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Semi-automatic identification of independent components representing EEG artifact.

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Semi-automatic identification of independent components representing EEG artifact. / Viola, Filipa Campos; Thorne, Jeremy; Edmonds, Barrie; Schneider, Till; Eichele, Tom; Debener, Stefan.

in: CLIN NEUROPHYSIOL, Jahrgang 120, Nr. 5, 5, 2009, S. 868-877.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Viola, FC, Thorne, J, Edmonds, B, Schneider, T, Eichele, T & Debener, S 2009, 'Semi-automatic identification of independent components representing EEG artifact.', CLIN NEUROPHYSIOL, Jg. 120, Nr. 5, 5, S. 868-877. <http://www.ncbi.nlm.nih.gov/pubmed/19345611?dopt=Citation>

APA

Viola, F. C., Thorne, J., Edmonds, B., Schneider, T., Eichele, T., & Debener, S. (2009). Semi-automatic identification of independent components representing EEG artifact. CLIN NEUROPHYSIOL, 120(5), 868-877. [5]. http://www.ncbi.nlm.nih.gov/pubmed/19345611?dopt=Citation

Vancouver

Viola FC, Thorne J, Edmonds B, Schneider T, Eichele T, Debener S. Semi-automatic identification of independent components representing EEG artifact. CLIN NEUROPHYSIOL. 2009;120(5):868-877. 5.

Bibtex

@article{8f94ef90749b4aa3a0f394af5f239b1f,
title = "Semi-automatic identification of independent components representing EEG artifact.",
abstract = "OBJECTIVE: Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related signals. However, the identification and interpretation of independent components is time-consuming and involves subjective decision making. We developed and evaluated a semi-automatic tool designed for clustering independent components from different subjects and/or EEG recordings. METHODS: CORRMAP is an open-source EEGLAB plug-in, based on the correlation of ICA inverse weights, and finds independent components that are similar to a user-defined template. Component similarity is measured using a correlation procedure that selects components that pass a threshold. The threshold can be either user-defined or determined automatically. CORRMAP clustering performance was evaluated by comparing it with the performance of 11 users from different laboratories familiar with ICA. RESULTS: For eye-related artifacts, a very high degree of overlap between users (phi>0.80), and between users and CORRMAP (phi>0.80) was observed. Lower degrees of association were found for heartbeat artifact components, between users (phi",
author = "Viola, {Filipa Campos} and Jeremy Thorne and Barrie Edmonds and Till Schneider and Tom Eichele and Stefan Debener",
year = "2009",
language = "Deutsch",
volume = "120",
pages = "868--877",
journal = "CLIN NEUROPHYSIOL",
issn = "1388-2457",
publisher = "Elsevier",
number = "5",

}

RIS

TY - JOUR

T1 - Semi-automatic identification of independent components representing EEG artifact.

AU - Viola, Filipa Campos

AU - Thorne, Jeremy

AU - Edmonds, Barrie

AU - Schneider, Till

AU - Eichele, Tom

AU - Debener, Stefan

PY - 2009

Y1 - 2009

N2 - OBJECTIVE: Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related signals. However, the identification and interpretation of independent components is time-consuming and involves subjective decision making. We developed and evaluated a semi-automatic tool designed for clustering independent components from different subjects and/or EEG recordings. METHODS: CORRMAP is an open-source EEGLAB plug-in, based on the correlation of ICA inverse weights, and finds independent components that are similar to a user-defined template. Component similarity is measured using a correlation procedure that selects components that pass a threshold. The threshold can be either user-defined or determined automatically. CORRMAP clustering performance was evaluated by comparing it with the performance of 11 users from different laboratories familiar with ICA. RESULTS: For eye-related artifacts, a very high degree of overlap between users (phi>0.80), and between users and CORRMAP (phi>0.80) was observed. Lower degrees of association were found for heartbeat artifact components, between users (phi

AB - OBJECTIVE: Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related signals. However, the identification and interpretation of independent components is time-consuming and involves subjective decision making. We developed and evaluated a semi-automatic tool designed for clustering independent components from different subjects and/or EEG recordings. METHODS: CORRMAP is an open-source EEGLAB plug-in, based on the correlation of ICA inverse weights, and finds independent components that are similar to a user-defined template. Component similarity is measured using a correlation procedure that selects components that pass a threshold. The threshold can be either user-defined or determined automatically. CORRMAP clustering performance was evaluated by comparing it with the performance of 11 users from different laboratories familiar with ICA. RESULTS: For eye-related artifacts, a very high degree of overlap between users (phi>0.80), and between users and CORRMAP (phi>0.80) was observed. Lower degrees of association were found for heartbeat artifact components, between users (phi

M3 - SCORING: Zeitschriftenaufsatz

VL - 120

SP - 868

EP - 877

JO - CLIN NEUROPHYSIOL

JF - CLIN NEUROPHYSIOL

SN - 1388-2457

IS - 5

M1 - 5

ER -