Independent EEG Sources Are Dipolar
Centre National de la Recherche Scientifique · University of California San Diego · +4 more institutions
Abstract
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…
Citation impact
- FWCI
- 35.40
- Percentile
- 100%
- References
- 59
Authors
5- ADArnaud DelormeCorresponding
Centre National de la Recherche Scientifique, University of California San Diego, Centre de recherche cerveau et cognition, Université Toulouse III - Paul Sabatier
- JPJason Palmer
University of California San Diego
- JOJulie Onton
University of California San Diego, Naval Health Research Center
- RORobert Oostenveld
Radboud University Nijmegen
- SMScott Makeig
University of California San Diego
Topics & keywords
- Independent component analysis
- Principal component analysis
- Electroencephalography
- Pattern recognition (psychology)
- Mutual information
- Computer science
- Blind signal separation
- Dimensionality reduction