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dc.contributor.authorWang, Xiulin
dc.contributor.authorLiu, Wenya
dc.contributor.authorWang, Xiaoyu
dc.contributor.authorMu, Zhen
dc.contributor.authorXu, Jing
dc.contributor.authorChang, Yi
dc.contributor.authorZhang, Qing
dc.contributor.authorWu, Jianlin
dc.contributor.authorCong, Fengyu
dc.date.accessioned2022-01-26T06:11:40Z
dc.date.available2022-01-26T06:11:40Z
dc.date.issued2021
dc.identifier.citationWang, X., Liu, W., Wang, X., Mu, Z., Xu, J., Chang, Y., Zhang, Q., Wu, J., & Cong, F. (2021). Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization. <i>Frontiers in Human Neuroscience</i>, <i>15</i>, Article 799288. <a href="https://doi.org/10.3389/fnhum.2021.799288" target="_blank">https://doi.org/10.3389/fnhum.2021.799288</a>
dc.identifier.otherCONVID_103997507
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/79508
dc.description.abstractOngoing electroencephalography (EEG) signals are recorded as a mixture of stimulus-elicited EEG, spontaneous EEG and noises, which poses a huge challenge to current data analyzing techniques, especially when different groups of participants are expected to have common or highly correlated brain activities and some individual dynamics. In this study, we proposed a data-driven shared and unshared feature extraction framework based on nonnegative and coupled tensor factorization, which aims to conduct group-level analysis for the EEG signals from major depression disorder (MDD) patients and healthy controls (HC) when freely listening to music. Constrained tensor factorization not only preserves the multilinear structure of the data, but also considers the common and individual components between the data. The proposed framework, combined with music information retrieval, correlation analysis, and hierarchical clustering, facilitated the simultaneous extraction of shared and unshared spatio-temporal-spectral feature patterns between/in MDD and HC groups. Finally, we obtained two shared feature patterns between MDD and HC groups, and obtained totally three individual feature patterns from HC and MDD groups. The results showed that the MDD and HC groups triggered similar brain dynamics when listening to music, but at the same time, MDD patients also brought some changes in brain oscillatory network characteristics along with music perception. These changes may provide some basis for the clinical diagnosis and the treatment of MDD patients.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherFrontiers Media SA
dc.relation.ispartofseriesFrontiers in Human Neuroscience
dc.rightsCC BY 4.0
dc.subject.otherCANDECOMP/PARAFAC
dc.subject.otherconstrained tensor factorization
dc.subject.otherEEG
dc.subject.othermajor depressive disorder
dc.subject.othernaturalistic music stimuli
dc.titleShared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202201261279
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineSecure Communications Engineering and Signal Processingfi
dc.contributor.oppiaineOhjelmisto- ja tietoliikennetekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineSecure Communications Engineering and Signal Processingen
dc.contributor.oppiaineSoftware and Communications Engineeringen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.relation.issn1662-5161
dc.relation.volume15
dc.type.versionpublishedVersion
dc.rights.copyright© 2021 Wang, Liu, Wang, Mu, Xu, Chang, Zhang, Wu and Cong
dc.rights.accesslevelopenAccessfi
dc.subject.ysoärsykkeet
dc.subject.ysomasennus
dc.subject.ysomusiikki
dc.subject.ysosignaalianalyysi
dc.subject.ysoEEG
dc.subject.ysoaivotutkimus
dc.subject.ysosignaalinkäsittely
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p2943
jyx.subject.urihttp://www.yso.fi/onto/yso/p7995
jyx.subject.urihttp://www.yso.fi/onto/yso/p1808
jyx.subject.urihttp://www.yso.fi/onto/yso/p26805
jyx.subject.urihttp://www.yso.fi/onto/yso/p3328
jyx.subject.urihttp://www.yso.fi/onto/yso/p23705
jyx.subject.urihttp://www.yso.fi/onto/yso/p12266
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3389/fnhum.2021.799288
jyx.fundinginformationThis work is supported by National Natural Science Foundation of China (grant no. 91748105), National Foundation in China (no. JCKY2019110B009 and 2020-JCJQ-JJ-252), the Fundamental Research Funds for the Central Universities (DUT2019 and DUT20LAB303) in Dalian University of Technology in China, Dalian Science and Technology Innovation Fund Project (2021JJ12SN38), and the scholarship from China scholarship Council (no. 201706060263).


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