| pubmed-article:14622887 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:14622887 | lifeskim:mentions | umls-concept:C1179435 | lld:lifeskim |
| pubmed-article:14622887 | lifeskim:mentions | umls-concept:C0085862 | lld:lifeskim |
| pubmed-article:14622887 | lifeskim:mentions | umls-concept:C1511726 | lld:lifeskim |
| pubmed-article:14622887 | lifeskim:mentions | umls-concept:C1299583 | lld:lifeskim |
| pubmed-article:14622887 | lifeskim:mentions | umls-concept:C0439855 | lld:lifeskim |
| pubmed-article:14622887 | lifeskim:mentions | umls-concept:C0936012 | lld:lifeskim |
| pubmed-article:14622887 | lifeskim:mentions | umls-concept:C1705248 | lld:lifeskim |
| pubmed-article:14622887 | lifeskim:mentions | umls-concept:C1608386 | lld:lifeskim |
| pubmed-article:14622887 | lifeskim:mentions | umls-concept:C1548799 | lld:lifeskim |
| pubmed-article:14622887 | lifeskim:mentions | umls-concept:C1549571 | lld:lifeskim |
| pubmed-article:14622887 | lifeskim:mentions | umls-concept:C1524073 | lld:lifeskim |
| pubmed-article:14622887 | lifeskim:mentions | umls-concept:C0449432 | lld:lifeskim |
| pubmed-article:14622887 | pubmed:issue | 9 | lld:pubmed |
| pubmed-article:14622887 | pubmed:dateCreated | 2003-11-19 | lld:pubmed |
| pubmed-article:14622887 | pubmed:abstractText | 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. | lld:pubmed |
| pubmed-article:14622887 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:14622887 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:14622887 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:14622887 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:14622887 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:14622887 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:14622887 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:14622887 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:14622887 | pubmed:language | eng | lld:pubmed |
| pubmed-article:14622887 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:14622887 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:14622887 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:14622887 | pubmed:month | Nov | lld:pubmed |
| pubmed-article:14622887 | pubmed:issn | 0893-6080 | lld:pubmed |
| pubmed-article:14622887 | pubmed:author | pubmed-author:MakeigScottS | lld:pubmed |
| pubmed-article:14622887 | pubmed:author | pubmed-author:SejnowskiTerr... | lld:pubmed |
| pubmed-article:14622887 | pubmed:author | pubmed-author:AnemüllerJörn... | lld:pubmed |
| pubmed-article:14622887 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:14622887 | pubmed:volume | 16 | lld:pubmed |
| pubmed-article:14622887 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:14622887 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:14622887 | pubmed:pagination | 1311-23 | lld:pubmed |
| pubmed-article:14622887 | pubmed:dateRevised | 2010-9-20 | lld:pubmed |
| pubmed-article:14622887 | pubmed:meshHeading | pubmed-meshheading:14622887... | lld:pubmed |
| pubmed-article:14622887 | pubmed:meshHeading | pubmed-meshheading:14622887... | lld:pubmed |
| pubmed-article:14622887 | pubmed:meshHeading | pubmed-meshheading:14622887... | lld:pubmed |
| pubmed-article:14622887 | pubmed:year | 2003 | lld:pubmed |
| pubmed-article:14622887 | pubmed:articleTitle | Complex independent component analysis of frequency-domain electroencephalographic data. | lld:pubmed |
| pubmed-article:14622887 | pubmed:affiliation | Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, 9500 Gilman Dr, Dept 0961, La Jolla, CA 92093-0961, USA. jorn@salk.edu | lld:pubmed |
| pubmed-article:14622887 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:14622887 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
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