Journal
PROCEEDINGS OF THE IEEE
Volume 89, Issue 7, Pages 1107-1122Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/5.939827
Keywords
blind source separation; EEG; fMRI; independent component analysis
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Funding
- Howard Hughes Medical Institute Funding Source: Medline
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The analysis of electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging (fMRI) data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain.
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