Complex independent component analysis of frequency-domain electroencephalographic data

  • Authors:

  • Jörn Anemüller;Terrence J. Sejnowski;Scott Makeig

  • Affiliations:

  • Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego and Computational Neurobiology Laboratory, The Salk Institute for Biological Stud ...;Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego and Computational Neurobiology Laboratory, The Salk Institute for Biological Stud ...;Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego and Computational Neurobiology Laboratory, The Salk Institute for Biological Stud ...

  • Venue:
  • Neural Networks - Special issue: Neuroinformatics
  • Year:
  • 2003

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Abstract

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.