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dc.contributor.authorDaly, Ien
dc.contributor.authorHwang, Fen
dc.contributor.authorKirke, Aen
dc.contributor.authorMalik, Aen
dc.contributor.authorWeaver, Jen
dc.contributor.authorWilliams, Den
dc.contributor.authorMiranda, Een
dc.contributor.authorNasuto, SJen
dc.date.accessioned2016-10-13T16:18:29Z
dc.date.available2016-10-13T16:18:29Z
dc.date.issued2015-03-15en
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.

en
dc.format.extent65 - 71en
dc.languageengen
dc.language.isoengen
dc.subjectEigen-decompositionen
dc.subjectElectroencephalogram (EEG)en
dc.subjectFeature selectionen
dc.subjectNeural correlatesen
dc.subjectAcoustic Stimulationen
dc.subjectAuditory Perceptionen
dc.subjectBrainen
dc.subjectBrain Mappingen
dc.subjectComputer Simulationen
dc.subjectElectroencephalographyen
dc.subjectEmotionsen
dc.subjectEvoked Potentialsen
dc.subjectHumansen
dc.subjectModels, Neurologicalen
dc.subjectMusicen
dc.subjectPattern Recognition, Automateden
dc.titleAutomated identification of neural correlates of continuous variables.en
dc.typeJournal Article
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/25546485en
plymouth.volume242en
plymouth.publication-statusPublisheden
plymouth.journalJ Neurosci Methodsen
dc.identifier.doi10.1016/j.jneumeth.2014.12.012en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/00 Groups by role
plymouth.organisational-group/Plymouth/00 Groups by role/Academics
plymouth.organisational-group/Plymouth/Faculty of Arts & Humanities
plymouth.organisational-group/Plymouth/Faculty of Arts & Humanities/School of Humanities and Performing Arts
dc.publisher.placeNetherlandsen
dcterms.dateAccepted2014-12-17en
dc.identifier.eissn1872-678Xen
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.1016/j.jneumeth.2014.12.012en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2015-03-15en
rioxxterms.typeJournal Article/Reviewen
plymouth.funderBrain-Computer Interface for Monitoring and Inducing Affective States::EPSRCen


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