dc.contributor.author | Nakisa, B | |
dc.contributor.author | Rastgoo, MN | |
dc.contributor.author | Tjondronegoro, D | |
dc.contributor.author | Chandran, V | |
dc.date.accessioned | 2020-01-14T05:24:22Z | |
dc.date.available | 2020-01-14T05:24:22Z | |
dc.date.issued | 2018 | |
dc.identifier.issn | 0957-4174 | |
dc.identifier.doi | 10.1016/j.eswa.2017.09.062 | |
dc.identifier.uri | http://hdl.handle.net/10072/385644 | |
dc.description.abstract | There 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.peerreviewed | Yes | |
dc.publisher | Elsevier | |
dc.relation.ispartofpagefrom | 143 | |
dc.relation.ispartofpageto | 155 | |
dc.relation.ispartofjournal | Expert Systems with Applications | |
dc.relation.ispartofvolume | 93 | |
dc.subject.fieldofresearch | Mathematical sciences | |
dc.subject.fieldofresearch | Engineering | |
dc.subject.fieldofresearchcode | 49 | |
dc.subject.fieldofresearchcode | 40 | |
dc.title | Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dc.type.code | C - Journal Articles | |
dcterms.license | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.description.version | Accepted 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.hasfulltext | Full Text | |
gro.griffith.author | Tjondronegoro, Dian W. | |
gro.griffith.author | Chandran, Vinod | |