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Applications of ICA to electrophysiological data

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Definitions of terms -- EEG, MEG, ERP, ERF

Electromagnetic fields associated with brain processes and recorded outside the head produce electroencephalographic (EEG) and magnetoencephalographic (MEG) data. Averages of EEG epochs time-locked to a set of experimental events of interest are called event-related potentials (ERPs). Similar magnetic averages are known as event-related fields or ERFs.

Suitability for ICA decomposition

In the EEG/MEG frequency range (roughly 0.1-100 Hz) the mixing of brain fields at the scalp electrodes is basically linear. Although skull attenuates EEG signals strongly and "smears" (low-pass filters) them spatially, this does not affect the linear relation between potential in the brain and potential at the scalp. Fields propagate to the sensors (electrodes or SQUID coils) through volume conduction without significant delays. This makes EEG and MEG data suited to linear decomposition via ICA. A number of "frequently asked questions" about the application of ICA to averaged or spontaneous EEG/MEG data are answered in Frequently Asked Questions about ICA applied to EEG/MEG data.

First Applications

The ICA algorithm of Bell & Sejnowski was first applied to EEG and ERP data in Makeig S, Bell AJ, Jung T-P, and Sejnowski TJ, "Independent component analysis of electroencephalographic data." Advances in Neural Information Processing Systems 8, 145-151,1996. This paper demonstrated the successful decomposition of 14-channel ERP data consisting of only 624 data points. Further details have now been published in a PNAS paper on ICA applied to ERP data. Preprint html and Postscript versions of this paper are also available for review and download from this site.

EEGLAB Toolbox

An interactive Matlab toolbox for EEG/MEG analysis using ICA, called EEGLAB, is available. The toolbox consists of scripts for ICA decomposition and plotting of results, together with general-purpose EEG plotting and computational routines. Please see the EEGLAB homepage at the Swartz Center for Computational Neuroscience (UCSD) for more information.

Bibliography of publications on biomedical applications of ICA