Automated identification of neural correlates of continuous variables
dc.contributor.author | Daly, I | |
dc.contributor.author | Hwang, F | |
dc.contributor.author | Kirke, Alexis | |
dc.contributor.author | Malik, A | |
dc.contributor.author | Weaver, J | |
dc.contributor.author | Williams, D | |
dc.contributor.author | Miranda, Eduardo | |
dc.contributor.author | Nasuto, SJ | |
dc.date.accessioned | 2016-10-13T16:18:29Z | |
dc.date.available | 2016-10-13T16:18:29Z | |
dc.date.issued | 2015-03 | |
dc.identifier.issn | 0165-0270 | |
dc.identifier.issn | 1872-678X | |
dc.identifier.uri | http://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.extent | 65-71 | |
dc.format.medium | Print-Electronic | |
dc.language | en | |
dc.language.iso | eng | |
dc.publisher | Elsevier BV | |
dc.subject | Feature selection | |
dc.subject | Eigen-decomposition | |
dc.subject | Neural correlates | |
dc.subject | Electroencephalogram (EEG) | |
dc.title | Automated identification of neural correlates of continuous variables | |
dc.type | journal-article | |
dc.type | Evaluation Study | |
dc.type | Journal Article | |
dc.type | Research Support, Non-U.S. Gov't | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000350921900006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.volume | 242 | |
plymouth.publication-status | Published | |
plymouth.journal | Journal of Neuroscience Methods | |
dc.identifier.doi | 10.1016/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.place | Netherlands | |
dcterms.dateAccepted | 2014-12-17 | |
dc.identifier.eissn | 1872-678X | |
dc.rights.embargoperiod | Not known | |
rioxxterms.funder | Engineering and Physical Sciences Research Council | |
rioxxterms.identifier.project | Brain-Computer Interface for Monitoring and Inducing Affective States | |
rioxxterms.versionofrecord | 10.1016/j.jneumeth.2014.12.012 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2015-03-15 | |
rioxxterms.type | Journal Article/Review | |
plymouth.funder | Brain-Computer Interface for Monitoring and Inducing Affective States::Engineering and Physical Sciences Research Council |