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rdf:type |
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lifeskim:mentions |
umls-concept:C0013819,
umls-concept:C0085862,
umls-concept:C0449432,
umls-concept:C0936012,
umls-concept:C1179435,
umls-concept:C1257890,
umls-concept:C1299583,
umls-concept:C1511726,
umls-concept:C1524073,
umls-concept:C1548799,
umls-concept:C1549571,
umls-concept:C1608386,
umls-concept:C1705248
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pubmed:dateCreated |
2011-7-12
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pubmed:abstractText |
Independent component analysis (ICA) is a powerful method for source separation and has been used for decomposition of EEG, MRI, and concurrent EEG-fMRI data. ICA is not naturally suited to draw group inferences since it is a non-trivial problem to identify and order components across individuals. One solution to this problem is to create aggregate data containing observations from all subjects, estimate a single set of components and then back-reconstruct this in the individual data. Here, we describe such a group-level temporal ICA model for event related EEG. When used for EEG time series analysis, the accuracy of component detection and back-reconstruction with a group model is dependent on the degree of intra- and interindividual time and phase-locking of event related EEG processes. We illustrate this dependency in a group analysis of hybrid data consisting of three simulated event-related sources with varying degrees of latency jitter and variable topographies. Reconstruction accuracy was tested for temporal jitter 1, 2 and 3 times the FWHM of the sources for a number of algorithms. The results indicate that group ICA is adequate for decomposition of single trials with physiological jitter, and reconstructs event related sources with high accuracy.
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pubmed:grant |
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pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-10731765,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-10731767,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-10946390,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-11559959,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-11559961,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-12559247,
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http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-15193578,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-15219593,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-15601739,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-15663957,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-15734355,
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http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-15808990,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-15866182,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-16108017,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-16246587,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-16314575,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-16354931,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-16568940,
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http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-17070708,
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http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-17688963,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-17688965,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-18072279,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-19059344,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-19223007,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-20510374,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-3774929,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-7584893,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-8791593,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-9380745,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21747835-9741777
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:status |
MEDLINE
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pubmed:issn |
1687-5273
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pubmed:author |
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pubmed:issnType |
Electronic
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pubmed:volume |
2011
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
129365
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pubmed:meshHeading |
pubmed-meshheading:21747835-Algorithms,
pubmed-meshheading:21747835-Computer Simulation,
pubmed-meshheading:21747835-Data Interpretation, Statistical,
pubmed-meshheading:21747835-Electroencephalography,
pubmed-meshheading:21747835-Evoked Potentials,
pubmed-meshheading:21747835-Humans,
pubmed-meshheading:21747835-Image Processing, Computer-Assisted,
pubmed-meshheading:21747835-Models, Statistical,
pubmed-meshheading:21747835-Principal Component Analysis,
pubmed-meshheading:21747835-Reference Values
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pubmed:year |
2011
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pubmed:articleTitle |
EEGIFT: group independent component analysis for event-related EEG data.
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pubmed:affiliation |
Department of Biological and Medical Psychology, University of Bergen, Jonas Lies Vei 91, 5011 Bergen, Norway. tom.eichele@gmail.com
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pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't,
Research Support, N.I.H., Extramural
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