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dc.contributor.authorCong, Fengyu
dc.contributor.authorLin, Qiu-Hua
dc.contributor.authorKuang, Li-Dan
dc.contributor.authorGong, Xiao-Feng
dc.contributor.authorAstikainen, Piia
dc.contributor.authorRistaniemi, Tapani
dc.date.accessioned2015-05-07T05:50:52Z
dc.date.available2015-05-07T05:50:52Z
dc.date.issued2015
dc.identifier.citationCong, F., Lin, Q.-H., Kuang, L.-D., Gong, X.-F., Astikainen, P., & Ristaniemi, T. (2015). Tensor decomposition of EEG signals: A brief review. <i>Journal of Neuroscience Methods</i>, <i>248</i>(June), 59-69. <a href="https://doi.org/10.1016/j.jneumeth.2015.03.018" target="_blank">https://doi.org/10.1016/j.jneumeth.2015.03.018</a>
dc.identifier.otherCONVID_24675365
dc.identifier.otherTUTKAID_65965
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/45800
dc.description.abstractElectroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG signals tend to be represented by a vector or a matrix to facilitate data processing and analysis with generally understood methodologies like time-series analysis, spectral analysis and matrix decomposition. Indeed, EEG signals are often naturally born with more than two modes of time and space, and they can be denoted by a multi-way array called as tensor. This review summarizes the current progress of tensor decomposition of EEG signals with three aspects. The first is about the existing modes and tensors of EEG signals. Second, two fundamental tensor decomposition models, canonical polyadic decomposition (CPD, it is also called parallel factor analysis-PARAFAC) and Tucker decomposition, are introduced and compared. Moreover, the applications of the two models for EEG signals are addressed. Particularly, the determination of the number of components for each mode is discussed. Finally, the N-way partial least square and higherorder partial least square are described for a potential trend to process and analyze brain signals of two modalities simultaneously
dc.language.isoeng
dc.publisherElsevier BV
dc.relation.ispartofseriesJournal of Neuroscience Methods
dc.subject.otherevent-related potentials
dc.subject.othertensor decomposition
dc.subject.othercanonical polyadic
dc.subject.othertucker
dc.subject.othersignal
dc.titleTensor decomposition of EEG signals: A brief review
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201505051727
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.oppiaineMonitieteinen aivotutkimuskeskusfi
dc.contributor.oppiaineHyvinvoinnin tutkimuksen yhteisöfi
dc.contributor.oppiainePsychologyen
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineCentre for Interdisciplinary Brain Researchen
dc.contributor.oppiaineSchool of Wellbeingen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2015-05-05T15:15:03Z
dc.type.coarhttp://purl.org/coar/resource_type/c_dcae04bc
dc.description.reviewstatuspeerReviewed
dc.format.pagerange59-69
dc.relation.issn0165-0270
dc.relation.numberinseriesJune
dc.relation.volume248
dc.type.versionpublishedVersion
dc.rights.copyright© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND.
dc.rights.accesslevelopenAccessfi
dc.subject.ysoEEG
dc.subject.ysoaivot
jyx.subject.urihttp://www.yso.fi/onto/yso/p3328
jyx.subject.urihttp://www.yso.fi/onto/yso/p7040
dc.rights.urlhttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.jneumeth.2015.03.018
dc.type.okmA2


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© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND.
Ellei muuten mainita, aineiston lisenssi on © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND.