Imaging Brain Dynamics Using Independent Component Analysis.
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.
Jung
Tzyy-Ping
TP
University of California at San Diego, La Jolla, CA 92093-0523 USA and also with The Salk Institute for Biological Studies, La Jolla, CA 92037 USA.
Makeig
Scott
S
McKeown
Martin J
MJ
Bell
Anthony J
AJ
Lee
Te-Won
TW
Sejnowski
Terrence J
TJ
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