F10. Co-localization of ictal and interictal activity using high density EEG data

Charles Bonnet syndrome (CBS) is a rare clinical condition characterized by complex visual hallucinations in people with loss of vision. So far, the neurobiological mechanisms underlying the hallucinations remain elusive. This case-report study aims at investigating electrical activity changes in a CBS patient during visual hallucinations, as compared to a resting-state period (without hallucinations). Prior to the EEG, the patient underwent neuropsychological, ophthalmologic, and neurological examinations. Spectral and connectivity, graph analyses and signal diversity were applied to high-density EEG data. Visual hallucinations (as compared to resting-state) were characterized by a significant reduction of power in the frontal areas, paralleled by an increase in the midline posterior regions in delta and theta bands and by an increase of alpha power in the occipital and midline posterior regions. We next observed a reduction of theta connectivity in the frontal and right posterior areas, which at a network level was complemented by a disruption of small-worldness (lower local and global efficiency) and by an increase of network modularity. Finally, we found a higher signal complexity especially when considering the frontal areas in the alpha band. The emergence of hallucinations may stem from these changes in the visual cortex and in core cortical regions encompassing both the default mode and the fronto-parietal attentional networks.

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Smartphone Household Wireless Electroencephalogram Hat

Applied Computational Intelligence and Soft Computing ◽

10.1155/2013/241489 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 4

Author(s):

Harold Szu ◽

Charles Hsu ◽

Gyu Moon ◽

Takeshi Yamakawa ◽

Binh Q. Tran ◽

...

Keyword(s):

Blind Deconvolution ◽

Impulse Response Function ◽

High Density ◽

Gaming Industry ◽

Dry Electrodes ◽

Eeg Data ◽

Close Proximity ◽

Ill Posed ◽

Parallel Frame ◽

Future Work

Rudimentarybrain machine interfacehas existed for the gaming industry. Here, we propose a wireless, real-time, and smartphone-based electroencephalogram (EEG) system for homecare applications. The system uses high-density dry electrodes and compressive sensing strategies to overcome conflicting requirements between spatial electrode density, temporal resolution, and spatiotemporal throughput rate.Spatial sparsenessis addressed by close proximity between active electrodes and desired source locations and using an adaptive selection ofNactive among10Npassive electrodes to formm-organized random linear combinations of readouts,m≪N≪10N.Temporal sparsenessis addressed via parallel frame differences in hardware. During the design phase, we took tethered laboratory EEG dataset and applied fuzzy logic to compute (a) spatiotemporal average of larger magnitude EEG data centers in 0.3 second intervals and (b) inside brainwave sources by Independent Component Analysis blind deconvolution without knowing the impulse response function. Our main contributions are the fidelity of quality wireless EEG data compared to original tethered data and the speed of compressive image recovery. We have compared our recovery of ill-posed inverse data against results using Block Sparse Code. Future work includes development of strategies to filter unwanted artifact from high-density EEGs (i.e., facial muscle-related events and wireless environmental electromagnetic interferences).

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P300 Analysis Using High-Density EEG to Decipher Neural Response to rTMS in Patients With Schizophrenia and Auditory Verbal Hallucinations

Frontiers in Neuroscience ◽

10.3389/fnins.2020.575538 ◽

2020 ◽

Vol 14 ◽

Author(s):

Romain Aubonnet ◽

Ovidiu C. Banea ◽

Roberta Sirica ◽

Eric M. Wassermann ◽

Sahar Yassine ◽

...

Keyword(s):

Data Analysis ◽

Transcranial Magnetic Stimulation ◽

Time Domain ◽

Magnetic Stimulation ◽

High Density ◽

Control Group ◽

Auditory Verbal Hallucinations ◽

Eeg Data ◽

Quality Of Life Scale ◽

The Time Domain

Schizophrenia is a complex disorder about which much is still unknown. Potential treatments, such as transcranial magnetic stimulation (TMS), have not been exploited, in part because of the variability in behavioral response. This can be overcome with the use of response biomarkers. It has been however shown that repetitive transcranial magnetic stimulation (rTMS) can the relieve positive and negative symptoms of schizophrenia, particularly auditory verbal hallucinations (AVH). This exploratory work aims to establish a quantitative methodological tool, based on high-density electroencephalogram (HD-EEG) data analysis, to assess the effect of rTMS on patients with schizophrenia and AVH. Ten schizophrenia patients with drug-resistant AVH were divided into two groups: the treatment group (TG) received 1 Hz rTMS treatment during 10 daily sessions (900 pulses/session) over the left T3-P3 International 10-20 location. The control group (CG) received rTMS treatment over the Cz (vertex) EEG location. We used the P300 oddball auditory paradigm, known for its reduced amplitude in schizophrenia with AVH, and recorded high-density electroencephalography (HD-EEG, 256 channels), twice for each patient: pre-rTMS and 1 week post-rTMS treatment. The use of HD-EEG enabled the analysis of the data in the time domain, but also in the frequency and source-space connectivity domains. The HD-EEG data were linked with the clinical outcome derived from the auditory hallucinations subscale (AHS) of the Psychotic Symptom Rating Scale (PSYRATS), the Quality of Life Scale (QoLS), and the Depression, Anxiety and Stress Scale (DASS). The general results show a variability between subjects, independent of the group they belong to. The time domain showed a higher N1-P3 amplitude post-rTMS, the frequency domain a higher power spectral density (PSD) in the alpha and beta bands, and the connectivity analysis revealed a higher brain network integration (quantified using the participation coefficient) in the beta band. Despite the small number of subjects and the high variability of the results, this work shows a robust data analysis and an interplay between morphology, spectral, and connectivity data. The identification of a trend post-rTMS for each domain in our results is a first step toward the definition of quantitative neurophysiological parameters to assess rTMS treatment.

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High-density scalp EEG data acquired in an inattentional blindness paradigm with background Gestalt stimuli

Data in Brief ◽

10.1016/j.dib.2019.104901 ◽

2020 ◽

Vol 28 ◽

pp. 104901 ◽

Cited By ~ 1

Author(s):

Alexandre de P. Nobre ◽

Andrey R. Nikolaev ◽

Johan Wagemans

Keyword(s):

Inattentional Blindness ◽

High Density ◽

Scalp Eeg ◽

Eeg Data

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Source localization of ictal epileptic activity based on high-density scalp EEG data

Epilepsia ◽

10.1111/epi.13749 ◽

2017 ◽

Vol 58 (6) ◽

pp. 1027-1036 ◽

Cited By ~ 41

Author(s):

Petros Nemtsas ◽

Gwenael Birot ◽

Francesca Pittau ◽

Christoph M. Michel ◽

Karl Schaller ◽

...

Keyword(s):

Source Localization ◽

Epileptic Activity ◽

High Density ◽

Scalp Eeg ◽

Eeg Data

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

PLoS ONE ◽

10.1371/journal.pone.0178409 ◽

2017 ◽

Vol 12 (5) ◽

pp. e0178409 ◽

Cited By ~ 11

Author(s):

Mette Thrane Foged ◽

Ulrich Lindberg ◽

Kishore Vakamudi ◽

Henrik B. W. Larsson ◽

Lars H. Pinborg ◽

...

Keyword(s):

Data Quality ◽

High Speed ◽

High Density ◽

3 Tesla ◽

Eeg Data

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Graph Theory Analysis of Functional Connectivity in Major Depression Disorder with High-Density Resting State EEG data

IEEE Transactions on Neural Systems and Rehabilitation Engineering ◽

10.1109/tnsre.2019.2894423 ◽

2019 ◽

pp. 1-1 ◽

Cited By ~ 3

Author(s):

Shuting Sun ◽

Xiaowei Li ◽

Jing Zhu ◽

Ying Wang ◽

Rong La ◽

...

Keyword(s):

Graph Theory ◽

Major Depression ◽

Functional Connectivity ◽

Resting State ◽

High Density ◽

Major Depression Disorder ◽

Theory Analysis ◽

Eeg Data ◽

Graph Theory Analysis ◽

Depression Disorder

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Automated classification of pain perception using high-density electroencephalography data

Journal of Neurophysiology ◽

10.1152/jn.00650.2016 ◽

2017 ◽

Vol 117 (2) ◽

pp. 786-795 ◽

Cited By ~ 25

Author(s):

Gaurav Misra ◽

Wei-en Wang ◽

Derek B. Archer ◽

Arnab Roy ◽

Stephen A. Coombes

Keyword(s):

Prefrontal Cortex ◽

Biological Markers ◽

Sensorimotor Cortex ◽

Pain Perception ◽

High Density ◽

Cortical Region ◽

Automated Classification ◽

Beta Power ◽

Eeg Data

The translation of brief, millisecond-long pain-eliciting stimuli to the subjective perception of pain is associated with changes in theta, alpha, beta, and gamma oscillations over sensorimotor cortex. However, when a pain-eliciting stimulus continues for minutes, regions beyond the sensorimotor cortex, such as the prefrontal cortex, are also engaged. Abnormalities in prefrontal cortex have been associated with chronic pain states, but conventional, millisecond-long EEG paradigms do not engage prefrontal regions. In the current study, we collected high-density EEG data during an experimental paradigm in which subjects experienced a 4-s, low- or high-intensity pain-eliciting stimulus. EEG data were analyzed using independent component analyses, EEG source localization analyses, and measure projection analyses. We report three novel findings. First, an increase in pain perception was associated with an increase in gamma and theta power in a cortical region that included medial prefrontal cortex. Second, a decrease in lower beta power was associated with an increase in pain perception in a cortical region that included the contralateral sensorimotor cortex. Third, we used machine learning for automated classification of EEG data into low- and high-pain classes. Theta and gamma power in the medial prefrontal region and lower beta power in the contralateral sensorimotor region served as features for classification. We found a leave-one-out cross-validation accuracy of 89.58%. The development of biological markers for pain states continues to gain traction in the literature, and our findings provide new information that advances this body of work.NEW & NOTEWORTHY The development of a biological marker for pain continues to gain traction in literature. Our findings show that high- and low-pain perception in human subjects can be classified with 89% accuracy using high-density EEG data from prefrontal cortex and contralateral sensorimotor cortex. Our approach represents a novel neurophysiological paradigm that advances the literature on biological markers for pain.

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