INDEPENDENT COMPONENT ANALYSIS OF ERPS COGNITIVE SCIENCE ONLINE, 2008 1 Independent Component Analysis of Event-Related Potentials

Independent component analysis (ICA) is a potentially powerful tool for analyzing event-related potentials (ERPs), one of the most popular measures of brain function in cognitive neuroscience. Based on the statistics of the electroencephalogram (EEG), from which ERPs are derived, ICA may be able to extract multiple, functionally distinct sources of an ERP generated by disparate regions of cerebral cortex. Extracting such sources greatly increases the informativeness of ERPs by providing a cleaner, less ambiguous measure of source activity and by facilitating the identification of this activity across different experimental paradigms. The main purpose of this review article is to explain the logic of ICA, to illustrate how ICA could in principle extract spatiotemporally overlapping ERP sources, and to review evidence that ICA is a well motivated methodology that can extract latent ERP sources in practice. In addition, we close the article by noting potential problems with ICA and by comparing it to three alternative methods for extracting ERP sources/components: spatial principal component analysis, source localization, and temporal principal component analysis. ue to their impeccable temporal resolution, noninvasiveness, and affordability, event-related potentials (ERPs) have been and continue to be one of the most popular measures of brain activity in cognitive neuroscience. ERP research has provided valuable insight into many domains of brain function (e.g., attention — Martinez et al., 1999, visual object recognition