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A K-means multivariate approach for clustering independent components from magnetoencephalographic data.

Abstract:

Independent component analysis (ICA) is typically applied on functional magnetic resonance imaging, electroencephalographic and magnetoencephalographic (MEG) data due to its data-driven nature. In these applications, ICA needs to be extended from single to multi-session and multi-subject studies for interpreting and assigning a statistical significance at the group level. Here a novel strategy for analyzing MEG independent components (ICs) is presented, Multivariate Algorithm for Grouping MEG...

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Authors


Spadone, S More by this author
de Pasquale, F More by this author
More by this author
Institution:
University of Oxford
Department:
Oxford, MSD, Experimental Psychology
Della Penna, S More by this author
Journal:
NeuroImage
Volume:
62
Issue:
3
Pages:
1912-1923
Publication date:
2012-09-05
DOI:
EISSN:
1095-9572
ISSN:
1053-8119
URN:
uuid:30f3b0fb-2834-4762-84e7-a103c88a037c
Source identifiers:
364102
Local pid:
pubs:364102

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