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dc.contributor.authorWang, Xiulin
dc.contributor.authorLiu, Wenya
dc.contributor.authorToiviainen, Petri
dc.contributor.authorRistaniemi, Tapani
dc.contributor.authorCong, Fengyu
dc.date.accessioned2019-12-30T07:09:20Z
dc.date.available2019-12-30T07:09:20Z
dc.date.issued2020
dc.identifier.citationWang, X., Liu, W., Toiviainen, P., Ristaniemi, T., & Cong, F. (2020). Group analysis of ongoing EEG data based on fast double-coupled nonnegative tensor decomposition. <i>Journal of Neuroscience Methods</i>, <i>330</i>, Article 108502. <a href="https://doi.org/10.1016/j.jneumeth.2019.108502" target="_blank">https://doi.org/10.1016/j.jneumeth.2019.108502</a>
dc.identifier.otherCONVID_33595951
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/67020
dc.description.abstractBackground Ongoing EEG data are recorded as mixtures of stimulus-elicited EEG, spontaneous EEG and noises, which require advanced signal processing techniques for separation and analysis. Existing methods cannot simultaneously consider common and individual characteristics among/within subjects when extracting stimulus-elicited brain activities from ongoing EEG elicited by 512-s long modern tango music. New method Aiming to discover the commonly music-elicited brain activities among subjects, we provide a comprehensive framework based on fast double-coupled nonnegative tensor decomposition (FDC-NTD) algorithm. The proposed algorithm with a generalized model is capable of simultaneously decomposing EEG tensors into common and individual components. Results With the proposed framework, the brain activities can be effectively extracted and sorted into the clusters of interest. The proposed algorithm based on the generalized model achieved higher fittings and stronger robustness. In addition to the distribution of centro-parietal and occipito-parietal regions with theta and alpha oscillations, the music-elicited brain activities were also located in the frontal region and distributed in the 4–11 Hz band. Comparison with existing method(s) The present study, by providing a solution of how to separate common stimulus-elicited brain activities using coupled tensor decomposition, has shed new light on the processing and analysis of ongoing EEG data in multi-subject level. It can also reveal more links between brain responses and the continuous musical stimulus. Conclusions The proposed framework based on coupled tensor decomposition can be successfully applied to group analysis of ongoing EEG data, as it can be reliably inferred that those brain activities we obtained are associated with musical stimulus.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherElsevier BV
dc.relation.ispartofseriesJournal of Neuroscience Methods
dc.rightsCC BY-NC-ND 4.0
dc.subject.othercoupled
dc.subject.othermusic
dc.subject.othernonnegative
dc.subject.othertensor decomposition
dc.subject.otherongoing EEG
dc.titleGroup analysis of ongoing EEG data based on fast double-coupled nonnegative tensor decomposition
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201912305496
dc.contributor.laitosMusiikin, taiteen ja kulttuurin tutkimuksen laitosfi
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosDepartment of Music, Art and Culture Studiesen
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMusiikkitiedefi
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineMusicologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.relation.issn0165-0270
dc.relation.volume330
dc.type.versionpublishedVersion
dc.rights.copyright© 2019 The Author(s).
dc.rights.accesslevelopenAccessfi
dc.subject.ysoEEG
dc.subject.ysosignaalianalyysi
dc.subject.ysoärsykkeet
dc.subject.ysomusiikki
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p3328
jyx.subject.urihttp://www.yso.fi/onto/yso/p26805
jyx.subject.urihttp://www.yso.fi/onto/yso/p2943
jyx.subject.urihttp://www.yso.fi/onto/yso/p1808
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.jneumeth.2019.108502
jyx.fundinginformationThis work was supported by the National Natural Science Foundation of China (Grant Nos. 91748105 and 81471742), the Fundamental Research Funds for the Central Universities [DUT2019] in Dalian University of Technology in China and the scholarships from China Scholarship Council (Nos. 201706060262 and 201706060263).


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