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dc.contributor.authorTsatsishvili, Valeri
dc.contributor.authorCong, Fengyu
dc.contributor.authorToiviainen, Petri
dc.contributor.authorRistaniemi, Tapani
dc.date.accessioned2015-12-03T10:59:36Z
dc.date.available2015-12-03T10:59:36Z
dc.date.issued2015
dc.identifier.citationTsatsishvili, V., Cong, F., Toiviainen, P., & Ristaniemi, T. (2015). Combining PCA and multiset CCA for dimension reduction when group ICA is applied to decompose naturalistic fMRI data. In <i>IJCNN 2015 : International Conference on Neural Networks</i>. Institute of Electrical and Electronic Engineers. Proceedings of International Joint Conference on Neural Networks. <a href="https://doi.org/10.1109/IJCNN.2015.7280722" target="_blank">https://doi.org/10.1109/IJCNN.2015.7280722</a>
dc.identifier.otherCONVID_25302619
dc.identifier.otherTUTKAID_67828
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/47978
dc.description.abstractAn extension of group independent component analysis (GICA) is introduced, where multi-set canonical correlation analysis (MCCA) is combined with principal component analysis (PCA) for three-stage dimension reduction. The method is applied on naturalistic functional MRI (fMRI) images acquired during task-free continuous music listening experiment, and the results are compared with the outcome of the conventional GICA. The extended GICA resulted slightly faster ICA convergence and, more interestingly, extracted more stimulus-related components than its conventional counterpart. Therefore, we think the extension is beneficial enhancement for GICA, especially when applied to challenging fMRI data.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronic Engineers
dc.relation.ispartofIJCNN 2015 : International Conference on Neural Networks
dc.relation.ispartofseriesProceedings of International Joint Conference on Neural Networks
dc.subject.othertemporal cocatenation
dc.subject.othernaturalistic fMRI
dc.subject.otherdimension reduction
dc.subject.othermultiset CCA
dc.subject.otherPCA
dc.titleCombining PCA and multiset CCA for dimension reduction when group ICA is applied to decompose naturalistic fMRI data
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201511203750
dc.contributor.laitosMusiikin laitosfi
dc.contributor.laitosTietotekniikan laitosfi
dc.contributor.laitosDepartment of Musicen
dc.contributor.laitosDepartment of Mathematical Information Technologyen
dc.contributor.oppiaineMusiikkitiedefi
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMusicologyen
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2015-11-20T13:15:09Z
dc.relation.isbn978-1-4799-1959-8
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1-6
dc.relation.issn2161-4393
dc.type.versionacceptedVersion
dc.rights.copyright© 2015 IEEE. This is an authors' post-print version of an article whose final and definitive form has been published in the conference proceeding by IEEE.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational joint conference on neural networks
dc.relation.doi10.1109/IJCNN.2015.7280722
dc.type.okmA4


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