dc.contributor.author | Islam, Md Rabiul | |
dc.contributor.author | Islam, Md Milon | |
dc.contributor.author | Rahman, Md Mustafizur | |
dc.contributor.author | Mondal, Chayan | |
dc.contributor.author | Singha, Suvojit Kumar | |
dc.contributor.author | Ahmad, Mohiuddin | |
dc.contributor.author | Awal, Md Abdul | |
dc.contributor.author | Islam, Md Saiful | |
dc.contributor.author | Moni, Mohammad Ali | |
dc.date.accessioned | 2021-08-12T05:28:52Z | |
dc.date.available | 2021-08-12T05:28:52Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 0010-4825 | |
dc.identifier.doi | 10.1016/j.compbiomed.2021.104757 | |
dc.identifier.uri | http://hdl.handle.net/10072/406838 | |
dc.description.abstract | Emotion recognition using Artificial Intelligence (AI) is a fundamental prerequisite to improve Human-Computer Interaction (HCI). Recognizing emotion from Electroencephalogram (EEG) has been globally accepted in many applications such as intelligent thinking, decision-making, social communication, feeling detection, affective computing, etc. Nevertheless, due to having too low amplitude variation related to time on EEG signal, the proper recognition of emotion from this signal has become too challenging. Usually, considerable effort is required to identify the proper feature or feature set for an effective feature-based emotion recognition system. To extenuate the manual human effort of feature extraction, we proposed a deep machine-learning-based model with Convolutional Neural Network (CNN). At first, the one-dimensional EEG data were converted to Pearson's Correlation Coefficient (PCC) featured images of channel correlation of EEG sub-bands. Then the images were fed into the CNN model to recognize emotion. Two protocols were conducted, namely, protocol-1 to identify two levels and protocol-2 to recognize three levels of valence and arousal that demonstrate emotion. We investigated that only the upper triangular portion of the PCC featured images reduced the computational complexity and size of memory without hampering the model accuracy. The maximum accuracy of 78.22% on valence and 74.92% on arousal were obtained using the internationally authorized DEAP dataset. | |
dc.description.peerreviewed | Yes | |
dc.language | en | |
dc.publisher | Elsevier BV | |
dc.relation.ispartofjournal | Computers in Biology and Medicine | |
dc.subject.fieldofresearch | Biomedical engineering not elsewhere classified | |
dc.subject.fieldofresearch | Artificial intelligence not elsewhere classified | |
dc.subject.fieldofresearch | Deep learning | |
dc.subject.fieldofresearchcode | 400399 | |
dc.subject.fieldofresearchcode | 460299 | |
dc.subject.fieldofresearchcode | 461103 | |
dc.title | EEG Channel Correlation Based Model for Emotion Recognition | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dcterms.bibliographicCitation | Islam, MR; Islam, MM; Rahman, MM; Mondal, C; Singha, SK; Ahmad, M; Awal, MA; Islam, MS; Moni, MA, EEG Channel Correlation Based Model for Emotion Recognition, Computers in Biology and Medicine, 2021 | |
dcterms.license | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.date.updated | 2021-08-11T23:28:12Z | |
dc.description.version | Accepted Manuscript (AM) | |
gro.description.notepublic | This publication has been entered as an advanced online version in Griffith Research Online. | |
gro.rights.copyright | © 2021 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 | Islam, Saiful | |