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dc.contributor.authorHoefle, Sebastian
dc.contributor.authorEngel, Annerose
dc.contributor.authorBasilio, Rodrigo
dc.contributor.authorAlluri, Vinoo
dc.contributor.authorToiviainen, Petri
dc.contributor.authorCagy, Maurício
dc.contributor.authorMoll, Jorge
dc.date.accessioned2018-02-12T07:29:44Z
dc.date.available2018-02-12T07:29:44Z
dc.date.issued2018
dc.identifier.citationHoefle, S., Engel, A., Basilio, R., Alluri, V., Toiviainen, P., Cagy, M., & Moll, J. (2018). Identifying musical pieces from fMRI data using encoding and decoding models. <i>Scientific Reports</i>, <i>8</i>, Article 2266. <a href="https://doi.org/10.1038/s41598-018-20732-3" target="_blank">https://doi.org/10.1038/s41598-018-20732-3</a>
dc.identifier.otherCONVID_27895639
dc.identifier.otherTUTKAID_76778
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/57047
dc.description.abstractEncoding models can reveal and decode neural representations in the visual and semantic domains. However, a thorough understanding of how distributed information in auditory cortices and temporal evolution of music contribute to model performance is still lacking in the musical domain. We measured fMRI responses during naturalistic music listening and constructed a two-stage approach that first mapped musical features in auditory cortices and then decoded novel musical pieces. We then probed the influence of stimuli duration (number of time points) and spatial extent (number of voxels) on decoding accuracy. Our approach revealed a linear increase in accuracy with duration and a point of optimal model performance for the spatial extent. We further showed that Shannon entropy is a driving factor, boosting accuracy up to 95% for music with highest information content. These findings provide key insights for future decoding and reconstruction algorithms and open new venues for possible clinical applications.
dc.language.isoeng
dc.publisherNature Publishing Group
dc.relation.ispartofseriesScientific Reports
dc.subject.othercortex
dc.subject.otherneural decoding
dc.subject.otherneural encoding
dc.titleIdentifying musical pieces from fMRI data using encoding and decoding models
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201802081457
dc.contributor.laitosMusiikin, taiteen ja kulttuurin tutkimuksen laitosfi
dc.contributor.laitosDepartment of Music, Art and Culture Studiesen
dc.contributor.oppiaineMusiikkitiedefi
dc.contributor.oppiaineMusicologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2018-02-08T13:15:15Z
dc.type.coarjournal article
dc.description.reviewstatuspeerReviewed
dc.relation.issn2045-2322
dc.relation.numberinseries0
dc.relation.volume8
dc.type.versionpublishedVersion
dc.rights.copyright© the Authors, 2018. This is an open access article distributed under the terms of the Creative Commons License.
dc.rights.accesslevelopenAccessfi
dc.subject.ysoaivokuori
dc.subject.ysokoneoppiminen
dc.subject.ysomusiikki
dc.subject.ysokuunteleminen
dc.subject.ysoneurotieteet
jyx.subject.urihttp://www.yso.fi/onto/yso/p7039
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p1808
jyx.subject.urihttp://www.yso.fi/onto/yso/p9106
jyx.subject.urihttp://www.yso.fi/onto/yso/p18502
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1038/s41598-018-20732-3


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© the Authors, 2018. This is an open access article distributed under the terms of the Creative Commons License.
Except where otherwise noted, this item's license is described as © the Authors, 2018. This is an open access article distributed under the terms of the Creative Commons License.