15866182

Source:http://linkedlifedata.com/resource/pubmed/id/15866182

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0085862, umls-concept:C0449432, umls-concept:C0936012, umls-concept:C1179435, umls-concept:C1299583, umls-concept:C1524073, umls-concept:C1548799, umls-concept:C1549571, umls-concept:C1608386, umls-concept:C1705248, umls-concept:C1875307
pubmed:issue	2
pubmed:dateCreated	2005-5-3
pubmed:abstractText	Independent component analysis (ICA) is a method for automatically identifying the underlying factors in a given data set. This rapidly evolving technique is currently finding applications in analysis of biomedical signals (e.g. ERP, EEG, fMRI, optical imaging), and in models of visual receptive fields and separation of speech signals. This article illustrates these applications, and provides an informal introduction to ICA.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/9708669
pubmed:status	PubMed-not-MEDLINE
pubmed:month	Feb
pubmed:issn	1364-6613
pubmed:author	pubmed-author:StoneJames VJV
pubmed:issnType	Print
pubmed:day	1
pubmed:volume	6
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	59-64
pubmed:year	2002
pubmed:articleTitle	Independent component analysis: an introduction.
pubmed:affiliation	Psychology Department, Sheffield University, Sheffield, UK. j.v.stone@sheffield.ac.uk
pubmed:publicationType	Journal Article