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Complex Independent Component Analysis of Frequency-Domain Electroencephalographic Data | | Jorn Anemuller
; Terrence J. Sejnowski
; Scott Makeig
; | | Date: |
10 Oct 2003 | | Journal: | Neural Networks, 16:1311-1323, 2003 DOI: 10.1016/j.neunet.2003.08.003 | | Subject: | Quantitative Methods; Neurons and Cognition; Computational Engineering, Finance, and Science; Data Analysis, Statistics and Probability | q-bio.QM cs.CE physics.data-an q-bio.NC | | 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. | | Source: | arXiv, q-bio.QM/0310011 | | Services: | Forum | Review | PDF | Favorites | |
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