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Comparing Results of Algorithms Implementing Blind Source Separation of EEG Data

By A. Delorme, J. Palmer, R. Oostenveld, J. Onton and Scott Makeig

Abstract

to electroencephalographic (EEG) data, has proven capable of separating artifacts and brain sources. Of the variety of ICA and blind source separation algorithms now available, which are more efficient at processing EEG data? Here, we defined efficiency to mean blind separation of the data into near “dipolar ” components having scalp maps consistent with synchronous activity in a single cortical region. We applied 20 ICA algorithms as well as PCA, whitening, and PROMAX decomposition to 71-channel data from 14 subjects, and ranked the resulting decompositions by the number of near-dipolar components they identified. By this measure, Infomax and Pearson ICA ranked highest, though similar near-dipolar components were returned by most of the ICA-based algorithms. I I

Year: 2014
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