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Utility of Independent Component Analysis for Interpretation of Intracranial EEG

Electrode arrays are sometimes implanted in the brains of patients with intractable epilepsy to better localize seizure foci before epilepsy surgery. Analysis of intracranial EEG (iEEG) recordings is typically performed in the electrode channel domain without explicit separation of the sources that...

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Autores principales: Whitmer, Diane, Worrell, Gregory, Stead, Matt, Lee, Il Keun, Makeig, Scott
Formato: Texto
Lenguaje:English
Publicado: Frontiers Research Foundation 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2998050/
https://www.ncbi.nlm.nih.gov/pubmed/21152349
http://dx.doi.org/10.3389/fnhum.2010.00184
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author Whitmer, Diane
Worrell, Gregory
Stead, Matt
Lee, Il Keun
Makeig, Scott
author_facet Whitmer, Diane
Worrell, Gregory
Stead, Matt
Lee, Il Keun
Makeig, Scott
author_sort Whitmer, Diane
collection PubMed
description Electrode arrays are sometimes implanted in the brains of patients with intractable epilepsy to better localize seizure foci before epilepsy surgery. Analysis of intracranial EEG (iEEG) recordings is typically performed in the electrode channel domain without explicit separation of the sources that generate the signals. However, intracranial EEG signals, like scalp EEG signals, could be linear mixtures of local activity and volume-conducted activity arising in multiple source areas. Independent component analysis (ICA) has recently been applied to scalp EEG data, and shown to separate the signal mixtures into independently generated brain and non-brain source signals. Here, we applied ICA to unmix source signals from intracranial EEG recordings from four epilepsy patients during a visually cued finger movement task in the presence of background pathological brain activity. This ICA decomposition demonstrated that the iEEG recordings were not maximally independent, but rather are linear mixtures of activity from multiple sources. Many of the independent component (IC) projections to the iEEG recording grid were consistent with sources from single brain regions, including components exhibiting classic movement-related dynamics. Notably, the largest IC projection to each channel accounted for no more than 20–80% of the channel signal variance, implying that in general intracranial recordings cannot be accurately interpreted as recordings of independent brain sources. These results suggest that ICA can be used to identify and monitor major field sources of local and distributed functional networks generating iEEG data. ICA decomposition methods are useful for improving the fidelity of source signals of interest, likely including distinguishing the sources of pathological brain activity.
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spelling pubmed-29980502010-12-09 Utility of Independent Component Analysis for Interpretation of Intracranial EEG Whitmer, Diane Worrell, Gregory Stead, Matt Lee, Il Keun Makeig, Scott Front Hum Neurosci Neuroscience Electrode arrays are sometimes implanted in the brains of patients with intractable epilepsy to better localize seizure foci before epilepsy surgery. Analysis of intracranial EEG (iEEG) recordings is typically performed in the electrode channel domain without explicit separation of the sources that generate the signals. However, intracranial EEG signals, like scalp EEG signals, could be linear mixtures of local activity and volume-conducted activity arising in multiple source areas. Independent component analysis (ICA) has recently been applied to scalp EEG data, and shown to separate the signal mixtures into independently generated brain and non-brain source signals. Here, we applied ICA to unmix source signals from intracranial EEG recordings from four epilepsy patients during a visually cued finger movement task in the presence of background pathological brain activity. This ICA decomposition demonstrated that the iEEG recordings were not maximally independent, but rather are linear mixtures of activity from multiple sources. Many of the independent component (IC) projections to the iEEG recording grid were consistent with sources from single brain regions, including components exhibiting classic movement-related dynamics. Notably, the largest IC projection to each channel accounted for no more than 20–80% of the channel signal variance, implying that in general intracranial recordings cannot be accurately interpreted as recordings of independent brain sources. These results suggest that ICA can be used to identify and monitor major field sources of local and distributed functional networks generating iEEG data. ICA decomposition methods are useful for improving the fidelity of source signals of interest, likely including distinguishing the sources of pathological brain activity. Frontiers Research Foundation 2010-11-02 /pmc/articles/PMC2998050/ /pubmed/21152349 http://dx.doi.org/10.3389/fnhum.2010.00184 Text en Copyright © 2010 Whitmer, Worrell, Stead, Lee and Makeig. http://www.frontiersin.org/licenseagreement This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
spellingShingle Neuroscience
Whitmer, Diane
Worrell, Gregory
Stead, Matt
Lee, Il Keun
Makeig, Scott
Utility of Independent Component Analysis for Interpretation of Intracranial EEG
title Utility of Independent Component Analysis for Interpretation of Intracranial EEG
title_full Utility of Independent Component Analysis for Interpretation of Intracranial EEG
title_fullStr Utility of Independent Component Analysis for Interpretation of Intracranial EEG
title_full_unstemmed Utility of Independent Component Analysis for Interpretation of Intracranial EEG
title_short Utility of Independent Component Analysis for Interpretation of Intracranial EEG
title_sort utility of independent component analysis for interpretation of intracranial eeg
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2998050/
https://www.ncbi.nlm.nih.gov/pubmed/21152349
http://dx.doi.org/10.3389/fnhum.2010.00184
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