Information-based modeling of event-related brain dynamics.

We discuss the theory and practice of applying independent component analysis (ICA) to electroencephalographic (EEG) data. ICA blindly decomposes multi-channel EEG data into maximally independent component processes (ICs) that typically express either particularly brain generated EEG activities or some type of non-brain artifacts (line or other environmental noise, eye blinks and other eye movements, or scalp or heart muscle activity). Each brain and non-brain IC is identified with an activity time course (its 'activation') and a set of relative strengths of its projections (by volume conduction) to the recording electrodes (its 'scalp map'). Many non-articraft IC scalp maps strongly resemble the projection of a single dipole, allowing the location and orientation of the best-fitting equivalent dipole (or other source model) to be easily determined. In favorable circumstances, ICA decomposition of high-density scalp EEG data appears to allow concurrent monitoring, with high time resolution, of separate EEG activities in twenty or more separate cortical EEG source areas. We illustrate the differences between ICA and traditional approaches to EEG analysis by comparing time courses and mean event related spectral perturbations (ERSPs) of scalp channel and IC data. Comparing IC activities across subjects necessitates clustering of similar Ics based on common dynamic and/or spatial features. We discuss and illustrate such a component clustering strategy. In sum, continued application of ICA methods in EEG research should continue to yield new insights into the nature and role of the complex macroscopic cortical dynamics captured by scalp electrode recordings.