Independent Component Analysis of Electroencephalographic Data

Because of the distance between the skull and brain and their dier-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 signicant time delays, however, sug-gesting that the Independent Component Analysis (ICA) algorithm of Bell and Sejnowski [1] is suitable for performing blind source sep-aration on EEG data. The ICA algorithm separates the problem of source identication 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 dierent 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-pha and theta bursts and spatially-separable ERP components, to separate ICA channels. (4) Nonstationarities in EEG and behav-ioral state can be tracked using ICA via changes in the amount of residual correlation between ICA-ltered output channels.