Complex Independent Component Analysis of Frequency-Domain Electroencephalographic Data

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dc.creatorAnemuller, Jorn
dc.creatorSejnowski, Terrence J.
dc.creatorMakeig, Scott
dc.date2003-10-10
dc.date2003-11-25
dc.date.accessioned2026-07-07T05:57:54Z
dc.date.available2026-07-07T05:57:54Z
dc.descriptionIndependent 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.
dc.description21 pages, 11 figures. Added final journal reference, fixed minor typos
dc.identifierhttps://arxiv.org/abs/q-bio/0310011
dc.identifierhttp://arxiv.org/abs/q-bio/0310011
dc.identifierNeural Networks, 16:1311-1323, 2003
dc.identifierdoi:10.1016/j.neunet.2003.08.003
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/88161
dc.subjectQuantitative Methods
dc.subjectComputational Engineering, Finance, and Science
dc.subjectData Analysis, Statistics and Probability
dc.subjectNeurons and Cognition
dc.titleComplex Independent Component Analysis of Frequency-Domain Electroencephalographic Data
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