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Trends Cogn Sci. 2004 May;8(5):204-10.

Mining event-related brain dynamics.

Author information

1
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla 92093-0961, USA. smakeig@ucsd.edu <smakeig@ucsd.edu>

Abstract

This article provides a new, more comprehensive view of event-related brain dynamics founded on an information-based approach to modeling electroencephalographic (EEG) dynamics. Most EEG research focuses either on peaks 'evoked' in average event-related potentials (ERPs) or on changes 'induced' in the EEG power spectrum by experimental events. Although these measures are nearly complementary, they do not fully model the event-related dynamics in the data, and cannot isolate the signals of the contributing cortical areas. We propose that many ERPs and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.

PMID:
15120678
DOI:
10.1016/j.tics.2004.03.008
[Indexed for MEDLINE]
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