Skip to main content
Article thumbnail
Location of Repository

Comparing Results of Algorithms Implementing Blind Source Separation of EEG Data

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


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
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.