Comparing ICA-based and single-trial topographic ERP analyses.

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Serval ID
serval:BIB_D5882A144376
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Comparing ICA-based and single-trial topographic ERP analyses.
Journal
Brain topography
Author(s)
De Lucia M., Michel C.M., Murray M.M.
ISSN
1573-6792 (Electronic)
ISSN-L
0896-0267
Publication state
Published
Issued date
06/2010
Peer-reviewed
Oui
Volume
23
Number
2
Pages
119-127
Language
english
Notes
Publication types: Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
Single-trial analysis of human electroencephalography (EEG) has been recently proposed for better understanding the contribution of individual subjects to a group-analysis effect as well as for investigating single-subject mechanisms. Independent Component Analysis (ICA) has been repeatedly applied to concatenated single-trial responses and at a single-subject level in order to extract those components that resemble activities of interest. More recently we have proposed a single-trial method based on topographic maps that determines which voltage configurations are reliably observed at the event-related potential (ERP) level taking advantage of repetitions across trials. Here, we investigated the correspondence between the maps obtained by ICA versus the topographies that we obtained by the single-trial clustering algorithm that best explained the variance of the ERP. To do this, we used exemplar data provided from the EEGLAB website that are based on a dataset from a visual target detection task. We show there to be robust correspondence both at the level of the activation time courses and at the level of voltage configurations of a subset of relevant maps. We additionally show the estimated inverse solution (based on low-resolution electromagnetic tomography) of two corresponding maps occurring at approximately 300 ms post-stimulus onset, as estimated by the two aforementioned approaches. The spatial distribution of the estimated sources significantly correlated and had in common a right parietal activation within Brodmann's Area (BA) 40. Despite their differences in terms of theoretical bases, the consistency between the results of these two approaches shows that their underlying assumptions are indeed compatible.

Keywords
Algorithms, Brain/physiology, Brain Mapping/methods, Cluster Analysis, Databases as Topic, Electroencephalography/methods, Evoked Potentials, Functional Laterality, Humans, Internet, Models, Neurological, Normal Distribution, Parietal Lobe/physiology, Probability, Signal Processing, Computer-Assisted, Time Factors, Visual Perception/physiology
Pubmed
Web of science
Open Access
Yes
Create date
08/06/2010 16:30
Last modification date
01/10/2019 7:19
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