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dc.contributor.authorDaly, I
dc.contributor.authorHwang, F
dc.contributor.authorKirke, Alexis
dc.contributor.authorMalik, A
dc.contributor.authorWeaver, J
dc.contributor.authorWilliams, D
dc.contributor.authorMiranda, Eduardo
dc.contributor.authorNasuto, SJ
dc.date.accessioned2016-10-13T16:18:29Z
dc.date.available2016-10-13T16:18:29Z
dc.date.issued2015-03
dc.identifier.issn0165-0270
dc.identifier.issn1872-678X
dc.identifier.urihttp://hdl.handle.net/10026.1/6523
dc.description.abstract

BACKGROUND: The electroencephalogram (EEG) may be described by a large number of different feature types and automated feature selection methods are needed in order to reliably identify features which correlate with continuous independent variables. NEW METHOD: A method is presented for the automated identification of features that differentiate two or more groups in neurological datasets based upon a spectral decomposition of the feature set. Furthermore, the method is able to identify features that relate to continuous independent variables. RESULTS: The proposed method is first evaluated on synthetic EEG datasets and observed to reliably identify the correct features. The method is then applied to EEG recorded during a music listening task and is observed to automatically identify neural correlates of music tempo changes similar to neural correlates identified in a previous study. Finally, the method is applied to identify neural correlates of music-induced affective states. The identified neural correlates reside primarily over the frontal cortex and are consistent with widely reported neural correlates of emotions. COMPARISON WITH EXISTING METHODS: The proposed method is compared to the state-of-the-art methods of canonical correlation analysis and common spatial patterns, in order to identify features differentiating synthetic event-related potentials of different amplitudes and is observed to exhibit greater performance as the number of unique groups in the dataset increases. CONCLUSIONS: The proposed method is able to identify neural correlates of continuous variables in EEG datasets and is shown to outperform canonical correlation analysis and common spatial patterns.

dc.format.extent65-71
dc.format.mediumPrint-Electronic
dc.languageen
dc.language.isoeng
dc.publisherElsevier BV
dc.subjectFeature selection
dc.subjectEigen-decomposition
dc.subjectNeural correlates
dc.subjectElectroencephalogram (EEG)
dc.titleAutomated identification of neural correlates of continuous variables
dc.typejournal-article
dc.typeEvaluation Study
dc.typeJournal Article
dc.typeResearch Support, Non-U.S. Gov't
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000350921900006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.volume242
plymouth.publication-statusPublished
plymouth.journalJournal of Neuroscience Methods
dc.identifier.doi10.1016/j.jneumeth.2014.12.012
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Arts, Humanities and Business
plymouth.organisational-group/Plymouth/Faculty of Arts, Humanities and Business/School of Society and Culture
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA33 Music, Drama, Dance, Performing Arts, Film and Screen Studies
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.publisher.placeNetherlands
dcterms.dateAccepted2014-12-17
dc.identifier.eissn1872-678X
dc.rights.embargoperiodNot known
rioxxterms.funderEngineering and Physical Sciences Research Council
rioxxterms.identifier.projectBrain-Computer Interface for Monitoring and Inducing Affective States
rioxxterms.versionofrecord10.1016/j.jneumeth.2014.12.012
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2015-03-15
rioxxterms.typeJournal Article/Review
plymouth.funderBrain-Computer Interface for Monitoring and Inducing Affective States::Engineering and Physical Sciences Research Council


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