The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Complex independent component analysis of frequency-domain electroencephalographic data
Open accessAbstract
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.
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Cites
The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800020109 · Cited by 13,120
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Dynamic Brain Sources of Visual Evoked Responses
Makeig, S., Westerfield, M., Jung, T.-P., Enghoff, S., Townsend, J., Courchesne, E., & Sejnowski, T. J. (2002) · Science · doi:10.1126/science.1066168 · Cited by 1,630
Self-citationStimulus novelty, task relevance and the visual evoked potential in man
Courchesne, E., Hillyard, S. A., & Galambos, R. (1975) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(75)90003-6 · Cited by 1,012
Cited by
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
Self-citationMining event-related brain dynamics
Makeig, S., Debener, S., Onton, J., & Delorme, A. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.03.008 · Cited by 1,493
Self-citationPetersen, K. B., & Pedersen, M. S. (2012) · doi:10.5281/zenodo.8002926 · Cited by 1,434
Independent EEG Sources Are Dipolar
Delorme, A., Palmer, J., Onton, J., Oostenveld, R., & Makeig, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0030135 · Cited by 810
Self-citationComplex-Valued Signal Processing: The Proper Way to Deal With Impropriety
Adali, T., Schreier, P. J., & Scharf, L. L. (2011) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2011.2162954 · Cited by 398
Metadata
- Authors
- Anemüller, J., Sejnowski, T. J., & Makeig, S.
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2003-11
- Volume
- 16
- Issue
- 9
- Pages
- 1311-1323
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W2112345337
- Cited by
- 212
- References
- 4
- FWCI
- 14.20
- Type
- journal article
- Language
- en
- Open access
- green
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