Independent Component Analysis of Electroencephalographic Data

TLDR

EEG signals recorded from the scalp are spatially smeared by volume conduction between the skull and brain, but without significant time delays, making the Bell–Sejnowski ICA algorithm suitable for blind source separation and decoupling source identification from localization. ICA applied to EEG and ERP data in an auditory detection task proved seed‑insensitive, effectively removed artifacts, isolated overlapping alpha, theta, and ERP components, and tracked nonstationarities through changes in residual correlation among ICA outputs.

Abstract

Because of the distance between the skull and brain and their different 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, suggesting that the Independent Component Analysis (ICA) algorithm of Bell and Sejnowski [1] is suitable for performing blind source separation 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 alpha and theta bursts and spatially-separable ERP components, to separate ICA channels. (4) Nonstationarities in EEG and behavioral state can be tracked using ICA via changes in the amount of residual correlation between ICA-filtered output channels.