both in education and research. PMID:23504543

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.

Combined MEG-EEG source localisation in patients with sub-acute sclerosing pan-encephalitis.

PubMed

Velmurugan, J; Sinha, Sanjib; Nagappa, Madhu; Mariyappa, N; Bindu, P S; Ravi, G S; Hazra, Nandita; Thennarasu, K; Ravi, V; Taly, A B; Satishchandra, P

2016-08-01

To study the genesis and propagation patterns of periodic complexes (PCs) associated with myoclonic jerks in sub-acute sclerosing pan-encephalitis (SSPE) using magnetoencephalography (MEG) and electroencephalography (EEG). Simultaneous recording of MEG (306 channels) and EEG (64 channels) in five patients of SSPE (M:F = 3:2; age 10.8 ± 3.2 years; symptom-duration 6.2 ± 10 months) was carried out using Elekta Neuromag(®) TRIUX™ system. Qualitative analysis of 80-160 PCs per patient was performed. Ten isomorphic classical PCs with significant field topography per patient were analysed at the 'onset' and at 'earliest significant peak' of the burst using discrete and distributed source imaging methods. MEG background was asymmetrical in 2 and slow in 3 patients. Complexes were periodic (3) or quasi-periodic (2), occurring every 4-16 s and varied in morphology among patients. Mean source localization at onset of bursts using discrete and distributed source imaging in magnetic source imaging (MSI) was in thalami and or insula (50 and 50 %, respectively) and in electric source imaging (ESI) was also in thalami and or insula (38 and 46 %, respectively). Mean source localization at the earliest rising phase of peak in MSI was in peri-central gyrus (49 and 42 %) and in ESI it was in frontal cortex (52 and 56 %). Further analysis revealed that PCs were generated in thalami and or insula and thereafter propagated to anterolateral surface of the cortices (viz. sensori-motor cortex and frontal cortex) to same side as that of the onset. This novel MEG-EEG based case series of PCs provides newer insights for understanding the plausible generators of myoclonus in SSPE and patterns of their propagation.

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.

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.

Spatio-temporal Reconstruction of Neural Sources Using Indirect Dominant Mode Rejection.

PubMed

Jafadideh, Alireza Talesh; Asl, Babak Mohammadzadeh

2018-04-27

Adaptive minimum variance based beamformers (MVB) have been successfully applied to magnetoencephalogram (MEG) and electroencephalogram (EEG) data to localize brain activities. However, the performance of these beamformers falls down in situations where correlated or interference sources exist. To overcome this problem, we propose indirect dominant mode rejection (iDMR) beamformer application in brain source localization. This method by modifying measurement covariance matrix makes MVB applicable in source localization in the presence of correlated and interference sources. Numerical results on both EEG and MEG data demonstrate that presented approach accurately reconstructs time courses of active sources and localizes those sources with high spatial resolution. In addition, the results of real AEF data show the good performance of iDMR in empirical situations. Hence, iDMR can be reliably used for brain source localization especially when there are correlated and interference sources.

Multimodal Spatial Calibration for Accurately Registering EEG Sensor Positions

PubMed Central

Chen, Shengyong; Xiao, Gang; Li, Xiaoli

2014-01-01

This paper proposes a fast and accurate calibration method to calibrate multiple multimodal sensors using a novel photogrammetry system for fast localization of EEG sensors. The EEG sensors are placed on human head and multimodal sensors are installed around the head to simultaneously obtain all EEG sensor positions. A multiple views' calibration process is implemented to obtain the transformations of multiple views. We first develop an efficient local repair algorithm to improve the depth map, and then a special calibration body is designed. Based on them, accurate and robust calibration results can be achieved. We evaluate the proposed method by corners of a chessboard calibration plate. Experimental results demonstrate that the proposed method can achieve good performance, which can be further applied to EEG source localization applications on human brain. PMID:24803954

How Different EEG References Influence Sensor Level Functional Connectivity Graphs

PubMed Central

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

2017-01-01

Highlights: Hamming Distance is applied to distinguish the difference of functional connectivity networkThe orientations of sources are testified to influence the scalp Functional Connectivity Graph (FCG) from different references significantlyREST, the reference electrode standardization technique, is proved to have an overall stable and excellent performance in variable situations. The choice of an electroencephalograph (EEG) reference is a practical issue for the study of brain functional connectivity. To study how EEG reference influence functional connectivity estimation (FCE), this study compares the differences of FCE resulting from the different references such as REST (the reference electrode standardization technique), average reference (AR), linked mastoids (LM), and left mastoid references (LR). Simulations involve two parts. One is based on 300 dipolar pairs, which are located on the superficial cortex with a radial source direction. The other part is based on 20 dipolar pairs. In each pair, the dipoles have various orientation combinations. The relative error (RE) and Hamming distance (HD) between functional connectivity matrices of ideal recordings and that of recordings obtained with different references, are metrics to compare the differences of the scalp functional connectivity graph (FCG) derived from those two kinds of recordings. Lower RE and HD values imply more similarity between the two FCGs. Using the ideal recording (IR) as a standard, the results show that AR, LM and LR perform well only in specific conditions, i.e., AR performs stable when there is no upward component in sources' orientation. LR achieves desirable results when the sources' locations are away from left ear. LM achieves an indistinct difference with IR, i.e., when the distribution of source locations is symmetric along the line linking the two ears. However, REST not only achieves excellent performance for superficial and radial dipolar sources, but also achieves a stable and robust performance with variable source locations and orientations. Benefitting from the stable and robust performance of REST vs. other reference methods, REST might best recover the real FCG of EEG. Thus, REST based FCG may be a good candidate to compare the FCG of EEG based on different references from different labs. PMID:28725175

A method for classification of transient events in EEG recordings: application to epilepsy diagnosis.

PubMed

Tzallas, A T; Karvelis, P S; Katsis, C D; Fotiadis, D I; Giannopoulos, S; Konitsiotis, S

2006-01-01

The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method. A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity. The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases. The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.

Abdominal epilepsy as an unusual cause of abdominal pain: a case report.

PubMed

Yunus, Yilmaz; Sefer, Ustebay; Dondu, Ulker Ustebay; Ismail, Ozanli; Yusuf, Ehi

2016-09-01

Abdominal pain, in etiology sometimes difficult to be defined, is a frequent complaint in childhood. Abdominal epilepsy is a rare cause of abdominal pain. In this article, we report on 5 year old girl patient with abdominal epilepsy. Some investigations (stool investigation, routine blood tests, ultrasonography (USG), electrocardiogram (ECHO) and electrocardiograpy (ECG), holter for 24hr.) were done to understand the origin of these complaints; but no abnormalities were found. Finally an EEG was done during an episode of abdominal pain and it was shown that there were generalized spikes especially precipitated by hyperventilation. The patient did well on valproic acid therapy and EEG was normal 1 month after beginning of the treatment. The cause of chronic recurrent paroxymal abdominal pain is difficult for the clinicians to diagnose in childhood. A lot of disease may lead to paroxysmal gastrointestinal symptoms like familial mediterranean fever and porfiria. Abdominal epilepsy is one of the rare but easily treatable cause of abdominal pain. In conclusion, abdominal epilepsy should be suspected in children with recurrent abdominal pain.

Electrophysiological biomarkers of epileptogenicity after traumatic brain injury.

PubMed

Perucca, Piero; Smith, Gregory; Santana-Gomez, Cesar; Bragin, Anatol; Staba, Richard

2018-06-05

Post-traumatic epilepsy is the architype of acquired epilepsies, wherein a brain insult initiates an epileptogenic process culminating in an unprovoked seizure after weeks, months or years. Identifying biomarkers of such process is a prerequisite for developing and implementing targeted therapies aimed at preventing the development of epilepsy. Currently, there are no validated electrophysiological biomarkers of post-traumatic epileptogenesis. Experimental EEG studies using the lateral fluid percussion injury model have identified three candidate biomarkers of post-traumatic epileptogenesis: pathological high-frequency oscillations (HFOs, 80-300 Hz); repetitive HFOs and spikes (rHFOSs); and reduction in sleep spindle duration and dominant frequency at the transition from stage III to rapid eye movement sleep. EEG studies in humans have yielded conflicting data; recent evidence suggests that epileptiform abnormalities detected acutely after traumatic brain injury carry a significantly increased risk of subsequent epilepsy. Well-designed studies are required to validate these promising findings, and ultimately establish whether there are post-traumatic electrophysiological features which can guide the development of 'antiepileptogenic' therapies. Copyright © 2018 Elsevier Inc. All rights reserved.

Localization of MEG pathologic gamma oscillations in adult epilepsy patients with focal cortical dysplasia☆

PubMed Central

Jeong, Woorim; Kim, June Sic; Chung, Chun Kee

2013-01-01

We aimed to evaluate the clinical value of gamma oscillations in MEG for intractable neocortical epilepsy patients with cortical dysplasia by comparing gamma and interictal spike events. A retrospective analysis of MEG recordings of 30 adult neocortical epilepsy patients was performed. Gamma (30–70 Hz) and interictal spike events were independently identified, their independent or concurrent presence determined, and their source localization rates compared. Of 30 patients, gamma activities were detected in 28 patients and interictal spikes in 24 patients. Gamma events alone appeared in 5 patients, interictal spikes alone in 1 patient, and no events in 1 patient. Gamma co-occurred with interictal spikes in 20.1 ± 22.1% and interictal spikes co-occurred with gamma in 15.0 ± 19.2%. Rates of event localization within the resection cavity were significantly different (p = 0.042) between gamma (63.3 ± 32.6%) and interictal spike (47.0 ± 41.3%) events. In 4 of the 5 gamma-only patients the mean localization rate was 42.5%. Compared with the interictal spike localization rate, 4 of 9 seizure-free patients had higher gamma localization rates, 4 had the same rate, and 1 had a lower rate. Individual gamma events can be detected independently from interictal spike presence. Gamma can be localized to the resection cavity at least comparably to or more frequently than that from interictal spikes. Even when interictal spikes were undetected, gamma sources were localized to the resection cavity. Gamma oscillations may be a useful indicator of epileptogenic focus. PMID:24273733

Understanding neurodynamical systems via Fuzzy Symbolic Dynamics.

PubMed

Dobosz, Krzysztof; Duch, Włodzisław

2010-05-01

Neurodynamical systems are characterized by a large number of signal streams, measuring activity of individual neurons, local field potentials, aggregated electrical (EEG) or magnetic potentials (MEG), oxygen use (fMRI) or activity of simulated neurons. Various basis set decomposition techniques are used to analyze such signals, trying to discover components that carry meaningful information, but these techniques tell us little about the global activity of the whole system. A novel technique called Fuzzy Symbolic Dynamics (FSD) is introduced to help in understanding of the multidimensional dynamical system's behavior. It is based on a fuzzy partitioning of the signal space that defines a non-linear mapping of the system's trajectory to the low-dimensional space of membership function activations. This allows for visualization of the trajectory showing various aspects of observed signals that may be difficult to discover looking at individual components, or to notice otherwise. FSD mapping can be applied to raw signals, transformed signals (for example, ICA components), or to signals defined in the time-frequency domain. To illustrate the method two FSD visualizations are presented: a model system with artificial radial oscillatory sources, and the output layer (50 neurons) of Respiratory Rhythm Generator (RRG) composed of 300 spiking neurons. 2009 Elsevier Ltd. All rights reserved.

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.

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.

Mushu, a free- and open source BCI signal acquisition, written in Python.

PubMed

Venthur, Bastian; Blankertz, Benjamin

2012-01-01

The following paper describes Mushu, a signal acquisition software for retrieval and online streaming of Electroencephalography (EEG) data. It is written, but not limited, to the needs of Brain Computer Interfacing (BCI). It's main goal is to provide a unified interface to EEG data regardless of the amplifiers used. It runs under all major operating systems, like Windows, Mac OS and Linux, is written in Python and is free- and open source software licensed under the terms of the GNU General Public License.

Unsupervised Segmentation of Head Tissues from Multi-modal MR Images for EEG Source Localization.

PubMed

Mahmood, Qaiser; Chodorowski, Artur; Mehnert, Andrew; Gellermann, Johanna; Persson, Mikael

2015-08-01

In this paper, we present and evaluate an automatic unsupervised segmentation method, hierarchical segmentation approach (HSA)-Bayesian-based adaptive mean shift (BAMS), for use in the construction of a patient-specific head conductivity model for electroencephalography (EEG) source localization. It is based on a HSA and BAMS for segmenting the tissues from multi-modal magnetic resonance (MR) head images. The evaluation of the proposed method was done both directly in terms of segmentation accuracy and indirectly in terms of source localization accuracy. The direct evaluation was performed relative to a commonly used reference method brain extraction tool (BET)-FMRIB's automated segmentation tool (FAST) and four variants of the HSA using both synthetic data and real data from ten subjects. The synthetic data includes multiple realizations of four different noise levels and several realizations of typical noise with a 20% bias field level. The Dice index and Hausdorff distance were used to measure the segmentation accuracy. The indirect evaluation was performed relative to the reference method BET-FAST using synthetic two-dimensional (2D) multimodal magnetic resonance (MR) data with 3% noise and synthetic EEG (generated for a prescribed source). The source localization accuracy was determined in terms of localization error and relative error of potential. The experimental results demonstrate the efficacy of HSA-BAMS, its robustness to noise and the bias field, and that it provides better segmentation accuracy than the reference method and variants of the HSA. They also show that it leads to a more accurate localization accuracy than the commonly used reference method and suggest that it has potential as a surrogate for expert manual segmentation for the EEG source localization problem.

Independent EEG Sources Are Dipolar

PubMed Central

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

2012-01-01

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

Neural basis of postural instability identified by VTC and EEG

PubMed Central

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

2010-01-01

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

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

PubMed Central

2010-01-01

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

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

PubMed

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

2016-08-01

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

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.

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

PubMed

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

2013-01-01

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

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.

Study design and percent recoveries of anthropogenic organic compounds with and without the addition of ascorbic acid to preserve water samples containing free chlorine, 2004-06

USGS Publications Warehouse

Valder, Joshua F.; Delzer, Gregory C.; Price, Curtis V.; Sandstrom, Mark W.

2008-01-01

The National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey (USGS) began implementing Source Water-Quality Assessments (SWQAs) in 2002 that focus on characterizing the quality of source water and finished water of aquifers and major rivers used by some of the larger community water systems in the United States. As used for SWQA studies, source water is the raw (ambient) water collected at the supply well prior to water treatment (for ground water) or the raw (ambient) water collected from the river near the intake (for surface water). Finished water is the water that is treated, which typically involves, in part, the addition of chlorine or other disinfection chemicals to remove pathogens, and is ready to be delivered to consumers. Finished water is collected before the water enters the distribution system. This report describes the study design and percent recoveries of anthropogenic organic compounds (AOCs) with and without the addition of ascorbic acid to preserve water samples containing free chlorine. The percent recoveries were determined by using analytical results from a laboratory study conducted in 2004 by the USGS's National Water Quality Laboratory (NWQL) and from data collected during 2004-06 for a field study currently (2008) being conducted by the USGS's NAWQA Program. The laboratory study was designed to determine if preserving samples with ascorbic acid (quenching samples) adversely affects analytical performance under controlled conditions. During the laboratory study, eight samples of reagent water were spiked for each of five analytical schedules evaluated. Percent recoveries from these samples were then compared in two ways: (1) four quenched reagent spiked samples analyzed on day 0 were compared with four quenched reagent spiked samples analyzed on day 7 or 14, and (2) the combined eight quenched reagent spiked samples analyzed on day 0, 7, or 14 were compared with eight laboratory reagent spikes (LRSs). Percent recoveries from the quenched reagent spiked samples that were analyzed at two different times (day 0 and day 7 or 14) can be used to determine the stability of the quenched samples held for an amount of time representative of the normal amount of time between sample collection and analysis. The comparison between the quenched reagent spiked samples and the LRSs can be used to determine if quenching samples adversely affects the analytical performance under controlled conditions. The field study began in 2004 and is continuing today (February 2008) to characterize the effect of quenching on field-matrix spike recoveries and to better understand the potential oxidation and transformation of 277 AOCs. Three types of samples were collected from 11 NAWQA Study Units across the Nation: (1) quenched finished-water samples (not spiked), (2) quenched finished-water spiked samples, and (3) nonquenched finished-water spiked samples. Percent recoveries of AOCs in quenched and nonquenched finished-water spiked samples collected during 2004-06 are presented. Comparisons of percent recoveries between quenched and nonquenched spiked samples can be used to show how quenching affects finished-water samples. A maximum of 6 surface-water and 7 ground-water quenched finished-water spiked samples paired with nonquenched finished-water spiked samples were analyzed. Analytical results for the field study are presented in two ways: (1) by surface-water supplies or ground-water supplies, and (2) by use (or source) group category for surface-water and ground-water supplies. Graphical representations of percent recoveries for the quenched and nonquenched finished-water spiked samples also are presented.

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

Source analysis of MEG activities during sleep (abstract)

NASA Astrophysics Data System (ADS)

Ueno, S.; Iramina, K.

1991-04-01

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

Linking EEG signals, brain functions and mental operations: Advantages of the Laplacian transformation.

PubMed

Vidal, Franck; Burle, Boris; Spieser, Laure; Carbonnell, Laurence; Meckler, Cédric; Casini, Laurence; Hasbroucq, Thierry

2015-09-01

Electroencephalography (EEG) is a very popular technique for investigating brain functions and/or mental processes. To this aim, EEG activities must be interpreted in terms of brain and/or mental processes. EEG signals being a direct manifestation of neuronal activity it is often assumed that such interpretations are quite obvious or, at least, straightforward. However, they often rely on (explicit or even implicit) assumptions regarding the structures supposed to generate the EEG activities of interest. For these assumptions to be used appropriately, reliable links between EEG activities and the underlying brain structures must be established. Because of volume conduction effects and the mixture of activities they induce, these links are difficult to establish with scalp potential recordings. We present different examples showing how the Laplacian transformation, acting as an efficient source separation method, allowed to establish more reliable links between EEG activities and brain generators and, ultimately, with mental operations. The nature of those links depends on the depth of inferences that can vary from weak to strong. Along this continuum, we show that 1) while the effects of experimental manipulation can appear widely distributed with scalp potentials, Laplacian transformation allows to reveal several generators contributing (in different manners) to these modulations, 2) amplitude variations within the same set of generators can generate spurious differences in scalp potential topographies, often interpreted as reflecting different source configurations. In such a case, Laplacian transformation provides much more similar topographies, evidencing the same generator(s) set, and 3) using the LRP as an index of response activation most often produces ambiguous results, Laplacian-transformed response-locked ERPs obtained over motor areas allow resolving these ambiguities. Copyright © 2015 Elsevier B.V. All rights reserved.

Junior temperament character inventory together with quantitative EEG discriminate children with attention deficit hyperactivity disorder combined subtype from children with attention deficit hyperactivity disorder combined subtype plus oppositional defiant disorder.

PubMed

Chiarenza, Giuseppe A; Villa, Stefania; Galan, Lidice; Valdes-Sosa, Pedro; Bosch-Bayard, Jorge

2018-05-19

Oppositional defiant disorder (ODD) is frequently associated with Attention Deficit Hyperactivity Disorder (ADHD) but no clear neurophysiological evidence exists that distinguishes the two groups. Our aim was to identify biomarkers that distinguish children with Attention Deficit Hyperactivity Disorder combined subtype (ADHD_C) from children with ADHD_C + ODD, by combining the results of quantitative EEG (qEEG) and the Junior Temperament Character Inventory (JTCI). 28 ADHD_C and 22 ADHD_C + ODD children who met the DSMV criteria participated in the study. JTCI and EEG were analyzed. Stability based Biomarkers identification methodology was applied to the JTCI and the qEEG separately and combined. The qEEG was tested at the scalp and the sources levels. The classification power of the selected biomarkers was tested with a robust ROC technique. The best discriminant power was obtained when TCI and qEEG were analyzed together. Novelty seeking, self-directedness and cooperativeness were selected as biomarkers together with F4 and Cz in Delta; Fz and F4 in Theta and F7 and F8 in Beta, with a robust AUC of 0.95 for the ROC. At sources level: the regions were the right lateral and medial orbito-frontal cortex, cingular region, angular gyrus, right inferior occipital gyrus, occipital pole and the left insula in Theta, Alpha and Beta. The robust estimate of the total AUC was 0.91. These structures are part of extensive networks of novelty seeking, self-directedness and cooperativeness systems that seem dysregulated in these children. These methods represent an original approach to associate differences of personality and behavior to specific neuronal systems and subsystems. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

[Consciousness and the electroencephalogram].

PubMed

Faber, J; Vladyka, V; Subrt, O

1991-08-01

In the course of 12 years the authors subjected to clinical EEG and stereo-EEG (SEEG) 72 patients (66 epileptics with the diagnosis of psychomotor epilepsy and grand mal) and six psychotic patients suffering from schizophrenia. With the exception of five epileptics and two psychotic patients all subjects had epileptic foci in the amygdalohippocampal complex (AHK). After coagulation of these foci marked improvement of the fits and the mental state occurred in half the patients. During EEG and SEEG recording the authors used different activation methods (hyperventilation through the nose and mouth, sleep, listening to music) and above all direct electric stimulation (ES) of one of the AHK. Secondary epileptic foci had, as a rule, more spikes and a lower threshold for ES than primary ones which contained more delta and slow theta waves. The ES led as a rule to an emotional response, such as anxiety and fear, more rarely to illusions, depersonalization and oneiroid hallucinations and twice to a hedonic response of non-sexual character. The purpose of ES was to assess the site from where it is possible to start the original aura or typical parox. The authors considered these foci, consistent with data in the literature, as the leading focus and it was subsequently coagulated. The authors investigated the reactivity and vigility by the patient's response to sound (the patient had to press a button) and by an interview with the patient. It was revealed that in isolated discharges of the spikes and waves in the scalp electrodes, i.e. in the neocortex, reactivity is lacking. In isolated discharges in the AHK the reactivity was satisfactory, but as a rule anxiety developed. It is thus possible to divide consciousness into emotional consciousness with its site in the AHK, i.e. in the limbic system, and rational consciousness which is a function of the neocrotical system. Congenital changes of consciousness such as vigility or sleep are described as "states" of consciousness. The rational or emotional aspect of behaviour is described as "type" of consciousness. Under normal conditions the states of consciousness alternate periodically and are sharply defined, the types of consciousness are closely linked and are difficult to separate. Under pathological conditions the "states" of consciousness differ less markedly and the "types" of consciousness are in dissociation. Thus obnubilation, depersonalization, illusions, pathic affects etc. develop, as a rule as part of the epileptiform or psychotiform syndrome.

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.

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.

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.

Deblurring

NASA Technical Reports Server (NTRS)

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

1999-01-01

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

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.

Seizure-onset patterns in focal cortical dysplasia and neurodevelopmental tumors: Relationship with surgical prognosis and neuropathologic subtypes.

PubMed

Lagarde, Stanislas; Bonini, Francesca; McGonigal, Aileen; Chauvel, Patrick; Gavaret, Martine; Scavarda, Didier; Carron, Romain; Régis, Jean; Aubert, Sandrine; Villeneuve, Nathalie; Giusiano, Bernard; Figarella-Branger, Dominique; Trebuchon, Agnès; Bartolomei, Fabrice

2016-09-01

The study of intracerebral electroencephalography (EEG) seizure-onset patterns is crucial to accurately define the epileptogenic zone and guide successful surgical resection. It also raises important pathophysiologic issues concerning mechanisms of seizure generation. Until now, several seizure-onset patterns have been described using distinct recording methods (subdural, depth electrode), mostly in temporal lobe epilepsies or with heterogeneous neocortical lesions. We analyzed data from a cohort of 53 consecutive patients explored by stereoelectroencephalography (SEEG) and with pathologically confirmed malformation of cortical development (MCD; including focal cortical dysplasia [FCD] and neurodevelopmental tumors [NDTs]). We identified six seizure-onset patterns using visual and time-frequency analysis: low-voltage fast activity (LVFA); preictal spiking followed by LVFA; burst of polyspikes followed by LVFA; slow wave/DC shift followed by LVFA; theta/alpha sharp waves; and rhythmic spikes/spike-waves. We found a high prevalence of patterns that included LVFA (83%), indicating nevertheless that LVFA is not a constant characteristic of seizure onset. An association between seizure-onset patterns and histologic types was found (p = 001). The more prevalent patterns were as follows: (1) in FCD type I LVFA (23.1%) and slow wave/baseline shift followed by LVFA (15.4%); (2) in FCD type II burst of polyspikes followed by LVFA (31%), LVFA (27.6%), and preictal spiking followed by LVFA (27.6%); (3) in NDT, LVFA (54.5%). We found that a seizure-onset pattern that included LVFA was associated with favorable postsurgical outcome, but the completeness of the EZ resection was the sole independent predictive variable. Six different seizure-onset patterns can be described in FCD and NDT. Better postsurgical outcome is associated with patterns that incorporate LVFA. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

Lacosamide modulates interictal spiking and high-frequency oscillations in a model of mesial temporal lobe epilepsy

PubMed Central

Behr, Charles; Lévesque, Maxime; Ragsdale, David; Avoli, Massimo

2016-01-01

Objective Nearly one third of patients presenting with mesial temporal lobe epilepsy (MTLE), the most prevalent lesion-related epileptic disorder in adulthood, do not respond to currently available antiepileptic medications. Thus, there is a need to identify and characterize new antiepileptic drugs. In this study, we used the pilocarpine model of MTLE to establish the effects of a third generation drug, lacosamide (LCM), on seizures, interictal spikes and high-frequency oscillations (HFOs, ripples: 80–200 Hz, fast ripples: 250–500 Hz). Methods Sprague–Dawley rats (250–300 g) were injected with pilocarpine to induce a status epilepticus (SE) that was pharmacologically terminated after 1 h. Eight pilocarpine-treated rats were then injected with LCM (30 mg/kg, i.p.) 4 h after SE and daily for 14 days. Eight pilocarpine-treated rats were used as controls and treated with saline. Three days after SE, all rats were implanted with bipolar electrodes in the hippocampal CA3 region, entorhinal cortex (EC), dentate gyrus (DG) and subiculum and EEG-video monitored from day 4 to day 14 after SE. Results LCM-treated animals showed lower rates of seizures (0.21 (±0.11) seizures/day) than controls (2.6 (±0.57), p < 0.05), and a longer latent period (LCM: 11 (±1) days, controls: 6.25 (±1), p < 0.05). Rates of interictal spikes in LCM-treated rats were significantly lower than in controls in CA3 and subiculum (p < 0.05). Rates of ripples and fast ripples associated with interictal spikes in CA3 and subiculum as well as rates of fast ripples occurring outside of interictal spikes in CA3 were also significantly lower in LCM-treated animals. In controls, interictal spikes and associated HFOs correlated to seizure clustering, while this was not the case for isolated HFOs. Significance Our findings show that early treatment with LCM has powerful anti-ictogenic properties in the pilocarpine model of MTLE. These effects are accompanied by decreased rates of interictal spikes and associated HFOs. Isolated HFOs were also modulated by LCM, in a manner that appeared to be unrelated to its antiictogenic effects. These results thus suggest that distinct mechanisms may underlie interictal-associated and isolated HFOs in the pilocarpine model of MTLE. PMID:26220372

Galactic center gamma-ray excess from dark matter annihilation: is there a black hole spike?

PubMed

Fields, Brian D; Shapiro, Stuart L; Shelton, Jessie

2014-10-10

If the supermassive black hole Sgr A* at the center of the Milky Way grew adiabatically from an initial seed embedded in a Navarro-Frenk-White dark matter (DM) halo, then the DM profile near the hole has steepened into a spike. We calculate the dramatic enhancement to the gamma-ray flux from the Galactic center (GC) from such a spike if the 1-3 GeV excess observed in Fermi data is due to DM annihilations. We find that for the parameter values favored in recent fits, the point-source-like flux from the spike is 35 times greater than the flux from the inner 1° of the halo, far exceeding all Fermi point source detections near the GC. We consider the dependence of the spike signal on astrophysical and particle parameters and conclude that if the GC excess is due to DM, then a canonical adiabatic spike is disfavored by the data. We discuss alternative Galactic histories that predict different spike signals, including (i) the nonadiabatic growth of the black hole, possibly associated with halo and/or black hole mergers, (ii) gravitational interaction of DM with baryons in the dense core, such as heating by stars, or (iii) DM self-interactions. We emphasize that the spike signal is sensitive to a different combination of particle parameters than the halo signal and that the inclusion of a spike component to any DM signal in future analyses would provide novel information about both the history of the GC and the particle physics of DM annihilations.

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

PubMed

Sai, Chong Yeh; Mokhtar, Norrima; Arof, Hamzah; Cumming, Paul; Iwahashi, Masahiro

2018-05-01

Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pretrained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy, and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multichannel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

fMRI activation patterns in an analytic reasoning task: consistency with EEG source localization

NASA Astrophysics Data System (ADS)

Li, Bian; Vasanta, Kalyana C.; O'Boyle, Michael; Baker, Mary C.; Nutter, Brian; Mitra, Sunanda

2010-03-01

Functional magnetic resonance imaging (fMRI) is used to model brain activation patterns associated with various perceptual and cognitive processes as reflected by the hemodynamic (BOLD) response. While many sensory and motor tasks are associated with relatively simple activation patterns in localized regions, higher-order cognitive tasks may produce activity in many different brain areas involving complex neural circuitry. We applied a recently proposed probabilistic independent component analysis technique (PICA) to determine the true dimensionality of the fMRI data and used EEG localization to identify the common activated patterns (mapped as Brodmann areas) associated with a complex cognitive task like analytic reasoning. Our preliminary study suggests that a hybrid GLM/PICA analysis may reveal additional regions of activation (beyond simple GLM) that are consistent with electroencephalography (EEG) source localization patterns.

Causal Inference and Explaining Away in a Spiking Network

PubMed Central

Moreno-Bote, Rubén; Drugowitsch, Jan

2015-01-01

While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification. PMID:26621426

Causal Inference and Explaining Away in a Spiking Network.

PubMed

Moreno-Bote, Rubén; Drugowitsch, Jan

2015-12-01

While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification.

Current source density correlates of cerebellar Golgi and Purkinje cell responses to tactile input

PubMed Central

Tahon, Koen; Wijnants, Mike; De Schutter, Erik

2011-01-01

The overall circuitry of the cerebellar cortex has been known for over a century, but the function of many synaptic connections remains poorly characterized in vivo. We used a one-dimensional multielectrode probe to estimate the current source density (CSD) of Crus IIa in response to perioral tactile stimuli in anesthetized rats and to correlate current sinks and sources to changes in the spike rate of corecorded Golgi and Purkinje cells. The punctate stimuli evoked two distinct early waves of excitation (at <10 and ∼20 ms) associated with current sinks in the granular layer. The second wave was putatively of corticopontine origin, and its associated sink was located higher in the granular layer than the first trigeminal sink. The distinctive patterns of granular-layer sinks correlated with the spike responses of corecorded Golgi cells. In general, Golgi cell spike responses could be linearly reconstructed from the CSD profile. A dip in simple-spike activity of coregistered Purkinje cells correlated with a current source deep in the molecular layer, probably generated by basket cell synapses, interspersed between sparse early sinks presumably generated by synapses from granule cells. The late (>30 ms) enhancement of simple-spike activity in Purkinje cells was characterized by the absence of simultaneous sinks in the granular layer and by the suppression of corecorded Golgi cell activity, pointing at inhibition of Golgi cells by Purkinje axon collaterals as a likely mechanism of late Purkinje cell excitation. PMID:21228303

Continuous high-frequency activity in mesial temporal lobe structures

PubMed Central

Mari, Francesco; Zelmann, Rina; Andrade-Valenca, Luciana; Dubeau, Francois; Gotman, Jean

2013-01-01

Summary Purpose Many recent studies have reported the importance of high-frequency oscillations (HFOs) in the intracerebral electroencephalography (EEG) of patients with epilepsy. These HFOs have been defined as events that stand out from the background. We have noticed that this background often consists itself of high-frequency rhythmic activity. The purpose of this study is to perform a first evaluation of the characteristics of high-frequency continuous or semicontinuous background activity. Methods Because the continuous high-frequency pattern was noted mainly in mesial temporal structures, we reviewed the EEG studies from these structures in 24 unselected patients with electrodes implanted in these regions. Sections of background away from interictal spikes were marked visually during periods of slow-wave sleep and wakefulness. They were then high-passed filtered at 80 Hz and categorized as having high-frequency rhythmic activity in one of three patterns: continuous/semicontinuous, irregular, sporadic. Wavelet entropy, which measures the degree of rhythmicity of a signal, was calculated for the marked background sections. Key Findings Ninety-six bipolar channels were analyzed. The continuous/semicontinuous pattern was found frequently (29/96 channels during wake and 34/96 during sleep). The different patterns were consistent between sleep and wakefulness. The continuous/semicontinuous pattern was found significantly more often in the hippocampus than in the parahippocampal gyrus and was rarely found in the amygdala. The types of pattern were not influenced by whether a channel was within the seizure-onset zone, or whether it was a lesional channel. The continuous/semicontinuous pattern was associated with a higher frequency of spikes and with high rates of ripples and fast ripples. Significance It appears that high-frequency activity (above 80 Hz) does not appear only in the form of brief paroxysmal events but also in the form of continuous rhythmic activity or very long bursts. In this study limited to mesial temporal structures, we found a clear anatomic preference for the hippocampus. Although associated with spikes and with distinct HFOs, this pattern was not clearly associated with the seizure-onset zone. Future studies will need to evaluate systematically the presence of this pattern, as it may have a pathophysiologic significance and it will also have an important influence on the very definition of HFOs. PMID:22416973

Recent progress in multi-electrode spike sorting methods

PubMed Central

Lefebvre, Baptiste; Yger, Pierre; Marre, Olivier

2017-01-01

In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms. PMID:28263793

Convex relaxations of spectral sparsity for robust super-resolution and line spectrum estimation

NASA Astrophysics Data System (ADS)

Chi, Yuejie

2017-08-01

We consider recovering the amplitudes and locations of spikes in a point source signal from its low-pass spectrum that may suffer from missing data and arbitrary outliers. We first review and provide a unified view of several recently proposed convex relaxations that characterize and capitalize the spectral sparsity of the point source signal without discretization under the framework of atomic norms. Next we propose a new algorithm when the spikes are known a priori to be positive, motivated by applications such as neural spike sorting and fluorescence microscopy imaging. Numerical experiments are provided to demonstrate the effectiveness of the proposed approach.

A three domain covariance framework for EEG/MEG data.

PubMed

Roś, Beata P; Bijma, Fetsje; de Gunst, Mathisca C M; de Munck, Jan C

2015-10-01

In this paper we introduce a covariance framework for the analysis of single subject EEG and MEG data that takes into account observed temporal stationarity on small time scales and trial-to-trial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three components that correspond to space, time and epochs/trials, and consider maximum likelihood estimation of the unknown parameter values. An iterative algorithm that finds approximations of the maximum likelihood estimates is proposed. Our covariance model is applicable in a variety of cases where spontaneous EEG or MEG acts as source of noise and realistic noise covariance estimates are needed, such as in evoked activity studies, or where the properties of spontaneous EEG or MEG are themselves the topic of interest, like in combined EEG-fMRI experiments in which the correlation between EEG and fMRI signals is investigated. We use a simulation study to assess the performance of the estimator and investigate the influence of different assumptions about the covariance factors on the estimated covariance matrix and on its components. We apply our method to real EEG and MEG data sets. Copyright © 2015 Elsevier Inc. All rights reserved.

Bluetooth Communication Interface for EEG Signal Recording in Hyperbaric Chambers.

PubMed

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

2015-07-01

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

Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics.

PubMed

Chen, Xun; Liu, Aiping; Chen, Qiang; Liu, Yu; Zou, Liang; McKeown, Martin J

2017-09-01

Electroencephalography (EEG) recordings are frequently contaminated by both ocular and muscle artifacts. These are normally dealt with separately, by employing blind source separation (BSS) techniques relying on either second-order or higher-order statistics (SOS & HOS respectively). When HOS-based methods are used, it is usually in the setting of assuming artifacts are statistically independent to the EEG. When SOS-based methods are used, it is assumed that artifacts have autocorrelation characteristics distinct from the EEG. In reality, ocular and muscle artifacts do not completely follow the assumptions of strict temporal independence to the EEG nor completely unique autocorrelation characteristics, suggesting that exploiting HOS or SOS alone may be insufficient to remove these artifacts. Here we employ a novel BSS technique, independent vector analysis (IVA), to jointly employ HOS and SOS simultaneously to remove ocular and muscle artifacts. Numerical simulations and application to real EEG recordings were used to explore the utility of the IVA approach. IVA was superior in isolating both ocular and muscle artifacts, especially for raw EEG data with low signal-to-noise ratio, and also integrated usually separate SOS and HOS steps into a single unified step. Copyright © 2017 Elsevier Ltd. All rights reserved.

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

NASA Astrophysics Data System (ADS)

Mishra, Puneet; Singla, Sunil Kumar

2013-01-01

In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.

Propofol Anesthesia and Sleep: A High-Density EEG Study

PubMed Central

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

2011-01-01

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

Reversing pathologically increased EEG power by acoustic coordinated reset neuromodulation

PubMed Central

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

2014-01-01

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

Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models

PubMed Central

Paz-Linares, Deirel; Vega-Hernández, Mayrim; Rojas-López, Pedro A.; Valdés-Hernández, Pedro A.; Martínez-Montes, Eduardo; Valdés-Sosa, Pedro A.

2017-01-01

The estimation of EEG generating sources constitutes an Inverse Problem (IP) in Neuroscience. This is an ill-posed problem due to the non-uniqueness of the solution and regularization or prior information is needed to undertake Electrophysiology Source Imaging. Structured Sparsity priors can be attained through combinations of (L1 norm-based) and (L2 norm-based) constraints such as the Elastic Net (ENET) and Elitist Lasso (ELASSO) models. The former model is used to find solutions with a small number of smooth nonzero patches, while the latter imposes different degrees of sparsity simultaneously along different dimensions of the spatio-temporal matrix solutions. Both models have been addressed within the penalized regression approach, where the regularization parameters are selected heuristically, leading usually to non-optimal and computationally expensive solutions. The existing Bayesian formulation of ENET allows hyperparameter learning, but using the computationally intensive Monte Carlo/Expectation Maximization methods, which makes impractical its application to the EEG IP. While the ELASSO have not been considered before into the Bayesian context. In this work, we attempt to solve the EEG IP using a Bayesian framework for ENET and ELASSO models. We propose a Structured Sparse Bayesian Learning algorithm based on combining the Empirical Bayes and the iterative coordinate descent procedures to estimate both the parameters and hyperparameters. Using realistic simulations and avoiding the inverse crime we illustrate that our methods are able to recover complicated source setups more accurately and with a more robust estimation of the hyperparameters and behavior under different sparsity scenarios than classical LORETA, ENET and LASSO Fusion solutions. We also solve the EEG IP using data from a visual attention experiment, finding more interpretable neurophysiological patterns with our methods. The Matlab codes used in this work, including Simulations, Methods, Quality Measures and Visualization Routines are freely available in a public website. PMID:29200994

Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models.

PubMed

Paz-Linares, Deirel; Vega-Hernández, Mayrim; Rojas-López, Pedro A; Valdés-Hernández, Pedro A; Martínez-Montes, Eduardo; Valdés-Sosa, Pedro A

2017-01-01

The estimation of EEG generating sources constitutes an Inverse Problem (IP) in Neuroscience. This is an ill-posed problem due to the non-uniqueness of the solution and regularization or prior information is needed to undertake Electrophysiology Source Imaging. Structured Sparsity priors can be attained through combinations of (L1 norm-based) and (L2 norm-based) constraints such as the Elastic Net (ENET) and Elitist Lasso (ELASSO) models. The former model is used to find solutions with a small number of smooth nonzero patches, while the latter imposes different degrees of sparsity simultaneously along different dimensions of the spatio-temporal matrix solutions. Both models have been addressed within the penalized regression approach, where the regularization parameters are selected heuristically, leading usually to non-optimal and computationally expensive solutions. The existing Bayesian formulation of ENET allows hyperparameter learning, but using the computationally intensive Monte Carlo/Expectation Maximization methods, which makes impractical its application to the EEG IP. While the ELASSO have not been considered before into the Bayesian context. In this work, we attempt to solve the EEG IP using a Bayesian framework for ENET and ELASSO models. We propose a Structured Sparse Bayesian Learning algorithm based on combining the Empirical Bayes and the iterative coordinate descent procedures to estimate both the parameters and hyperparameters. Using realistic simulations and avoiding the inverse crime we illustrate that our methods are able to recover complicated source setups more accurately and with a more robust estimation of the hyperparameters and behavior under different sparsity scenarios than classical LORETA, ENET and LASSO Fusion solutions. We also solve the EEG IP using data from a visual attention experiment, finding more interpretable neurophysiological patterns with our methods. The Matlab codes used in this work, including Simulations, Methods, Quality Measures and Visualization Routines are freely available in a public website.

Feasibility of imaging epileptic seizure onset with EIT and depth electrodes.

PubMed

Witkowska-Wrobel, Anna; Aristovich, Kirill; Faulkner, Mayo; Avery, James; Holder, David

2018-06-01

Imaging ictal and interictal activity with Electrical Impedance Tomography (EIT) using intracranial electrode mats has been demonstrated in animal models of epilepsy. In human epilepsy subjects undergoing presurgical evaluation, depth electrodes are often preferred. The purpose of this work was to evaluate the feasibility of using EIT to localise epileptogenic areas with intracranial electrodes in humans. The accuracy of localisation of the ictal onset zone was evaluated in computer simulations using 9M element FEM models derived from three subjects. 5 mm radius perturbations imitating a single seizure onset event were placed in several locations forming two groups: under depth electrode coverage and in the contralateral hemisphere. Simulations were made for impedance changes of 1% expected for neuronal depolarisation over milliseconds and 10% for cell swelling over seconds. Reconstructions were compared with EEG source modelling for a radially orientated dipole with respect to the closest EEG recording contact. The best accuracy of EIT was obtained using all depth and 32 scalp electrodes, greater than the equivalent accuracy with EEG inverse source modelling. The localisation error was 5.2 ± 1.8, 4.3 ± 0 and 46.2 ± 25.8 mm for perturbations within the volume enclosed by depth electrodes and 29.6 ± 38.7, 26.1 ± 36.2, 54.0 ± 26.2 mm for those without (EIT 1%, 10% change, EEG source modelling, n = 15 in 3 subjects, p < 0.01). As EIT was insensitive to source dipole orientation, all 15 perturbations within the volume enclosed by depth electrodes were localised, whereas the standard clinical method of visual inspection of EEG voltages, only localised 8 out of 15 cases. This suggests that adding EIT to SEEG measurements could be beneficial in localising the onset of seizures. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Localization of synchronous cortical neural sources.

PubMed

Zerouali, Younes; Herry, Christophe L; Jemel, Boutheina; Lina, Jean-Marc

2013-03-01

Neural synchronization is a key mechanism to a wide variety of brain functions, such as cognition, perception, or memory. High temporal resolution achieved by EEG recordings allows the study of the dynamical properties of synchronous patterns of activity at a very fine temporal scale but with very low spatial resolution. Spatial resolution can be improved by retrieving the neural sources of EEG signal, thus solving the so-called inverse problem. Although many methods have been proposed to solve the inverse problem and localize brain activity, few of them target the synchronous brain regions. In this paper, we propose a novel algorithm aimed at localizing specifically synchronous brain regions and reconstructing the time course of their activity. Using multivariate wavelet ridge analysis, we extract signals capturing the synchronous events buried in the EEG and then solve the inverse problem on these signals. Using simulated data, we compare results of source reconstruction accuracy achieved by our method to a standard source reconstruction approach. We show that the proposed method performs better across a wide range of noise levels and source configurations. In addition, we applied our method on real dataset and identified successfully cortical areas involved in the functional network underlying visual face perception. We conclude that the proposed approach allows an accurate localization of synchronous brain regions and a robust estimation of their activity.

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

NASA Astrophysics Data System (ADS)

Almurshedi, Ahmed; Ismail, Abd Khamim

2015-04-01

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

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

PubMed

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

2011-05-01

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

Dissociation between sustained single-neuron spiking and transient β-LFP oscillations in primate motor cortex

PubMed Central

Rule, Michael E.; Vargas-Irwin, Carlos E.; Donoghue, John P.

2017-01-01

Determining the relationship between single-neuron spiking and transient (20 Hz) β-local field potential (β-LFP) oscillations is an important step for understanding the role of these oscillations in motor cortex. We show that whereas motor cortex firing rates and beta spiking rhythmicity remain sustained during steady-state movement preparation periods, β-LFP oscillations emerge, in contrast, as short transient events. Single-neuron mean firing rates within and outside transient β-LFP events showed no differences, and no consistent correlation was found between the beta oscillation amplitude and firing rates, as was the case for movement- and visual cue-related β-LFP suppression. Importantly, well-isolated single units featuring beta-rhythmic spiking (43%, 125/292) showed no apparent or only weak phase coupling with the transient β-LFP oscillations. Similar results were obtained for the population spiking. These findings were common in triple microelectrode array recordings from primary motor (M1), ventral (PMv), and dorsal premotor (PMd) cortices in nonhuman primates during movement preparation. Although beta spiking rhythmicity indicates strong membrane potential fluctuations in the beta band, it does not imply strong phase coupling with β-LFP oscillations. The observed dissociation points to two different sources of variation in motor cortex β-LFPs: one that impacts single-neuron spiking dynamics and another related to the generation of mesoscopic β-LFP signals. Furthermore, our findings indicate that rhythmic spiking and diverse neuronal firing rates, which encode planned actions during movement preparation, may naturally limit the ability of different neuronal populations to strongly phase-couple to a single dominant oscillation frequency, leading to the observed spiking and β-LFP dissociation. NEW & NOTEWORTHY We show that whereas motor cortex spiking rates and beta (~20 Hz) spiking rhythmicity remain sustained during steady-state movement preparation periods, β-local field potential (β-LFP) oscillations emerge, in contrast, as transient events. Furthermore, the β-LFP phase at which neurons spike drifts: phase coupling is typically weak or absent. This dissociation points to two sources of variation in the level of motor cortex beta: one that impacts single-neuron spiking and another related to the generation of measured mesoscopic β-LFPs. PMID:28100654

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

PubMed

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

2006-04-01

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

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

PubMed

Petrov, Yury; Sridhar, Srinivas

2013-01-01

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

Brain electric correlates of strong belief in paranormal phenomena: intracerebral EEG source and regional Omega complexity analyses.

PubMed

Pizzagalli, D; Lehmann, D; Gianotti, L; Koenig, T; Tanaka, H; Wackermann, J; Brugger, P

2000-12-22

The neurocognitive processes underlying the formation and maintenance of paranormal beliefs are important for understanding schizotypal ideation. Behavioral studies indicated that both schizotypal and paranormal ideation are based on an overreliance on the right hemisphere, whose coarse rather than focussed semantic processing may favor the emergence of 'loose' and 'uncommon' associations. To elucidate the electrophysiological basis of these behavioral observations, 35-channel resting EEG was recorded in pre-screened female strong believers and disbelievers during resting baseline. EEG data were subjected to FFT-Dipole-Approximation analysis, a reference-free frequency-domain dipole source modeling, and Regional (hemispheric) Omega Complexity analysis, a linear approach estimating the complexity of the trajectories of momentary EEG map series in state space. Compared to disbelievers, believers showed: more right-located sources of the beta2 band (18.5-21 Hz, excitatory activity); reduced interhemispheric differences in Omega complexity values; higher scores on the Magical Ideation scale; more general negative affect; and more hypnagogic-like reveries after a 4-min eyes-closed resting period. Thus, subjects differing in their declared paranormal belief displayed different active, cerebral neural populations during resting, task-free conditions. As hypothesized, believers showed relatively higher right hemispheric activation and reduced hemispheric asymmetry of functional complexity. These markers may constitute the neurophysiological basis for paranormal and schizotypal ideation.

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

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.

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.

FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data.

PubMed

Oostenveld, Robert; Fries, Pascal; Maris, Eric; Schoffelen, Jan-Mathijs

2011-01-01

This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.

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

PubMed

Puskás, Szilvia

2011-03-30

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

Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module.

PubMed

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

2015-08-01

An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p <; 0.05).

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

PubMed

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

2016-08-30

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

A novel method for device-related electroencephalography artifact suppression to explore cochlear implant-related cortical changes in single-sided deafness.

PubMed

Kim, Kyungsoo; Punte, Andrea Kleine; Mertens, Griet; Van de Heyning, Paul; Park, Kyung-Joon; Choi, Hongsoo; Choi, Ji-Woong; Song, Jae-Jin

2015-11-30

Quantitative electroencephalography (qEEG) is effective when used to analyze ongoing cortical oscillations in cochlear implant (CI) users. However, localization of cortical activity in such users via qEEG is confounded by the presence of artifacts produced by the device itself. Typically, independent component analysis (ICA) is used to remove CI artifacts in auditory evoked EEG signals collected upon brief stimulation and it is effective for auditory evoked potentials (AEPs). However, AEPs do not reflect the daily environments of patients, and thus, continuous EEG data that are closer to such environments are desirable. In this case, device-related artifacts in EEG data are difficult to remove selectively via ICA due to over-completion of EEG data removal in the absence of preprocessing. EEGs were recorded for a long time under conditions of continuous auditory stimulation. To obviate the over-completion problem, we limited the frequency of CI artifacts to a significant characteristic peak and apply ICA artifact removal. Topographic brain mapping results analyzed via band-limited (BL)-ICA exhibited a better energy distribution, matched to the CI location, than data obtained using conventional ICA. Also, source localization data verified that BL-ICA effectively removed CI artifacts. The proposed method selectively removes CI artifacts from continuous EEG recordings, while ICA removal method shows residual peak and removes important brain activity signals. CI artifacts in EEG data obtained during continuous passive listening can be effectively removed with the aid of BL-ICA, opening up new EEG research possibilities in subjects with CIs. Copyright © 2015 Elsevier B.V. All rights reserved.

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

Recent progress in multi-electrode spike sorting methods.

PubMed

Lefebvre, Baptiste; Yger, Pierre; Marre, Olivier

2016-11-01

In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparison of different Kalman filter approaches in deriving time varying connectivity from EEG data.

PubMed

Ghumare, Eshwar; Schrooten, Maarten; Vandenberghe, Rik; Dupont, Patrick

2015-08-01

Kalman filter approaches are widely applied to derive time varying effective connectivity from electroencephalographic (EEG) data. For multi-trial data, a classical Kalman filter (CKF) designed for the estimation of single trial data, can be implemented by trial-averaging the data or by averaging single trial estimates. A general linear Kalman filter (GLKF) provides an extension for multi-trial data. In this work, we studied the performance of the different Kalman filtering approaches for different values of signal-to-noise ratio (SNR), number of trials and number of EEG channels. We used a simulated model from which we calculated scalp recordings. From these recordings, we estimated cortical sources. Multivariate autoregressive model parameters and partial directed coherence was calculated for these estimated sources and compared with the ground-truth. The results showed an overall superior performance of GLKF except for low levels of SNR and number of trials.

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA.

PubMed

Labounek, René; Bridwell, David A; Mareček, Radek; Lamoš, Martin; Mikl, Michal; Slavíček, Tomáš; Bednařík, Petr; Baštinec, Jaromír; Hluštík, Petr; Brázdil, Milan; Jan, Jiří

2018-01-01

Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.

Truncated RAP-MUSIC (TRAP-MUSIC) for MEG and EEG source localization.

PubMed

Mäkelä, Niko; Stenroos, Matti; Sarvas, Jukka; Ilmoniemi, Risto J

2018-02-15

Electrically active brain regions can be located applying MUltiple SIgnal Classification (MUSIC) on magneto- or electroencephalographic (MEG; EEG) data. We introduce a new MUSIC method, called truncated recursively-applied-and-projected MUSIC (TRAP-MUSIC). It corrects a hidden deficiency of the conventional RAP-MUSIC algorithm, which prevents estimation of the true number of brain-signal sources accurately. The correction is done by applying a sequential dimension reduction to the signal-subspace projection. We show that TRAP-MUSIC significantly improves the performance of MUSIC-type localization; in particular, it successfully and robustly locates active brain regions and estimates their number. We compare TRAP-MUSIC and RAP-MUSIC in simulations with varying key parameters, e.g., signal-to-noise ratio, correlation between source time-courses, and initial estimate for the dimension of the signal space. In addition, we validate TRAP-MUSIC with measured MEG data. We suggest that with the proposed TRAP-MUSIC method, MUSIC-type localization could become more reliable and suitable for various online and offline MEG and EEG applications. Copyright © 2017 Elsevier Inc. All rights reserved.

Recurrent seizures during acute acquired toxoplasmosis in an immunocompetent traveller returning from Africa.

PubMed

Beltrame, Anna; Venturini, Sergio; Crichiutti, Giovanni; Meroni, Valeria; Buonfrate, Dora; Bassetti, Matteo

2016-04-01

We report an unusual case of acute acquired toxoplasmosis (AAT) presenting as lymphadenopathy and recurrent seizures in an immunocompetent 15-year-old boy. The patient reported an 18-day vacation to Africa (Ethiopia), 39 days prior to the first seizure. Electroencephalogram (EEG) showed sporadic single-spike or sharp-wave paroxysms and the magnetic resonance imaging (RMI) of the brain was negative. The serology for T. gondii was compatible with an acute infection defined as positive for both toxoplasma-specific IgG and IgM and a low avidity (6 %), confirmed by a reference laboratory. The patient reported other two episodes of seizures, occurring 7 days apart. He was treated with pyrimethamine plus sulfadiazine and leucovorin for 4 weeks, with an improvement of lymphadenitis and normalization of EEG. After 5 months, new seizures were reported and a diagnosis of epilepsy was done. Toxoplasma polymerase chain reaction (PCR) of cerebrospinal fluid (CSF) and blood were negative. A treatment with valproic acid was started, obtaining control of the neurological disease. Awareness of this neurologic manifestation by clinicians is required, also in immunocompetent patients. The relationship between toxoplasmosis and recurrent seizure needs to be investigated by new studies.

Advanced correlation grid: Analysis and visualisation of functional connectivity among multiple spike trains.

PubMed

Masud, Mohammad Shahed; Borisyuk, Roman; Stuart, Liz

2017-07-15

This study analyses multiple spike trains (MST) data, defines its functional connectivity and subsequently visualises an accurate diagram of connections. This is a challenging problem. For example, it is difficult to distinguish the common input and the direct functional connection of two spike trains. The new method presented in this paper is based on the traditional pairwise cross-correlation function (CCF) and a new combination of statistical techniques. First, the CCF is used to create the Advanced Correlation Grid (ACG) correlation where both the significant peak of the CCF and the corresponding time delay are used for detailed analysis of connectivity. Second, these two features of functional connectivity are used to classify connections. Finally, the visualization technique is used to represent the topology of functional connections. Examples are presented in the paper to demonstrate the new Advanced Correlation Grid method and to show how it enables discrimination between (i) influence from one spike train to another through an intermediate spike train and (ii) influence from one common spike train to another pair of analysed spike trains. The ACG method enables scientists to automatically distinguish between direct connections from spurious connections such as common source connection and indirect connection whereas existing methods require in-depth analysis to identify such connections. The ACG is a new and effective method for studying functional connectivity of multiple spike trains. This method can identify accurately all the direct connections and can distinguish common source and indirect connections automatically. Copyright © 2017 Elsevier B.V. All rights reserved.

Wedge MUSIC: a novel approach to examine experimental differences of brain source connectivity patterns from EEG/MEG data.

PubMed

Ewald, Arne; Avarvand, Forooz Shahbazi; Nolte, Guido

2014-11-01

We introduce a novel method to estimate bivariate synchronization, i.e. interacting brain sources at a specific frequency or band, from MEG or EEG data robust to artifacts of volume conduction. The data driven calculation is solely based on the imaginary part of the cross-spectrum as opposed to the imaginary part of coherency. In principle, the method quantifies how strong a synchronization between a distinct pair of brain sources is present in the data. As an input of the method all pairs of pre-defined locations inside the brain can be used which is computationally exhaustive. In contrast to that, reference sources can be used that have been identified by any source reconstruction technique in a prior analysis step. We introduce different variants of the method and evaluate the performance in simulations. As a particular advantage of the proposed methodology, we demonstrate that the novel approach is capable of investigating differences in brain source interactions between experimental conditions or with respect to a certain baseline. For measured data, we first show the application on resting state MEG data where we find locally synchronized sources in the motor-cortex based on the sensorimotor idle rhythms. Finally, we show an example on EEG motor imagery data where we contrast hand and foot movements. Here, we also find local interactions in the expected brain areas. Copyright © 2014. Published by Elsevier Inc.

Disentangling Neural Sources of the Motor Interference Effect in High Functioning Autism: An EEG-Study

ERIC Educational Resources Information Center

Deschrijver, Eliane; Wiersema, Jan R.; Brass, Marcel

2017-01-01

The role of imitation in autism spectrum disorder (ASD) is controversial. Researchers have argued that deficient control of self- and other-related motor representations (self-other distinction) might explain imitation difficulties. In a recent EEG study, we showed that control of imitation relies on high-level as well as on low-level cognitive…

ERP denoising in multichannel EEG data using contrasts between signal and noise subspaces.

PubMed

Ivannikov, Andriy; Kalyakin, Igor; Hämäläinen, Jarmo; Leppänen, Paavo H T; Ristaniemi, Tapani; Lyytinen, Heikki; Kärkkäinen, Tommi

2009-06-15

In this paper, a new method intended for ERP denoising in multichannel EEG data is discussed. The denoising is done by separating ERP/noise subspaces in multidimensional EEG data by a linear transformation and the following dimension reduction by ignoring noise components during inverse transformation. The separation matrix is found based on the assumption that ERP sources are deterministic for all repetitions of the same type of stimulus within the experiment, while the other noise sources do not obey the determinancy property. A detailed derivation of the technique is given together with the analysis of the results of its application to a real high-density EEG data set. The interpretation of the results and the performance of the proposed method under conditions, when the basic assumptions are violated - e.g. the problem is underdetermined - are also discussed. Moreover, we study how the factors of the number of channels and trials used by the method influence the effectiveness of ERP/noise subspaces separation. In addition, we explore also the impact of different data resampling strategies on the performance of the considered algorithm. The results can help in determining the optimal parameters of the equipment/methods used to elicit and reliably estimate ERPs.

Negative BOLD in default-mode structures measured with EEG-MREG is larger in temporal than extra-temporal epileptic spikes

PubMed Central

Jacobs, Julia; Menzel, Antonia; Ramantani, Georgia; Körbl, Katharina; Assländer, Jakob; Schulze-Bonhage, Andreas; Hennig, Jürgen; LeVan, Pierre

2014-01-01

Introduction: EEG-fMRI detects BOLD changes associated with epileptic interictal discharges (IED) and can identify epileptogenic networks in epilepsy patients. Besides positive BOLD changes, negative BOLD changes have sometimes been observed in the default-mode network, particularly using group analysis. A new fast fMRI sequence called MREG (Magnetic Resonance Encephalography) shows increased sensitivity to detect IED-related BOLD changes compared to the conventional EPI sequence, including frequent occurrence of negative BOLD responses in the DMN. The present study quantifies the concordance between the DMN and negative BOLD related to IEDs of temporal and extra-temporal origin. Methods: Focal epilepsy patients underwent simultaneous EEG-MREG. Areas of overlap were calculated between DMN regions, defined as precuneus, posterior cingulate, bilateral inferior parietal and mesial prefrontal cortices according to a standardized atlas, and significant negative BOLD changes revealed by an event-related analysis based on the timings of IED seen on EEG. Correlation between IED number/lobe of origin and the overlap were calculated. Results: 15 patients were analyzed, some showing IED over more than one location resulting in 30 different IED types. The average overlap between negative BOLD and DMN was significantly larger in temporal (23.7 ± 19.6 cm3) than extra-temporal IEDs (7.4 ± 5.1 cm3, p = 0.008). There was no significant correlation between the number of IEDs and the overlap between DMN structures and negative BOLD areas. Discussion: MREG results in an increased sensitivity to detect negative BOLD responses related to focal IED in single patients, with responses often occurring in DMN regions. In patients with high overlap with the DMN, this suggests that epileptic IEDs may be associated with a brief decrease in attention and cognitive ability. Interestingly this observation was not dependent on the frequency of IED but more common in IED of temporal origin. PMID:25477775

Micro EEG/ECG signal’s chopper-stabilization amplifying chip for novel dry-contact electrode

NASA Astrophysics Data System (ADS)

Sun, Jianhui; Wang, Chunxing; Wang, Gongtang; Wang, Jinhui; Hua, Qing; Cheng, Chuanfu; Cai, Xinxia; Yin, Tao; Yu, Yang; Yang, Haigang; Li, Dengwang

2017-02-01

Facing the body’s EEG (electroencephalograph, 0.5–100 Hz, 5–100 μV) and ECG’s (electrocardiogram, < 100 {Hz}, 0.01–5 mV) micro signal detection requirement, this paper develops a pervasive application micro signal detection ASIC chip with the chopping modulation/demodulation method. The chopper-stabilization circuit with the RRL (ripple reduction loop) circuit is to suppress the ripple voltage, which locates at the single-stage amplifier’s outputting terminal. The single-stage chopping core’s noise has been suppressed too, and it is beneficial for suppressing noises of post-circuit. The chopping core circuit uses the PFB (positive feedback loop) to increase the inputting resistance, and the NFB (negative feedback loop) to stabilize the 40 dB intermediate frequency gain. The cascaded switch-capacitor sample/hold circuit has been used for deleting spike noises caused by non-ideal MOS switches, and the VGA/BPF (voltage gain amplifier/band pass filter) circuit is used to tune the chopper system’s gain/bandwidth digitally. Assisted with the designed novel dry-electrode, the real test result of the chopping amplifying circuit gives some critical parameters: 8.1 μW/channel, 0.8 μVrms (@band-width = 100 Hz), 4216–11220 times digitally tuning gain range, etc. The data capture system uses the NI CO’s data capturing DAQmx interface, and the captured micro EEG/ECG’s waves are real-time displayed with the PC-Labview. The proposed chopper system is a unified EEG/ECG signal’s detection instrument and has a critical real application value. Project supported by the National Natural Science Foundation of China (Nos. 61527815, 31500800, 61501426, 61471342), the National Key Basic Research Plan (No. 2014CB744600), the Beijing Science and Technology Plan (No. Z141100000214002), and the Chinese Academy of Sciences’ Key Project (No. KJZD-EW-L11-2).

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.

Combining EEG and eye movement recording in free viewing: Pitfalls and possibilities.

PubMed

Nikolaev, Andrey R; Meghanathan, Radha Nila; van Leeuwen, Cees

2016-08-01

Co-registration of EEG and eye movement has promise for investigating perceptual processes in free viewing conditions, provided certain methodological challenges can be addressed. Most of these arise from the self-paced character of eye movements in free viewing conditions. Successive eye movements occur within short time intervals. Their evoked activity is likely to distort the EEG signal during fixation. Due to the non-uniform distribution of fixation durations, these distortions are systematic, survive across-trials averaging, and can become a source of confounding. We illustrate this problem with effects of sequential eye movements on the evoked potentials and time-frequency components of EEG and propose a solution based on matching of eye movement characteristics between experimental conditions. The proposal leads to a discussion of which eye movement characteristics are to be matched, depending on the EEG activity of interest. We also compare segmentation of EEG into saccade-related epochs relative to saccade and fixation onsets and discuss the problem of baseline selection and its solution. Further recommendations are given for implementing EEG-eye movement co-registration in free viewing conditions. By resolving some of the methodological problems involved, we aim to facilitate the transition from the traditional stimulus-response paradigm to the study of visual perception in more naturalistic conditions. Copyright © 2016 Elsevier Inc. All rights reserved.

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

PubMed

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

2016-06-01

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

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

PubMed

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

2014-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

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

PubMed

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

2014-12-01

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

Epileptic spasms and early-onset photosensitive epilepsy in Patau syndrome: An EEG study.

PubMed

Spagnoli, Carlotta; Kugathasan, Umaiyal; Brittain, Helen; Boyd, Stewart G

2015-08-01

Patau syndrome, trisomy 13, is the third commonest autosomal trisomy. It is associated with a 25-50% prevalence of epilepsy, but detailed electroclinical descriptions are rare. The occurrence of early-onset photosensitivity has recently been reported in single patients. We collected electroclinical data on 8 infants (age range from 2 months to 3 years and 9 months, median: 17 months) with Patau syndrome referred for an EEG in our Clinical Neurophysiology Department between 1991 and 2011. All EEGs, case-notes, cytogenetic diagnosis and neuroimaging when available were reviewed; data on the occurrence of seizures, epileptiform discharges, photoparoxysmal response and their characteristics in terms of positive frequencies, latencies, grade and duration were noted and analysed. Two patients had been previously diagnosed with epilepsy (one with tonic spasms and one with multiple seizure types). We found 3 patients with photosensitive myoclonic epilepsy (37.5%), and one with non-photosensitive myoclonic epilepsy. We also recorded non-epileptic myoclonic jerks in one patient known to suffer from epileptic spasms. Among photosensitive patients we found self-limited, Waltz's grade 2-4, spike-wave/polyspike-wave discharges in low, medium and high frequency ranges in two patients and in the high frequency range in the third patient, with latencies and duration from less than 1s to a maximum of 9s. In our cohort of Patau syndrome patients, we found a high prevalence of spasms and photic-induced myoclonic jerks. Photosensitivity shows an unusual early age of onset. Copyright © 2014 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Network-State Modulation of Power-Law Frequency-Scaling in Visual Cortical Neurons

PubMed Central

Béhuret, Sébastien; Baudot, Pierre; Yger, Pierre; Bal, Thierry; Destexhe, Alain; Frégnac, Yves

2009-01-01

Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of Vm activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the Vm reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the “effective” connectivity responsible for the dynamical signature of the population signals measured at different integration levels, from Vm to LFP, EEG and fMRI. PMID:19779556

Network-state modulation of power-law frequency-scaling in visual cortical neurons.

PubMed

El Boustani, Sami; Marre, Olivier; Béhuret, Sébastien; Baudot, Pierre; Yger, Pierre; Bal, Thierry; Destexhe, Alain; Frégnac, Yves

2009-09-01

Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of V(m) activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the V(m) reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the "effective" connectivity responsible for the dynamical signature of the population signals measured at different integration levels, from Vm to LFP, EEG and fMRI.

FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

PubMed Central

Oostenveld, Robert; Fries, Pascal; Maris, Eric; Schoffelen, Jan-Mathijs

2011-01-01

This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages. PMID:21253357

Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics.

PubMed

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

2018-06-04

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

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.

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.

Dual coding with STDP in a spiking recurrent neural network model of the hippocampus.

PubMed

Bush, Daniel; Philippides, Andrew; Husbands, Phil; O'Shea, Michael

2010-07-01

The firing rate of single neurons in the mammalian hippocampus has been demonstrated to encode for a range of spatial and non-spatial stimuli. It has also been demonstrated that phase of firing, with respect to the theta oscillation that dominates the hippocampal EEG during stereotype learning behaviour, correlates with an animal's spatial location. These findings have led to the hypothesis that the hippocampus operates using a dual (rate and temporal) coding system. To investigate the phenomenon of dual coding in the hippocampus, we examine a spiking recurrent network model with theta coded neural dynamics and an STDP rule that mediates rate-coded Hebbian learning when pre- and post-synaptic firing is stochastic. We demonstrate that this plasticity rule can generate both symmetric and asymmetric connections between neurons that fire at concurrent or successive theta phase, respectively, and subsequently produce both pattern completion and sequence prediction from partial cues. This unifies previously disparate auto- and hetero-associative network models of hippocampal function and provides them with a firmer basis in modern neurobiology. Furthermore, the encoding and reactivation of activity in mutually exciting Hebbian cell assemblies demonstrated here is believed to represent a fundamental mechanism of cognitive processing in the brain.

Algorithm for AEEG data selection leading to wireless and long term epilepsy monitoring.

PubMed

Casson, Alexander J; Yates, David C; Patel, Shyam; Rodriguez-Villegas, Esther

2007-01-01

High quality, wireless ambulatory EEG (AEEG) systems that can operate over extended periods of time are not currently feasible due to the high power consumption of wireless transmitters. Previous work has thus proposed data reduction by only transmitting sections of data that contain candidate epileptic activity. This paper investigates algorithms by which this data selection can be carried out. It is essential that the algorithm is low power and that all possible features are identified, even at the expense of more false detections. Given this, a brief review of spike detection algorithms is carried out with a view to using these algorithms to drive the data reduction process. A CWT based algorithm is deemed most suitable for use and an algorithm is described in detail and its performance tested. It is found that over 90% of expert marked spikes are identified whilst giving a 40% reduction in the amount of data to be transmitted and analysed. The performance varies with the recording duration in response to each detection and this effect is also investigated. The proposed algorithm will form the basis of new a AEEG system that allows wireless and longer term epilepsy monitoring.

Unavoidable Errors: A Spatio-Temporal Analysis of Time-Course and Neural Sources of Evoked Potentials Associated with Error Processing in a Speeded Task

ERIC Educational Resources Information Center

Vocat, Roland; Pourtois, Gilles; Vuilleumier, Patrik

2008-01-01

The detection of errors is known to be associated with two successive neurophysiological components in EEG, with an early time-course following motor execution: the error-related negativity (ERN/Ne) and late positivity (Pe). The exact cognitive and physiological processes contributing to these two EEG components, as well as their functional…

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

PubMed

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

2016-01-01

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

PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG.

PubMed

Ball, Kenneth; Bigdely-Shamlo, Nima; Mullen, Tim; Robbins, Kay

2016-01-01

Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal order of signals and enforces certain mixing stationarity constraints. The resulting procedure, which we call Pairwise Complex Independent Component Analysis (PWC-ICA), performs the ICA in a complex setting and then reinterprets the results in the original observation space. We examine the performance of our candidate approach relative to several existing ICA algorithms for the blind source separation (BSS) problem on both real and simulated EEG data. On simulated data, PWC-ICA is often capable of achieving a better solution to the BSS problem than AMICA, Extended Infomax, or FastICA. On real data, the dipole interpretations of the BSS solutions discovered by PWC-ICA are physically plausible, are competitive with existing ICA approaches, and may represent sources undiscovered by other ICA methods. In conjunction with this paper, the authors have released a MATLAB toolbox that performs PWC-ICA on real, vector-valued signals.

PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG

PubMed Central

Bigdely-Shamlo, Nima; Mullen, Tim; Robbins, Kay

2016-01-01

Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal order of signals and enforces certain mixing stationarity constraints. The resulting procedure, which we call Pairwise Complex Independent Component Analysis (PWC-ICA), performs the ICA in a complex setting and then reinterprets the results in the original observation space. We examine the performance of our candidate approach relative to several existing ICA algorithms for the blind source separation (BSS) problem on both real and simulated EEG data. On simulated data, PWC-ICA is often capable of achieving a better solution to the BSS problem than AMICA, Extended Infomax, or FastICA. On real data, the dipole interpretations of the BSS solutions discovered by PWC-ICA are physically plausible, are competitive with existing ICA approaches, and may represent sources undiscovered by other ICA methods. In conjunction with this paper, the authors have released a MATLAB toolbox that performs PWC-ICA on real, vector-valued signals. PMID:27340397

Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

PubMed Central

Huang, Chih-Sheng; Yang, Wen-Yu; Chuang, Chun-Hsiang; Wang, Yu-Kai

2018-01-01

Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research. PMID:29599950

Data acquisition instrument for EEG based on embedded system

NASA Astrophysics Data System (ADS)

Toresano, La Ode Husein Z.; Wijaya, Sastra Kusuma; Prawito, Sudarmaji, Arief; Syakura, Abdan; Badri, Cholid

2017-02-01

An electroencephalogram (EEG) is a device for measuring and recording the electrical activity of brain. The EEG data of signal can be used as a source of analysis for human brain function. The purpose of this study was to design a portable multichannel EEG based on embedded system and ADS1299. The ADS1299 is an analog front-end to be used as an Analog to Digital Converter (ADC) to convert analog signal of electrical activity of brain, a filter of electrical signal to reduce the noise on low-frequency band and a data communication to the microcontroller. The system has been tested to capture brain signal within a range of 1-20 Hz using the NETECH EEG simulator 330. The developed system was relatively high accuracy of more than 82.5%. The EEG Instrument has been successfully implemented to acquire the brain signal activity using a PC (Personal Computer) connection for displaying the recorded data. The final result of data acquisition has been processed using OpenBCI GUI (Graphical User Interface) based through real-time process for 8-channel signal acquisition, brain-mapping and power spectral decomposition signal using the standard FFT (Fast Fourier Transform) algorithm.

Multimodal neuroimaging evidence linking memory and attention systems during visual search cued by context.

PubMed

Kasper, Ryan W; Grafton, Scott T; Eckstein, Miguel P; Giesbrecht, Barry

2015-03-01

Visual search can be facilitated by the learning of spatial configurations that predict the location of a target among distractors. Neuropsychological and functional magnetic resonance imaging (fMRI) evidence implicates the medial temporal lobe (MTL) memory system in this contextual cueing effect, and electroencephalography (EEG) studies have identified the involvement of visual cortical regions related to attention. This work investigated two questions: (1) how memory and attention systems are related in contextual cueing; and (2) how these systems are involved in both short- and long-term contextual learning. In one session, EEG and fMRI data were acquired simultaneously in a contextual cueing task. In a second session conducted 1 week later, EEG data were recorded in isolation. The fMRI results revealed MTL contextual modulations that were correlated with short- and long-term behavioral context enhancements and attention-related effects measured with EEG. An fMRI-seeded EEG source analysis revealed that the MTL contributed the most variance to the variability in the attention enhancements measured with EEG. These results support the notion that memory and attention systems interact to facilitate search when spatial context is implicitly learned. © 2015 New York Academy of Sciences.

The Smartphone Brain Scanner: A Portable Real-Time Neuroimaging System

PubMed Central

Stopczynski, Arkadiusz; Stahlhut, Carsten; Larsen, Jakob Eg; Petersen, Michael Kai; Hansen, Lars Kai

2014-01-01

Combining low-cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. Here we present the technical details and validation of a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system – Smartphone Brain Scanner – combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully portable system for real-time 3D EEG imaging. We discuss the benefits and challenges, including technical limitations as well as details of real-time reconstruction of 3D images of brain activity. We present examples of brain activity captured in a simple experiment involving imagined finger tapping, which shows that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using an off-the-shelf consumer neuroheadset is lower than the signal obtained using high-density standard EEG equipment, we propose mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings. PMID:24505263

Entropy is more resistant to artifacts than bispectral index in brain-dead organ donors.

PubMed

Wennervirta, Johanna; Salmi, Tapani; Hynynen, Markku; Yli-Hankala, Arvi; Koivusalo, Anna-Maria; Van Gils, Mark; Pöyhiä, Reino; Vakkuri, Anne

2007-01-01

To evaluate the usefulness of entropy and the bispectral index (BIS) in brain-dead subjects. A prospective, open, nonselective, observational study in the university hospital. 16 brain-dead organ donors. Time-domain electroencephalography (EEG), spectral entropy of the EEG, and BIS were recorded during solid organ harvest. State entropy differed significantly from 0 (isoelectric EEG) 28%, response entropy 29%, and BIS 68% of the total recorded time. The median values during the operation were state entropy 0.0, response entropy 0.0, and BIS 3.0. In four of 16 organ donors studied the EEG was not isoelectric, and nonreactive rhythmic activity was noted in time-domain EEG. After excluding the results from subjects with persistent residual EEG activity state entropy, response entropy, and BIS values differed from zero 17%, 18%, and 62% of the recorded time, respectively. Median values were 0.0, 0.0, and 2.0 for state entropy, response entropy, and BIS, respectively. The highest index values in entropy and BIS monitoring were recorded without neuromuscular blockade. The main sources of artifacts were electrocauterization, 50-Hz artifact, handling of the donor, ballistocardiography, electromyography, and electrocardiography. Both entropy and BIS showed nonzero values due to artifacts after brain death diagnosis. BIS was more liable to artifacts than entropy. Neither of these indices are diagnostic tools, and care should be taken when interpreting EEG and EEG-derived indices in the evaluation of brain death.

Poisson-Like Spiking in Circuits with Probabilistic Synapses

PubMed Central

Moreno-Bote, Rubén

2014-01-01

Neuronal activity in cortex is variable both spontaneously and during stimulation, and it has the remarkable property that it is Poisson-like over broad ranges of firing rates covering from virtually zero to hundreds of spikes per second. The mechanisms underlying cortical-like spiking variability over such a broad continuum of rates are currently unknown. We show that neuronal networks endowed with probabilistic synaptic transmission, a well-documented source of variability in cortex, robustly generate Poisson-like variability over several orders of magnitude in their firing rate without fine-tuning of the network parameters. Other sources of variability, such as random synaptic delays or spike generation jittering, do not lead to Poisson-like variability at high rates because they cannot be sufficiently amplified by recurrent neuronal networks. We also show that probabilistic synapses predict Fano factor constancy of synaptic conductances. Our results suggest that synaptic noise is a robust and sufficient mechanism for the type of variability found in cortex. PMID:25032705

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

NASA Astrophysics Data System (ADS)

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

2016-05-01

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

Definitions of state variables and state space for brain-computer interface : Part 2. Extraction and classification of feature vectors.

PubMed

Freeman, Walter J

2007-06-01

The hypothesis is proposed that the central dynamics of the action-perception cycle has five steps: emergence from an existing macroscopic brain state of a pattern that predicts a future goal state; selection of a mesoscopic frame for action control; execution of a limb trajectory by microscopic spike activity; modification of microscopic cortical spike activity by sensory inputs; construction of mesoscopic perceptual patterns; and integration of a new macroscopic brain state. The basis is the circular causality between microscopic entities (neurons) and the mesoscopic and macroscopic entities (populations) self-organized by axosynaptic interactions. Self-organization of neural activity is bidirectional in all cortices. Upwardly the organization of mesoscopic percepts from microscopic spike input predominates in primary sensory areas. Downwardly the organization of spike outputs that direct specific limb movements is by mesoscopic fields constituting plans to achieve predicted goals. The mesoscopic fields in sensory and motor cortices emerge as frames within macroscopic activity. Part 1 describes the action-perception cycle and its derivative reflex arc qualitatively. Part 2 describes the perceptual limb of the arc from microscopic MSA to mesoscopic wave packets, and from these to macroscopic EEG and global ECoG fields that express experience-dependent knowledge in successive states. These macroscopic states are conceived to embed and control mesoscopic frames in premotor and motor cortices that are observed in local ECoG and LFP of frontoparietal areas. The fields sampled by ECoG and LFP are conceived as local patterns of neural activity in which trajectories of multiple spike activities (MSA) emerge that control limb movements. Mesoscopic frames are located by use of the analytic signal from the Hilbert transform after band pass filtering. The state variables in frames are measured to construct feature vectors by which to describe and classify frame patterns. Evidence is cited to justify use of linear analysis. The aim of the review is to enable researchers to conceive and identify goal-oriented states in brain activity for use as commands, in order to relegate the details of execution to adaptive control devices outside the brain.

Low-noise encoding of active touch by layer 4 in the somatosensory cortex.

PubMed

Hires, Samuel Andrew; Gutnisky, Diego A; Yu, Jianing; O'Connor, Daniel H; Svoboda, Karel

2015-08-06

Cortical spike trains often appear noisy, with the timing and number of spikes varying across repetitions of stimuli. Spiking variability can arise from internal (behavioral state, unreliable neurons, or chaotic dynamics in neural circuits) and external (uncontrolled behavior or sensory stimuli) sources. The amount of irreducible internal noise in spike trains, an important constraint on models of cortical networks, has been difficult to estimate, since behavior and brain state must be precisely controlled or tracked. We recorded from excitatory barrel cortex neurons in layer 4 during active behavior, where mice control tactile input through learned whisker movements. Touch was the dominant sensorimotor feature, with >70% spikes occurring in millisecond timescale epochs after touch onset. The variance of touch responses was smaller than expected from Poisson processes, often reaching the theoretical minimum. Layer 4 spike trains thus reflect the millisecond-timescale structure of tactile input with little noise.

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

PubMed

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

2011-06-27

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

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

PubMed

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

2016-08-01

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

Automatic classification of artifactual ICA-components for artifact removal in EEG signals.

PubMed

Winkler, Irene; Haufe, Stefan; Tangermann, Michael

2011-08-02

Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies.

Determination of head conductivity frequency response in vivo with optimized EIT-EEG.

PubMed

Dabek, Juhani; Kalogianni, Konstantina; Rotgans, Edwin; van der Helm, Frans C T; Kwakkel, Gert; van Wegen, Erwin E H; Daffertshofer, Andreas; de Munck, Jan C

2016-02-15

Electroencephalography (EEG) benefits from accurate head models. Dipole source modelling errors can be reduced from over 1cm to a few millimetres by replacing generic head geometry and conductivity with tailored ones. When adequate head geometry is available, electrical impedance tomography (EIT) can be used to infer the conductivities of head tissues. In this study, the boundary element method (BEM) is applied with three-compartment (scalp, skull and brain) subject-specific head models. The optimal injection of small currents to the head with a modular EIT current injector, and voltage measurement by an EEG amplifier is first sought by simulations. The measurement with a 64-electrode EEG layout is studied with respect to three noise sources affecting EIT: background EEG, deviations from the fitting assumption of equal scalp and brain conductivities, and smooth model geometry deviations from the true head geometry. The noise source effects were investigated depending on the positioning of the injection and extraction electrode and the number of their combinations used sequentially. The deviation from equal scalp and brain conductivities produces rather deterministic errors in the three conductivities irrespective of the current injection locations. With a realistic measurement of around 2 min and around 8 distant distinct current injection pairs, the error from the other noise sources is reduced to around 10% or less in the skull conductivity. The analysis of subsequent real measurements, however, suggests that there could be subject-specific local thinnings in the skull, which could amplify the conductivity fitting errors. With proper analysis of multiplexed sinusoidal EIT current injections, the measurements on average yielded conductivities of 340 mS/m (scalp and brain) and 6.6 mS/m (skull) at 2 Hz. From 11 to 127 Hz, the conductivities increased by 1.6% (scalp and brain) and 6.7% (skull) on the average. The proper analysis was ensured by using recombination of the current injections into virtual ones, avoiding problems in location-specific skull morphology variations. The observed large intersubject variations support the need for in vivo measurement of skull conductivity, resulting in calibrated subject-specific head models. Copyright © 2015 Elsevier Inc. All rights reserved.

Clonic Seizures in GAERS Rats after Oral Administration of Enrofloxacin

PubMed Central

Bauquier, Sebastien H; Jiang, Jonathan L; Lai, Alan; Cook, Mark J

2016-01-01

The aim of this study was to evaluate the effect of oral enrofloxacin on the epileptic status of Genetic Absence Epilepsy Rats from Strasbourg (GAERS). Five adult female GAERS rats, with implanted extradural electrodes for EEG monitoring, were declared free of clonic seizures after an 8-wk observation period. Enrofloxacin was then added to their drinking water (42.5 mg in 750 mL), and rats were observed for another 3 days. The number of spike-and-wave discharges and mean duration of a single discharge did not differ before and after treatment, but 2 of the 5 rats developed clonic seizures after treatment. Enrofloxacin should be used with caution in GAERS rats because it might induce clonic seizures. PMID:27298247

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

PubMed Central

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

2014-01-01

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

The performance of the spatiotemporal Kalman filter and LORETA in seizure onset localization.

PubMed

Hamid, Laith; Sarabi, Masoud; Japaridze, Natia; Wiegand, Gert; Heute, Ulrich; Stephani, Ulrich; Galka, Andreas; Siniatchkin, Michael

2015-08-01

The assumption of spatial-smoothness is often used to solve the bioelectric inverse problem during electroencephalographic (EEG) source imaging, e.g., in low resolution electromagnetic tomography (LORETA). Since the EEG data show a temporal structure, the combination of the temporal-smoothness and the spatial-smoothness constraints may improve the solution of the EEG inverse problem. This study investigates the performance of the spatiotemporal Kalman filter (STKF) method, which is based on spatial and temporal smoothness, in the localization of a focal seizure's onset and compares its results to those of LORETA. The main finding of the study was that the STKF with an autoregressive model of order two significantly outperformed LORETA in the accuracy and consistency of the localization, provided that the source space consists of a whole-brain volumetric grid. In the future, these promising results will be confirmed using data from more patients and performing statistical analyses on the results. Furthermore, the effects of the temporal smoothness constraint will be studied using different types of focal seizures.

TopoToolbox: using sensor topography to calculate psychologically meaningful measures from event-related EEG/MEG.

PubMed

Tian, Xing; Poeppel, David; Huber, David E

2011-01-01

The open-source toolbox "TopoToolbox" is a suite of functions that use sensor topography to calculate psychologically meaningful measures (similarity, magnitude, and timing) from multisensor event-related EEG and MEG data. Using a GUI and data visualization, TopoToolbox can be used to calculate and test the topographic similarity between different conditions (Tian and Huber, 2008). This topographic similarity indicates whether different conditions involve a different distribution of underlying neural sources. Furthermore, this similarity calculation can be applied at different time points to discover when a response pattern emerges (Tian and Poeppel, 2010). Because the topographic patterns are obtained separately for each individual, these patterns are used to produce reliable measures of response magnitude that can be compared across individuals using conventional statistics (Davelaar et al. Submitted and Huber et al., 2008). TopoToolbox can be freely downloaded. It runs under MATLAB (The MathWorks, Inc.) and supports user-defined data structure as well as standard EEG/MEG data import using EEGLAB (Delorme and Makeig, 2004).

Corrected Four-Sphere Head Model for EEG Signals.

PubMed

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

2017-01-01

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

Corrected Four-Sphere Head Model for EEG Signals

PubMed Central

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

2017-01-01

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

The interactive electrode localization utility: software for automatic sorting and labeling of intracranial subdural electrodes

PubMed Central

Tang, Wei; Peled, Noam; Vallejo, Deborah I.; Borzello, Mia; Dougherty, Darin D.; Eskandar, Emad N.; Widge, Alik S.; Cash, Sydney S.; Stufflebeam, Steven M.

2018-01-01

Purpose Existing methods for sorting, labeling, registering, and across-subject localization of electrodes in intracranial encephalography (iEEG) may involve laborious work requiring manual inspection of radiological images. Methods We describe a new open-source software package, the interactive electrode localization utility which presents a full pipeline for the registration, localization, and labeling of iEEG electrodes from CT and MR images. In addition, we describe a method to automatically sort and label electrodes from subdural grids of known geometry. Results We validated our software against manual inspection methods in twelve subjects undergoing iEEG for medically intractable epilepsy. Our algorithm for sorting and labeling performed correct identification on 96% of the electrodes. Conclusions The sorting and labeling methods we describe offer nearly perfect performance and the software package we have distributed may simplify the process of registering, sorting, labeling, and localizing subdural iEEG grid electrodes by manual inspection. PMID:27915398

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.

A Comparison of Independent Event-Related Desynchronization Responses in Motor-Related Brain Areas to Movement Execution, Movement Imagery, and Movement Observation.

PubMed

Duann, Jeng-Ren; Chiou, Jin-Chern

2016-01-01

Electroencephalographic (EEG) event-related desynchronization (ERD) induced by movement imagery or by observing biological movements performed by someone else has recently been used extensively for brain-computer interface-based applications, such as applications used in stroke rehabilitation training and motor skill learning. However, the ERD responses induced by the movement imagery and observation might not be as reliable as the ERD responses induced by movement execution. Given that studies on the reliability of the EEG ERD responses induced by these activities are still lacking, here we conducted an EEG experiment with movement imagery, movement observation, and movement execution, performed multiple times each in a pseudorandomized order in the same experimental runs. Then, independent component analysis (ICA) was applied to the EEG data to find the common motor-related EEG source activity shared by the three motor tasks. Finally, conditional EEG ERD responses associated with the three movement conditions were computed and compared. Among the three motor conditions, the EEG ERD responses induced by motor execution revealed the alpha power suppression with highest strengths and longest durations. The ERD responses of the movement imagery and movement observation only partially resembled the ERD pattern of the movement execution condition, with slightly better detectability for the ERD responses associated with the movement imagery and faster ERD responses for movement observation. This may indicate different levels of involvement in the same motor-related brain circuits during different movement conditions. In addition, because the resulting conditional EEG ERD responses from the ICA preprocessing came with minimal contamination from the non-related and/or artifactual noisy components, this result can play a role of the reference for devising a brain-computer interface using the EEG ERD features of movement imagery or observation.

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

PubMed

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

2017-01-01

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

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-01-01

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

Application and Evaluation of Independent Component Analysis Methods to Generalized Seizure Disorder Activities Exhibited in the Brain.

PubMed

George, S Thomas; Balakrishnan, R; Johnson, J Stanly; Jayakumar, J

2017-07-01

EEG records the spontaneous electrical activity of the brain using multiple electrodes placed on the scalp, and it provides a wealth of information related to the functions of brain. Nevertheless, the signals from the electrodes cannot be directly applied to a diagnostic tool like brain mapping as they undergo a "mixing" process because of the volume conduction effect in the scalp. A pervasive problem in neuroscience is determining which regions of the brain are active, given voltage measurements at the scalp. Because of which, there has been a surge of interest among the biosignal processing community to investigate the process of mixing and unmixing to identify the underlying active sources. According to the assumptions of independent component analysis (ICA) algorithms, the resultant mixture obtained from the scalp can be closely approximated by a linear combination of the "actual" EEG signals emanating from the underlying sources of electrical activity in the brain. As a consequence, using these well-known ICA techniques in preprocessing of the EEG signals prior to clinical applications could result in development of diagnostic tool like quantitative EEG which in turn can assist the neurologists to gain noninvasive access to patient-specific cortical activity, which helps in treating neuropathologies like seizure disorders. The popular and proven ICA schemes mentioned in various literature and applications were selected (which includes Infomax, JADE, and SOBI) and applied on generalized seizure disorder samples using EEGLAB toolbox in MATLAB environment to see their usefulness in source separations; and they were validated by the expert neurologist for clinical relevance in terms of pathologies on brain functionalities. The performance of Infomax method was found to be superior when compared with other ICA schemes applied on EEG and it has been established based on the validations carried by expert neurologist for generalized seizure and its clinical correlation. The results are encouraging for furthering the studies in the direction of developing useful brain mapping tools using ICA methods.

Review on solving the forward problem in EEG source analysis

PubMed Central

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

2007-01-01

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

Kurtosis-Based Blind Source Extraction of Complex Non-Circular Signals with Application in EEG Artifact Removal in Real-Time

PubMed Central

Javidi, Soroush; Mandic, Danilo P.; Took, Clive Cheong; Cichocki, Andrzej

2011-01-01

A new class of complex domain blind source extraction algorithms suitable for the extraction of both circular and non-circular complex signals is proposed. This is achieved through sequential extraction based on the degree of kurtosis and in the presence of non-circular measurement noise. The existence and uniqueness analysis of the solution is followed by a study of fast converging variants of the algorithm. The performance is first assessed through simulations on well understood benchmark signals, followed by a case study on real-time artifact removal from EEG signals, verified using both qualitative and quantitative metrics. The results illustrate the power of the proposed approach in real-time blind extraction of general complex-valued sources. PMID:22319461

Triheptanoin for glucose transporter type I deficiency (G1D): Modulation of human ictogenesis, cerebral metabolic rate and cognitive indices by a food supplement

PubMed Central

Pascual, Juan M.; Liu, Peiying; Mao, Deng; Kelly, Dorothy; Hernandez, Ana; Sheng, Min; Good, Levi B.; Ma, Qian; Marin-Valencia, Isaac; Zhang, Xuchen; Park, Jason Y.; Hynan, Linda S.; Stavinoha, Peter; Roe, Charles R.; Lu, Hanzhang

2015-01-01

Objective G1D is commonly associated with electrographic spike-wave and - less-noticeably – with absence seizures. The G1D syndrome has long been attributed to energy (i.e., ATP-synthetic) failure, as have experimental, toxic-rodent epilepsies to impaired brain metabolism and tricarboxylic acid (TCA) cycle intermediate depletion. Indeed, a (seldom-acknowledged) function of glucose and other substrates is the generation of brain TCAs via carbon-donor reactions collectively named anaplerosis. However, TCAs are preserved in murine G1D. This renders inferences about energy failure premature and suggests a different hypothesis, also grounded on our findings, that consumption of alternate TCA precursors is stimulated, potentially detracting from other functions. Second, common ketogenic diets can ameliorate G1D seizures, but lead to a therapeutically-counterintuitive reduction in blood glucose available to the brain, and they can prove ineffective in 1/3 of cases. While developing G1D treatments, all of this motivated us to: a) uphold (rather than attenuate) the residual brain glucose flux that all G1D patients possess; and b) stimulate the TCA cycle, including anaplerosis. Therefore, we tested the medium-chain triglyceride triheptanoin, a widely-used medical food supplement that can fulfill both of these metabolic roles. The rationale is that ketone bodies derived from ketogenic diets are not anaplerotic, in contrast with triheptanoin metabolites, as we have shown in the G1D mouse brain. Design We supplemented the regular diet of a case series of G1D patients with food-grade triheptanoin. First we confirmed that, despite their frequent electroencephalographic (EEG) presence as spike-waves, most seizures are rarely visible, such that perceptions by patients or others are inadequate for treatment evaluation. Thus, we used EEG, quantitative neuropsychological, blood analytical, and MRI cerebral metabolic rate measurements as main outcomes. Setting Academic and unsponsored. Patients Fourteen G1D children and adults not receiving a ketogenic diet. Results Eleven patients tolerated triheptanoin without significant adverse effects. Spike-waves decreased robustly in all patients who manifested them. Additionally, neuropsychological performance and cerebral metabolic rate increased in most patients. Conclusions Triheptanoin can favorably influence cardinal aspects of neural function in G1D. Additionally, our outcome measures offer a framework for the evaluation of therapies for G1D and other encephalopathies associated with impaired intermediary metabolism. PMID:25110966

Characterization of the bout durations of sleep and wakefulness.

PubMed

McShane, Blakeley B; Galante, Raymond J; Jensen, Shane T; Naidoo, Nirinjini; Pack, Allan I; Wyner, Abraham

2010-11-30

(a) Develop a new statistical approach to describe the microarchitecture of wakefulness and sleep in mice; (b) evaluate differences among inbred strains in this microarchitecture; (c) compare results when data are scored in 4-s versus 10-s epochs. Studies in male mice of four inbred strains: AJ, C57BL/6, DBA and PWD. EEG/EMG were recorded for 24h and scored independently in 4-s and 10-s epochs. Distribution of bout durations of wakefulness, NREM and REM sleep in mice has two distinct components, i.e., short and longer bouts. This is described as a spike (short bouts) and slab (longer bouts) distribution, a particular type of mixture model. The distribution in any state depends on the state the mouse is transitioning from and can be characterized by three parameters: the number of such bouts conditional on the previous state, the size of the spike, and the average length of the slab. While conventional statistics such as time spent in state, average bout duration, and number of bouts show some differences between inbred strains, this new statistical approach reveals more major differences. The major difference between strains is their ability to sustain long bouts of NREM sleep or wakefulness. Scoring mouse sleep/wake in 4-s epochs offered little new information when using conventional metrics but did when evaluating the microarchitecture based on this new approach. Standard statistical approaches do not adequately characterize the microarchitecture of mouse behavioral state. Approaches based on a spike-and-slab provide a quantitative description. Copyright © 2010 Elsevier B.V. All rights reserved.

Gamma oscillations precede interictal epileptiform spikes in the seizure onset zone

PubMed Central

Ren, Liankun; Kucewicz, Michal T.; Cimbalnik, Jan; Matsumoto, Joseph Y.; Brinkmann, Benjamin H.; Hu, Wei; Marsh, W. Richard; Meyer, Fredric B.; Stead, S. Matthew

2015-01-01

Objective: To investigate the generation, spectral characteristics, and potential clinical significance of brain activity preceding interictal epileptiform spike discharges (IEDs) recorded with intracranial EEG. Methods: Seventeen adult patients with drug-resistant temporal lobe epilepsy were implanted with intracranial electrodes as part of their evaluation for epilepsy surgery. IEDs detected on clinical macro- and research microelectrodes were analyzed using time-frequency spectral analysis. Results: Gamma frequency oscillations (30–100 Hz) often preceded IEDs in spatially confined brain areas. The gamma-IEDs were consistently observed 35 to 190 milliseconds before the epileptiform spike waveforms on individual macro- and microelectrodes. The gamma oscillations associated with IEDs had longer duration (p < 0.001) and slightly higher frequency (p = 0.045) when recorded on microelectrodes compared with clinical macroelectrodes. Although gamma-IEDs comprised only a subset of IEDs, they were strongly associated with electrodes in the seizure onset zone (SOZ) compared with the surrounding brain regions (p = 0.004), in sharp contrast to IEDs without preceding gamma oscillations that were often also detected outside of the SOZ. Similar to prior studies, isolated pathologic high-frequency oscillations in the gamma (30–100 Hz) and higher (100–600 Hz) frequency range, not associated with an IED, were also found to be associated with SOZ. Conclusions: The occurrence of locally generated gamma oscillations preceding IEDs suggests a mechanistic role for gamma in pathologic network activity generating IEDs. The results show a strong association between SOZ and gamma-IEDs. The potential clinical application of gamma-IEDs for mapping pathologic brain regions is intriguing, but will require future prospective studies. PMID:25589669

EEG and MEG source localization using recursively applied (RAP) MUSIC

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

Mosher, J.C.; Leahy, R.M.

1996-12-31

The multiple signal characterization (MUSIC) algorithm locates multiple asynchronous dipolar sources from electroencephalography (EEG) and magnetoencephalography (MEG) data. A signal subspace is estimated from the data, then the algorithm scans a single dipole model through a three-dimensional head volume and computes projections onto this subspace. To locate the sources, the user must search the head volume for local peaks in the projection metric. Here we describe a novel extension of this approach which we refer to as RAP (Recursively APplied) MUSIC. This new procedure automatically extracts the locations of the sources through a recursive use of subspace projections, which usesmore » the metric of principal correlations as a multidimensional form of correlation analysis between the model subspace and the data subspace. The dipolar orientations, a form of `diverse polarization,` are easily extracted using the associated principal vectors.« less

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.

Revealing spatio-spectral electroencephalographic dynamics of musical mode and tempo perception by independent component analysis.

PubMed

Lin, Yuan-Pin; Duann, Jeng-Ren; Feng, Wenfeng; Chen, Jyh-Horng; Jung, Tzyy-Ping

2014-02-28

Music conveys emotion by manipulating musical structures, particularly musical mode- and tempo-impact. The neural correlates of musical mode and tempo perception revealed by electroencephalography (EEG) have not been adequately addressed in the literature. This study used independent component analysis (ICA) to systematically assess spatio-spectral EEG dynamics associated with the changes of musical mode and tempo. Empirical results showed that music with major mode augmented delta-band activity over the right sensorimotor cortex, suppressed theta activity over the superior parietal cortex, and moderately suppressed beta activity over the medial frontal cortex, compared to minor-mode music, whereas fast-tempo music engaged significant alpha suppression over the right sensorimotor cortex. The resultant EEG brain sources were comparable with previous studies obtained by other neuroimaging modalities, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). In conjunction with advanced dry and mobile EEG technology, the EEG results might facilitate the translation from laboratory-oriented research to real-life applications for music therapy, training and entertainment in naturalistic environments.

Magnetoencephalography with temporal spread imaging to visualize propagation of epileptic activity.

PubMed

Shibata, Sumiya; Matsuhashi, Masao; Kunieda, Takeharu; Yamao, Yukihiro; Inano, Rika; Kikuchi, Takayuki; Imamura, Hisaji; Takaya, Shigetoshi; Matsumoto, Riki; Ikeda, Akio; Takahashi, Ryosuke; Mima, Tatsuya; Fukuyama, Hidenao; Mikuni, Nobuhiro; Miyamoto, Susumu

2017-05-01

We describe temporal spread imaging (TSI) that can identify the spatiotemporal pattern of epileptic activity using Magnetoencephalography (MEG). A three-dimensional grid of voxels covering the brain is created. The array-gain minimum-variance spatial filter is applied to an interictal spike to estimate the magnitude of the source and the time (Ta) when the magnitude exceeds a predefined threshold at each voxel. This calculation is performed through all spikes. Each voxel has the mean Ta () and spike number (N sp ), which is the number of spikes whose source exceeds the threshold. Then, a random resampling method is used to determine the cutoff value of N sp for the statistically reproducible pattern of the activity. Finally, all the voxels where the source exceeds the threshold reproducibly shown on the MRI with a color scale representing . Four patients with intractable mesial temporal lobe epilepsy (MTLE) were analyzed. In three patients, the common pattern of the overlap between the propagation and the hypometabolism shown by fluorodeoxyglucose-positron emission tomography (FDG-PET) was identified. TSI can visualize statistically reproducible patterns of the temporal and spatial spread of epileptic activity. TSI can assess the statistical significance of the spatiotemporal pattern based on its reproducibility. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

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

PubMed

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

2015-02-01

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

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

PubMed

Mitchell, Daniel J; Cusack, Rhodri

2011-01-01

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

Consensus-Based Sorting of Neuronal Spike Waveforms

PubMed Central

Fournier, Julien; Mueller, Christian M.; Shein-Idelson, Mark; Hemberger, Mike

2016-01-01

Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data. PMID:27536990

Consensus-Based Sorting of Neuronal Spike Waveforms.

PubMed

Fournier, Julien; Mueller, Christian M; Shein-Idelson, Mark; Hemberger, Mike; Laurent, Gilles

2016-01-01

Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained "ground truth" data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data.

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-01-01

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

Optimal Measurement Conditions for Spatiotemporal EEG/MEG Source Analysis.

ERIC Educational Resources Information Center

Huizenga, Hilde M.; Heslenfeld, Dirk J.; Molenaar, Peter C. M.

2002-01-01

Developed a method to determine the required number and position of sensors for human brain electromagnetic source analysis. Studied the method through a simulation study and an empirical study on visual evoked potentials in one adult male. Results indicate the method is fast and reliable and improves source precision. (SLD)

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

PubMed Central

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

2015-01-01

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

ION SOURCE

DOEpatents

Brobeck, W.M.

1959-04-14

This patent deals with calutrons and more particularly to an arrangement therein whereby charged bottles in a calutron source unit may be replaced without admitting atmospheric air to the calutron vacuum chamber. As described, an ion unit is disposed within a vacuum tank and has a reservoir open toward a wall of the tank. A spike projects from thc source into the reservoir. When a charge bottle is placed in the reservoir, the spike breaks a frangible seal on the bottle. After the contents of the bottle are expended the bottle may be withdrawn and replaced with another charge bottle by a varuum lock arrangement in conjunction with an arm for manipulating the bottle.

Sound Source Localization through 8 MEMS Microphones Array Using a Sand-Scorpion-Inspired Spiking Neural Network.

PubMed

Beck, Christoph; Garreau, Guillaume; Georgiou, Julius

2016-01-01

Sand-scorpions and many other arachnids perceive their environment by using their feet to sense ground waves. They are able to determine amplitudes the size of an atom and locate the acoustic stimuli with an accuracy of within 13° based on their neuronal anatomy. We present here a prototype sound source localization system, inspired from this impressive performance. The system presented utilizes custom-built hardware with eight MEMS microphones, one for each foot, to acquire the acoustic scene, and a spiking neural model to localize the sound source. The current implementation shows smaller localization error than those observed in nature.

Ion source

DOEpatents

Brobeck, W. M.

1959-04-14

This patent deals with calutrons and more particularly to an arrangement therein whereby charged bottles in a calutron source unit may be replaced without admitting atmospheric air to the calutron vacuum chamber. As described, an ion unit is disposed within a vacuum tank and has a reservoir open toward a wall of the tank. A spike projects from the source into the reservoir. When a charge bottle is placed in the reservoir, the spike breaks a frangible seal on the bottle. After the contents of the bottle are expended the bottle may be withdrawn and replaced with another charge bottle by a vacuum lock arrangement in conjunction with an arm for manipulating the bottle.

PICK1 uncoupling from mGluR7a causes absence-like seizures

PubMed Central

Bertaso, Federica; Zhang, Chuansheng; Scheschonka, Astrid; de Bock, Frédéric; Fontanaud, Pierre; Marin, Philippe; Huganir, Richard L; Betz, Heinrich; Bockaert, Joël; Fagni, Laurent; Lerner-Natoli, Mireille

2009-01-01

Absence epilepsy is a neurological disorder that causes a recurrent loss of consciousness and generalized spike-and-wave discharges on an electroencephalogram (EEG). The role of metabotropic glutamate receptors (mGluRs) and associated scaffolding proteins in absence epilepsy has been unclear to date. We investigated a possible role for these proteins in absence epilepsy, focusing on the mGluR7a receptor and its PDZ-interacting protein, protein interacting with C kinase 1 (PICK1), in rats and mice. Injection of a cell-permeant dominant-negative peptide or targeted mutation of the mGluR7a C terminus, both of which disrupt the interaction between the receptor and PDZ proteins, caused behavioral symptoms and EEG discharges that are characteristic of absence epilepsy. Inactivation of the Pick1 gene also facilitated pharmacological induction of the absence epilepsy phenotype. The cortex and thalamus, which are known to participate in absence epilepsy, were involved, but the hippocampus was not. Our results indicate that disruption of the mGluR7a-PICK1 complex is sufficient to induce absence epilepsy—like seizures in rats and mice, thus providing, to the best of our knowledge, the first animal model of metabotropic glutamate receptor—PDZ protein interaction in absence epilepsy. PMID:18641645

Is it juvenile myoclonic epilepsy?

PubMed

Gelisse, P; Genton, P; Raybaud, C; Thomas, P; Bartolomei, F; Dravet, C

2000-03-01

A 21-year old man with marked developmental delay was referred for the diagnosis of myoclonic jerks (MJ), which were sometimes responsible for sudden falls without loss of consciousness, that had begun 2 years before, and for a recent generalized tonic-clonic seizure preceded by a cluster of MJ. Physical examination revealed a small stature, bilateral pyramidal signs, severe mental retardation, and retinis pigmentosa. Etiological factors for this encephalopathy were not found (muscle and skin biopsies, karyotype and extensive blood chemistry). Waking interictal EEG showed a normal background activity and generalized poly-spike-and wave (PSW) discharges. Photic stimulation disclosed a marked photoparoxysmal response, sometimes associated with myoclonic jerks. Three spontaneous jerks accompanied by a burst of generalized PSW were recorded on awakening from a nap. The MRI disclosed wide ventricles, a thin corpus callosum, brainstem atrophy and a so-called "redundant gyration"; these changes were evocative of acquired perinatal damage. Juvenile myoclonic epilepsy (JME) was diagnosed and valproate was started resulting in complete control of seizures. During a 5-year follow-up, the patient has remained seizure-free and the EEG consistently normal. In our opinion, JME can be diagnosed in very uncommon settings, including patients with significant brain damage, as long as all the other criteria for the diagnosis are present.

CDKL5 gene-related epileptic encephalopathy: electroclinical findings in the first year of life.

PubMed

Melani, Federico; Mei, Davide; Pisano, Tiziana; Savasta, Salvatore; Franzoni, Emilio; Ferrari, Anna Rita; Marini, Carla; Guerrini, Renzo

2011-04-01

Cyclin-dependent kinase-like 5 (CDKL5) gene abnormalities cause an early-onset epileptic encephalopathy. We performed video-electroencephalography (video-EEG) monitoring early in the course of CDKL5-related epileptic encephalopathy in order to examine the early electroclinical characteristics of the condition. We used video-EEG to monitor six infants (five females, one male) with CDKL5-related epileptic encephalopathy (five mutations; one deletion), at ages 45 days to 12 months and followed them up to the ages of 14 months to 5 years (mean age 23 mo). We focused our analysis on the first year of life. The results were evaluated against those of a comparison group of nine infants (aged below 1y) with epileptic encephalography who had tested negative for CDKL5 mutations and deletions. One infant exhibited normal background activity, three exhibited moderate slowing, and two exhibited a suppression burst pattern. Two participants had epileptic spasms and four had a stereotyped complex seizure pattern, which we defined as a 'prolonged' generalized tonic-clonic event consisting of a tonic-tonic/vibratory contraction, followed by a clonic phase with series of spasms, gradually translating into repetitive distal myoclonic jerks. Seizure duration ranged from 2 to 4 minutes. The EEG correlate of each clinical phase included an initial electrodecremental event (tonic vibratory phase), irregular series of sharp waves and spike slow waves (clonic phase with series of spasms), and bilateral rhythmic sharp waves (time locked with myoclonus). Infants with CDKL5-related early epileptic encephalopathy can present in the first year of life with an unusual electroclinical pattern of 'prolonged' generalized tonic-clonic seizures. © The Authors. Journal compilation © Mac Keith Press 2011.

Low-frequency stimulation in anterior nucleus of thalamus alleviates kainate-induced chronic epilepsy and modulates the hippocampal EEG rhythm.

PubMed

Wang, Yi; Liang, Jiao; Xu, Cenglin; Wang, Ying; Kuang, Yifang; Xu, Zhenghao; Guo, Yi; Wang, Shuang; Gao, Feng; Chen, Zhong

2016-02-01

High-frequency stimulation (HFS) of the anterior nucleus of thalamus (ANT) is a new and alternative option for the treatment of intractable epilepsy. However, the responder rate is relatively low. The present study was designed to determine the effect of low-frequency stimulation (LFS) in ANT on chronic spontaneous recurrent seizures and related pathological pattern in intra-hippocampal kainate mouse model. We found that LFS (1 Hz, 100 μs, 300 μA), but not HFS (100 Hz, 100 μs, 30 μA), in bilateral ANT significantly decreased the frequency of spontaneous recurrent seizures, either non-convulsive focal seizures or tonic-clonic generalized seizures. The anti-epileptic effect persisted for one week after LFS cessation, which manifested as a long-term inhibition of the frequency of seizures with short (20-60 s) and intermediate duration (60-120 s). Meanwhile, LFS decreased the frequency of high-frequency oscillations (HFOs) and interictal spikes, two indicators of seizure severity, whereas HFS increased the HFO frequency. Furthermore, LFS decreased the power of the delta band and increased the power of the gamma band of hippocampal background EEG. In addition, LFS, but not HFS, improved the performance of chronic epileptic mice in objection-location task, novel objection recognition and freezing test. These results provide the first evidence that LFS in ANT alleviates kainate-induced chronic epilepsy and cognitive impairment, which may be related to the modulation of the hippocampal EEG rhythm. This may be of great therapeutic significance for clinical treatment of epilepsy with deep brain stimulation. Copyright © 2015 Elsevier Inc. All rights reserved.