Linked Life Data

14622887

Source:http://linkedlifedata.com/resource/pubmed/id/14622887

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
9
pubmed:dateCreated
2003-11-19
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.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
0893-6080
pubmed:author
pubmed:issnType
Print
pubmed:volume
16
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1311-23
pubmed:dateRevised
2010-9-20
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Complex independent component analysis of frequency-domain electroencephalographic data.
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
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't