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dc.contributor.authorZhu, Yongjie
dc.contributor.authorLiu, Jia
dc.contributor.authorMathiak, Klaus
dc.contributor.authorRistaniemi, Tapani
dc.contributor.authorCong, Fengyu
dc.date.accessioned2020-02-19T10:39:31Z
dc.date.available2020-02-19T10:39:31Z
dc.date.issued2020
dc.identifier.citationZhu, Y., Liu, J., Mathiak, K., Ristaniemi, T., & Cong, F. (2020). Deriving electrophysiological brain network connectivity via tensor component analysis during freely listening to music. <i>IEEE Transactions on Neural Systems and Rehabilitation Engineering</i>, <i>28</i>(2), 409-418. <a href="https://doi.org/10.1109/tnsre.2019.2953971" target="_blank">https://doi.org/10.1109/tnsre.2019.2953971</a>
dc.identifier.otherCONVID_33917372
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/67885
dc.description.abstractRecent studies show that the dynamics of electrophysiological functional connectivity is attracting more and more interest since it is considered as a better representation of functional brain networks than static network analysis. It is believed that the dynamic electrophysiological brain networks with specific frequency modes, transiently form and dissolve to support ongoing cognitive function during continuous task performance. Here, we propose a novel method based on tensor component analysis (TCA), to characterize the spatial, temporal, and spectral signatures of dynamic electrophysiological brain networks in electroencephalography (EEG) data recorded during free music-listening. A three-way tensor containing time-frequency phase-coupling between pairs of parcellated brain regions is constructed. Nonnegative CANDECOMP/PARAFAC (CP) decomposition is then applied to extract three interconnected, low-dimensional descriptions of data including temporal, spectral, and spatial connection factors. Musical features are also extracted from stimuli using acoustic feature extraction. Correlation analysis is then conducted between temporal courses of musical features and TCA components to examine the modulation of brain patterns. We derive several brain networks with distinct spectral modes (described by TCA components) significantly modulated by musical features, including higher-order cognitive, sensorimotor, and auditory networks. The results demonstrate that brain networks during music listening in EEG are well characterized by TCA components, with spatial patterns of oscillatory phase-synchronization in specific spectral modes. The proposed method provides evidence for the time-frequency dynamics of brain networks during free music listening through TCA, which allows us to better understand the reorganization of electrophysiological networks.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.ispartofseriesIEEE Transactions on Neural Systems and Rehabilitation Engineering
dc.rightsCC BY 4.0
dc.subject.othertensor decomposition
dc.subject.otherfrequency-specific brain connectivity
dc.subject.otherfreely listening to music
dc.subject.otheroscillatory coherence
dc.subject.otherelectroencephalography (EEG)
dc.titleDeriving electrophysiological brain network connectivity via tensor component analysis during freely listening to music
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202002192118
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.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange409-418
dc.relation.issn1534-4320
dc.relation.numberinseries2
dc.relation.volume28
dc.type.versionpublishedVersion
dc.rights.copyright© Authors, 2020
dc.rights.accesslevelopenAccessfi
dc.subject.ysoEEG
dc.subject.ysokuunteleminen
dc.subject.ysosignaalinkäsittely
dc.subject.ysomusiikki
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p3328
jyx.subject.urihttp://www.yso.fi/onto/yso/p9106
jyx.subject.urihttp://www.yso.fi/onto/yso/p12266
jyx.subject.urihttp://www.yso.fi/onto/yso/p1808
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1109/tnsre.2019.2953971
jyx.fundinginformationThis work was supported by the National Natural Science Foundation of China (Grant No. 91748105&81471742), the Fundamental Research Funds for the Central Universities [DUT2019] in Dalian University of Technology in China, and the scholarship from China Scholarship Council (No. 201600090042; No. 201600090044). Y. Zhu was also supported by the Mobility Grant from University of Jyvaskyla.
dc.type.okmA1


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