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dc.contributor.authorZhu, Yongjie
dc.contributor.authorZhang, Chi
dc.contributor.authorPoikonen, Hanna
dc.contributor.authorToiviainen, Petri
dc.contributor.authorHuotilainen, Minna
dc.contributor.authorMathiak, Klaus
dc.contributor.authorRistaniemi, Tapani
dc.contributor.authorCong, Fengyu
dc.date.accessioned2020-03-03T12:24:09Z
dc.date.available2020-03-03T12:24:09Z
dc.date.issued2020
dc.identifier.citationZhu, Y., Zhang, C., Poikonen, H., Toiviainen, P., Huotilainen, M., Mathiak, K., Ristaniemi, T., & Cong, F. (2020). Exploring Frequency-Dependent Brain Networks from Ongoing EEG Using Spatial ICA During Music Listening. <i>Brain Topography</i>, <i>33</i>(3), 289-302. <a href="https://doi.org/10.1007/s10548-020-00758-5" target="_blank">https://doi.org/10.1007/s10548-020-00758-5</a>
dc.identifier.otherCONVID_34840033
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/68037
dc.description.abstractRecently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during freely listening to music. We used a data-driven method that combined music information retrieval with spatial Fourier Independent Components Analysis (spatial Fourier–ICA) to probe the interplay between the spatial profiles and the spectral patterns of the brain network emerging from music listening. Correlation analysis was performed between time courses of brain networks extracted from EEG data and musical feature time series extracted from music stimuli to derive the musical feature related oscillatory patterns in the listening brain. We found brain networks of musical feature processing were frequency-dependent. Musical feature time series, especially fluctuation centroid and key feature, were associated with an increased beta activation in the bilateral superior temporal gyrus. An increased alpha oscillation in the bilateral occipital cortex emerged during music listening, which was consistent with alpha functional suppression hypothesis in task-irrelevant regions. We also observed an increased delta–beta oscillatory activity in the prefrontal cortex associated with musical feature processing. In addition to these findings, the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofseriesBrain Topography
dc.rightsCC BY 4.0
dc.subject.otherfrequency-specific networks
dc.subject.othermusic information retrieval
dc.subject.otherEEG
dc.subject.otherindependent components analysis
dc.titleExploring Frequency-Dependent Brain Networks from Ongoing EEG Using Spatial ICA During Music Listening
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202003032259
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.oppiaineMusiikkitiedefi
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMonitieteinen aivotutkimuskeskusfi
dc.contributor.oppiaineHyvinvoinnin tutkimuksen yhteisöfi
dc.contributor.oppiaineMusicologyen
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.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange289-302
dc.relation.issn0896-0267
dc.relation.numberinseries3
dc.relation.volume33
dc.type.versionpublishedVersion
dc.rights.copyright© The Authors 2020
dc.rights.accesslevelopenAccessfi
dc.subject.ysoEEG
dc.subject.ysotaajuus
dc.subject.ysokuunteleminen
dc.subject.ysoaivotutkimus
dc.subject.ysoaivot
dc.subject.ysoaivokuori
dc.subject.ysomusiikki
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p3328
jyx.subject.urihttp://www.yso.fi/onto/yso/p704
jyx.subject.urihttp://www.yso.fi/onto/yso/p9106
jyx.subject.urihttp://www.yso.fi/onto/yso/p23705
jyx.subject.urihttp://www.yso.fi/onto/yso/p7040
jyx.subject.urihttp://www.yso.fi/onto/yso/p7039
jyx.subject.urihttp://www.yso.fi/onto/yso/p1808
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1007/s10548-020-00758-5
jyx.fundinginformationOpen access funding provided by University of Jyväskylä (JYU). This work was supported by the National Natural Science Foundation of China (Grant No. 91748105), 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).
dc.type.okmA1


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