dc.contributor.author | Tsatsishvili, Valeri | |
dc.contributor.author | Cong, Fengyu | |
dc.contributor.author | Toiviainen, Petri | |
dc.contributor.author | Ristaniemi, Tapani | |
dc.date.accessioned | 2015-12-03T10:59:36Z | |
dc.date.available | 2015-12-03T10:59:36Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Tsatsishvili, 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.other | CONVID_25302619 | |
dc.identifier.other | TUTKAID_67828 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/47978 | |
dc.description.abstract | An 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.iso | eng | |
dc.publisher | Institute of Electrical and Electronic Engineers | |
dc.relation.ispartof | IJCNN 2015 : International Conference on Neural Networks | |
dc.relation.ispartofseries | Proceedings of International Joint Conference on Neural Networks | |
dc.subject.other | temporal cocatenation | |
dc.subject.other | naturalistic fMRI | |
dc.subject.other | dimension reduction | |
dc.subject.other | multiset CCA | |
dc.subject.other | PCA | |
dc.title | Combining PCA and multiset CCA for dimension reduction when group ICA is applied to decompose naturalistic fMRI data | |
dc.type | conferenceObject | |
dc.identifier.urn | URN:NBN:fi:jyu-201511203750 | |
dc.contributor.laitos | Musiikin laitos | fi |
dc.contributor.laitos | Tietotekniikan laitos | fi |
dc.contributor.laitos | Department of Music | en |
dc.contributor.laitos | Department of Mathematical Information Technology | en |
dc.contributor.oppiaine | Musiikkitiede | fi |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.contributor.oppiaine | Musicology | en |
dc.contributor.oppiaine | Mathematical Information Technology | en |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
dc.date.updated | 2015-11-20T13:15:09Z | |
dc.relation.isbn | 978-1-4799-1959-8 | |
dc.type.coar | http://purl.org/coar/resource_type/c_5794 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 1-6 | |
dc.relation.issn | 2161-4393 | |
dc.type.version | acceptedVersion | |
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.accesslevel | openAccess | fi |
dc.relation.conference | International joint conference on neural networks | |
dc.relation.doi | 10.1109/IJCNN.2015.7280722 | |
dc.type.okm | A4 | |