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dc.contributor.authorCong, Fengyu
dc.contributor.authorZhou, Guoxu
dc.contributor.authorAstikainen, Piia
dc.contributor.authorZhao, Qibin
dc.contributor.authorWu, Qiang
dc.contributor.authorNandi, Asoke
dc.contributor.authorHietanen, Jari K.
dc.contributor.authorRistaniemi, Tapani
dc.contributor.authorCichocki, Andrzej
dc.date.accessioned2021-09-20T08:21:34Z
dc.date.available2021-09-20T08:21:34Z
dc.date.issued2014
dc.identifier.citationCong, F., Zhou, G., Astikainen, P., Zhao, Q., Wu, Q., Nandi, A., Hietanen, J. K., Ristaniemi, T., & Cichocki, A. (2014). Low-rank approximation based non-negative multi-way array decomposition on event-related potentials. <i>International Journal of Neural Systems</i>, <i>24</i>(8), Article 1440005. <a href="https://doi.org/10.1142/S012906571440005X" target="_blank">https://doi.org/10.1142/S012906571440005X</a>
dc.identifier.otherCONVID_23749884
dc.identifier.otherTUTKAID_62294
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/77841
dc.description.abstractNon-negative tensor factorization (NTF) has been successfully applied to analyze event-related potentials (ERPs), and shown superiority in terms of capturing multi-domain features. However, the time-frequency representation of ERPs by higher-order tensors are usually large-scale, which prevents the popularity of most tensor factorization algorithms. To overcome this issue, we introduce a non-negative canonical polyadic decomposition (NCPD) based on low-rank approximation (LRA) and hierarchical alternating least square (HALS) techniques. We applied NCPD (LRAHALS and benchmark HALS) and CPD to extract multi-domain features of a visual ERP. The features and components extracted by LRAHALS NCPD and HALS NCPD were very similar, but LRAHALS NCPD was 70 times faster than HALS NCPD. Moreover, the desired multi-domain feature of the ERP by NCPD showed a significant group difference (control versus depressed participants) and a difference in emotion processing (fearful versus happy faces). This was more satisfactory than that by CPD, which revealed only a group difference.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherWorld Scientific
dc.relation.ispartofseriesInternational Journal of Neural Systems
dc.rightsCC BY 4.0
dc.subject.otherEvent-related potential
dc.subject.otherlow-rank approximation
dc.subject.othermulti-domain feature
dc.subject.othernon-negative canonical polyadic decomposition
dc.subject.othernon-negative tensor factorization
dc.subject.othertensor decomposition
dc.titleLow-rank approximation based non-negative multi-way array decomposition on event-related potentials
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202109204914
dc.contributor.laitosPsykologian laitosfi
dc.contributor.laitosTietotekniikan laitosfi
dc.contributor.laitosDepartment of Psychologyen
dc.contributor.laitosDepartment of Mathematical Information Technologyen
dc.contributor.oppiainePsykologiafi
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiainePsychologyen
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn0129-0657
dc.relation.numberinseries8
dc.relation.volume24
dc.type.versionpublishedVersion
dc.rights.copyright© 2014 the Authors
dc.rights.accesslevelopenAccessfi
dc.format.contentfulltext
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1142/S012906571440005X
dc.type.okmA1


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