Show simple item record

dc.contributor.authorNakisa, B
dc.contributor.authorRastgoo, MN
dc.contributor.authorTjondronegoro, D
dc.contributor.authorChandran, V
dc.date.accessioned2020-01-14T05:24:22Z
dc.date.available2020-01-14T05:24:22Z
dc.date.issued2018
dc.identifier.issn0957-4174
dc.identifier.doi10.1016/j.eswa.2017.09.062
dc.identifier.urihttp://hdl.handle.net/10072/385644
dc.description.abstractThere is currently no standard or widely accepted subset of features to effectively classify different emotions based on electroencephalogram (EEG) signals. While combining all possible EEG features may improve the classification performance, it can lead to high dimensionality and worse performance due to redundancy and inefficiency. To solve the high-dimensionality problem, this paper proposes a new framework to automatically search for the optimal subset of EEG features using evolutionary computation (EC) algorithms. The proposed framework has been extensively evaluated using two public datasets (MAHNOB, DEAP) and a new dataset acquired with a mobile EEG sensor. The results confirm that EC algorithms can effectively support feature selection to identify the best EEG features and the best channels to maximize performance over a four-quadrant emotion classification problem. These findings are significant for informing future development of EEG-based emotion classification because low-cost mobile EEG sensors with fewer electrodes are becoming popular for many new applications.
dc.description.peerreviewedYes
dc.publisherElsevier
dc.relation.ispartofpagefrom143
dc.relation.ispartofpageto155
dc.relation.ispartofjournalExpert Systems with Applications
dc.relation.ispartofvolume93
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode40
dc.titleEvolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2018 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorTjondronegoro, Dian W.
gro.griffith.authorChandran, Vinod


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record