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dc.contributor.authorWang, Deqing
dc.contributor.authorWang, Xiaoyu
dc.contributor.authorZhu, Yongjie
dc.contributor.authorToiviainen, Petri
dc.contributor.authorHuotilainen, Minna
dc.contributor.authorRistaniemi, Tapani
dc.contributor.authorCong, Fengyu
dc.contributor.editorHuang, Tingwen
dc.contributor.editorLv, Jiancheng
dc.contributor.editorSun, Changyin
dc.contributor.editorTuzikov, Alexander V.
dc.date.accessioned2018-12-19T11:29:27Z
dc.date.available2019-05-27T21:35:41Z
dc.date.issued2018
dc.identifier.citationWang, D., Wang, X., Zhu, Y., Toiviainen, P., Huotilainen, M., Ristaniemi, T., & Cong, F. (2018). Increasing Stability of EEG Components Extraction Using Sparsity Regularized Tensor Decomposition. In T. Huang, J. Lv, C. Sun, & A. V. Tuzikov (Eds.), <i>ISNN 2018 : Advances in Neural Networks : 15th International Symposium on Neural Networks, Proceedings</i> (pp. 789-799). Springer International Publishing. Lecture Notes in Computer Science, 10878. <a href="https://doi.org/10.1007/978-3-319-92537-0_89" target="_blank">https://doi.org/10.1007/978-3-319-92537-0_89</a>
dc.identifier.otherCONVID_28070407
dc.identifier.otherTUTKAID_77731
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/60690
dc.description.abstractTensor decomposition has been widely employed for EEG signal processing in recent years. Constrained and regularized tensor decomposition often attains more meaningful and interpretable results. In this study, we applied sparse nonnegative CANDECOMP/PARAFAC tensor decomposition to ongoing EEG data under naturalistic music stimulus. Interesting temporal, spectral and spatial components highly related with music features were extracted. We explored the ongoing EEG decomposition results and properties in a wide range of sparsity levels, and proposed a paradigm to select reasonable sparsity regularization parameters. The stability of interesting components extraction from fourteen subjects’ data was deeply analyzed. Our results demonstrate that appropriate sparsity regularization can increase the stability of interesting components significantly and remove weak components at the same time.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer International Publishing
dc.relation.ispartofISNN 2018 : Advances in Neural Networks : 15th International Symposium on Neural Networks, Proceedings
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rightsIn Copyright
dc.subject.othertensor decomposition
dc.subject.othersparse regularization
dc.subject.othernonnegative constraints
dc.subject.otherongoing EEG
dc.subject.otherstability analysis
dc.titleIncreasing Stability of EEG Components Extraction Using Sparsity Regularized Tensor Decomposition
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201812195221
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosMusiikin, taiteen ja kulttuurin tutkimuksen laitosfi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.laitosDepartment of Music, Art and Culture Studiesen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMusiikkitiedefi
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineMusicologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2018-12-19T10:15:19Z
dc.relation.isbn978-3-319-92536-3
dc.description.reviewstatuspeerReviewed
dc.format.pagerange789-799
dc.relation.issn0302-9743
dc.relation.numberinseries10878
dc.type.versionacceptedVersion
dc.rights.copyright© Springer International Publishing AG, part of Springer Nature 2018.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Symposium on Neural Networks
dc.format.contentfulltext
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/978-3-319-92537-0_89


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