Independent Component Analysis of Simulated EEG Using a Three-Shell Spherical Head Model

摘要

The Independent Component Analysis (ICA) algorithm is a new information-theoretic approach to the problem of separating multichannelelectr oencephalographic (EEG) data into independent sources. We tested the potential usefulness of the ICA algorithm for EEG source decomposition by applying the algorithm to simulated EEG data. These data were synthesized by projecting 6 known input signals from single- and multiple-dipole sources in a three-shell spherical model head to 6 simulated scalp sensors. In different simulations, we (1) altered the relative source strengths, (2) added multiple low-level sources (weak brain sources and sensor noise) to the simulated EEG, and (3) permuted the simulated dipole source locations and orientations. The algorithm successfully and reliably separated the activities of relatively strong sources from the activities of weaker brain sources and sensor noise, regardless of source lo- cations and dipole orientations. These results suggest that the ICA algorithm should be able to separate temporally independent but spatially overlapping EEG activities arising from relatively strong brain and/or non-brain sources, regardless of their spatial distributions.