Mining event-related brain dynamics.
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.
Makeig
Scott
S
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>
Debener
Stefan
S
Onton
Julie
J
Delorme
Arnaud
A
D000200:Action Potentials / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000295:instrumentation*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015225:Magnetoencephalography / Q000379:methods; D008959:Models, Neurological; D015203:Reproducibility of Results