Scott Makeig, Anthony Bell, Tzyy-Ping Jung, Terrence J. Sejnowski
Because of the distance between the skull and brain and their differ(cid:173) ent resistivities, electroencephalographic (EEG) data collected from any point on the human scalp includes activity generated within a large brain area. This spatial smearing of EEG data by volume conduction does not involve significant time delays, however, sug(cid:173) gesting that the Independent Component Analysis (ICA) algorithm of Bell and Sejnowski  is suitable for performing blind source sep(cid:173) aration on EEG data. The ICA algorithm separates the problem of source identification from that of source localization. First results of applying the ICA algorithm to EEG and event-related potential (ERP) data collected during a sustained auditory detection task show: (1) ICA training is insensitive to different random seeds. (2) ICA may be used to segregate obvious artifactual EEG components (line and muscle noise, eye movements) from other sources. (3) ICA is capable of isolating overlapping EEG phenomena, including al(cid:173) pha and theta bursts and spatially-separable ERP components, to separate ICA channels. (4) N onstationarities in EEG and behav(cid:173) ioral state can be tracked using ICA via changes in the amount of residual correlation between ICA-filtered output channels.
S. MAKEIG, A. l . BELL, T.-P. lUNG, T. l. SEJNOWSKI