Complex Independent Component Analysis of Frequency-Domain Electroencephalographic Data
<|reference_start|>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.<|reference_end|>
@article{anemuller2003complex,
    title={Complex Independent Component Analysis of Frequency-Domain
  Electroencephalographic Data},
    author={Jorn Anemuller, Terrence J. Sejnowski, Scott Makeig},
    journal={Neural Networks, 16:1311-1323, 2003},
    year={2003},
    doi={10.1016/j.neunet.2003.08.003},
    archivePrefix={arXiv},
    eprint={q-bio/0310011},
    primaryClass={q-bio.QM cs.CE physics.data-an q-bio.NC}
}