Imaging human EEG dynamics using independent component analysis.
This review discusses the theory and practical application of independent component analysis (ICA) to multi-channel EEG data. We use examples from an audiovisual attention-shifting task performed by young and old subjects to illustrate the power of ICA to resolve subtle differences between evoked responses in the two age groups. Preliminary analysis of these data using ICA suggests a loss of task specificity in independent component (IC) processes in frontal and somatomotor cortex during post-response periods in older as compared to younger subjects, trends not detected during examination of scalp-channel event-related potential (ERP) averages. We discuss possible approaches to component clustering across subjects and new ways to visualize mean and trial-by-trial variations in the data, including ERP-image plots of dynamics within and across trials as well as plots of event-related spectral perturbations in component power, phase locking, and coherence. We believe that widespread application of these and related analysis methods should bring EEG once again to the forefront of brain imaging, merging its high time and frequency resolution with enhanced cm-scale spatial resolution of its cortical sources.
Onton
Julie
J
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093-0961, USA.
Westerfield
Marissa
M
Townsend
Jeanne
J
Makeig
Scott
S
D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001931:Brain Mapping; D003952:Diagnostic Imaging; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D017711:Nonlinear Dynamics; D025341:Principal Component Analysis