Complex independent component analysis of frequency-domain electroencephalographic data

Neural Netw. 2003 Nov;16(9):1311-23. doi: 10.1016/j.neunet.2003.08.003.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brain Mapping / methods*
  • Electroencephalography / methods*
  • Electroencephalography / statistics & numerical data
  • Humans