Complex independent component analysis of frequency-domain electroencephalographic data.
Independent component analysis (ICA) has proven useful for modeling brain and electroencephalographic (EEG) data. Here, we present a new, generalized method to better capture the dynamics of brain signals than previous ICA algorithms. We regard EEG sources as eliciting spatio-temporal activity patterns, corresponding to, e.g. trajectories of activation propagating across cortex. This leads to a model of convolutive signal superposition, in contrast with the commonly used instantaneous mixing model. In the frequency-domain, convolutive mixing is equivalent to multiplicative mixing of complex signal sources within distinct spectral bands. We decompose the recorded spectral-domain signals into independent components by a complex infomax ICA algorithm. First results from a visual attention EEG experiment exhibit: (1). sources of spatio-temporal dynamics in the data, (2). links to subject behavior, (3). sources with a limited spectral extent, and (4). a higher degree of independence compared to sources derived by standard ICA.
Anemüller
Jörn
J
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, 9500 Gilman Dr, Dept 0961, La Jolla, CA 92093-0961, USA. jorn@salk.edu
Sejnowski
Terrence J
TJ
Makeig
Scott
S
D001931:Brain Mapping / Q000379:methods*; D004569:Electroencephalography / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans