Feature extraction for fMRI-based human brain activity recognition

Bian, W; Li, J; Tao, D

Feature extraction for fMRI-based human brain activity recognition

Bian, W Li, J Tao, D

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6357 LNCS pp. 148 - 156
Issue Date:: 2010-10-25

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Field	Value	Language
dc.contributor.author	Bian, W	en_US
dc.contributor.author	Li, J	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2010-10-25	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6357 LNCS pp. 148 - 156	en_US
dc.identifier.isbn	3642159478	en_US
dc.identifier.isbn	9783642159473	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17286
dc.description.abstract	Mitchell et al. [9] demonstrated that support vector machines (SVM) are effective to classify the cognitive state of a human subject based on fRMI images observed over a single time interval. However, the direct use of classifiers on active voxels veils the understanding of brain activity recognition at the neurological level. In this paper, we present neurological insights to this problem by introducing the covariance selection (CS) to model the correlations between active voxels. In particular, we show that new features, i.e., different correlations between active voxels, are valuable to the recognition of brain activities, in a sense that the fMRI image sequences of different brain activities exhibit quite dissimilar patterns after projection onto these features. Based on the new feature, we employ classifiers, e.g., SVM and nearest neighbor classifier, for brain activity recognition. Significant improvements are achieved compared against the method used in [9]. © 2010 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-15948-0_19	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Feature extraction for fMRI-based human brain activity recognition	en_US
dc.type	Conference Proceeding
utslib.citation.volume	6357 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Beijing, China	en_US
dc.location.activity	Helsinki, Finland
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	6357 LNCS	en_US

Abstract:

Mitchell et al. [9] demonstrated that support vector machines (SVM) are effective to classify the cognitive state of a human subject based on fRMI images observed over a single time interval. However, the direct use of classifiers on active voxels veils the understanding of brain activity recognition at the neurological level. In this paper, we present neurological insights to this problem by introducing the covariance selection (CS) to model the correlations between active voxels. In particular, we show that new features, i.e., different correlations between active voxels, are valuable to the recognition of brain activities, in a sense that the fMRI image sequences of different brain activities exhibit quite dissimilar patterns after projection onto these features. Based on the new feature, we employ classifiers, e.g., SVM and nearest neighbor classifier, for brain activity recognition. Significant improvements are achieved compared against the method used in [9]. © 2010 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/17286