Linked Life Data

11951526

Source:http://linkedlifedata.com/resource/pubmed/id/11951526

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2002-4-15
pubmed:abstractText
Independent component analysis (ICA) is a new technique in statistical signal processing to extract independent components from multidimensional measurements of mixed signals. In this paper, for the processing of functional magnetic resonance imaging(fMRI) data, two signals of near voxels are used as the mixed signals and are separated by ICA. The correlation coefficients between the reference signal and the separated signals are calculated and those voxels whose correlation coefficients are greater than a threshold are considered to be the activated voxels by the stimulation, and so the functional localization of the stimulation is completed. The validity of the method was primarily proved by trial of real brain functional magnetic resonance imaging data.
pubmed:language
chi
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1001-5515
pubmed:author
pubmed:issnType
Print
pubmed:volume
19
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
64-6
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
[A method based on independent component analysis for processing fMRI data].
pubmed:affiliation
Dept of Applied Math, University of Electronic Science and Technology of China, Chengdu 610054.
pubmed:publicationType
Journal Article, English Abstract, Research Support, Non-U.S. Gov't