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dc.contributor.authorWang, Deqing
dc.contributor.authorZhu, Yongjie
dc.contributor.authorRistaniemi, Tapani
dc.contributor.authorCong, Fengyu
dc.date.accessioned2018-08-29T04:49:54Z
dc.date.available2020-04-01T21:35:18Z
dc.date.issued2018
dc.identifier.citationWang, D., Zhu, Y., Ristaniemi, T., & Cong, F. (2018). Extracting multi-mode ERP features using fifth-order nonnegative tensor decomposition. <i>Journal of Neuroscience Methods</i>, <i>308</i>, 240-247. <a href="https://doi.org/10.1016/j.jneumeth.2018.07.020" target="_blank">https://doi.org/10.1016/j.jneumeth.2018.07.020</a>
dc.identifier.otherCONVID_28203778
dc.identifier.otherTUTKAID_78485
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/59343
dc.description.abstractBackground Preprocessed Event-related potential (ERP) data are usually organized in multi-way tensor, in which tensor decomposition serves as a powerful tool for data processing. Due to the limitation of computation burden for multi-way data and the low algorithm performance of stability and efficiency, multi-way ERP data are conventionally reorganized into low-order tensor or matrix before further analysis. However, the reorganization may hamper mode specification and spoil the interaction information among different modes. New method In this study, we applied a fifth-order tensor decomposition to a set of fifth-order ERP data collected by exerting proprioceptive stimulus on left and right hand. One of the latest nonnegative CANDECOMP/PARAFAC (NCP) decomposition methods implemented by alternating proximal gradient (APG) was employed. We also proposed an improved DIFFIT method to select the optimal component number for the fifth-order tensor decomposition. Results By the fifth-order NCP model with a proper component number, the ERP data were fully decomposed into spatial, spectral, temporal, subject and condition factors in each component. The results showed more pairs of components with symmetric activation region in left and right hemisphere elicited by contralateral stimuli on hand. Comparison with existing method(s) In our experiment, more interesting components and coherent brain activities were extracted, compared with previous studies. Conclusions The discovered activities elicited by proprioceptive stimulus are in line with those in relevant cognitive neuroscience studies. Our proposed method has proved to be appropriate and viable for processing high-order EEG data with well-preserved interaction information among all modes.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier BV
dc.relation.ispartofseriesJournal of Neuroscience Methods
dc.rightsCC BY-NC-ND 4.0
dc.subject.othernonnegative tensor decomposition
dc.subject.otherCANDECOMP/PARAFAC
dc.subject.otherevent-related potential
dc.subject.othermulti-mode features
dc.subject.othercomponent number selection
dc.titleExtracting multi-mode ERP features using fifth-order nonnegative tensor decomposition
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201808203869
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2018-08-20T09:15:17Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange240-247
dc.relation.issn0165-0270
dc.relation.numberinseries0
dc.relation.volume308
dc.type.versionacceptedVersion
dc.rights.copyright© 2018 Elsevier B.V. All rights reserved.
dc.rights.accesslevelopenAccessfi
dc.subject.ysoEEG
dc.subject.ysosignaalinkäsittely
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p3328
jyx.subject.urihttp://www.yso.fi/onto/yso/p12266
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.jneumeth.2018.07.020
dc.type.okmA1


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