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dc.contributor.authorIslam, Md Rabiul
dc.contributor.authorIslam, Md Milon
dc.contributor.authorRahman, Md Mustafizur
dc.contributor.authorMondal, Chayan
dc.contributor.authorSingha, Suvojit Kumar
dc.contributor.authorAhmad, Mohiuddin
dc.contributor.authorAwal, Md Abdul
dc.contributor.authorIslam, Md Saiful
dc.contributor.authorMoni, Mohammad Ali
dc.date.accessioned2021-08-12T05:28:52Z
dc.date.available2021-08-12T05:28:52Z
dc.date.issued2021
dc.identifier.issn0010-4825
dc.identifier.doi10.1016/j.compbiomed.2021.104757
dc.identifier.urihttp://hdl.handle.net/10072/406838
dc.description.abstractEmotion 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.peerreviewedYes
dc.languageen
dc.publisherElsevier BV
dc.relation.ispartofjournalComputers in Biology and Medicine
dc.subject.fieldofresearchBiomedical engineering not elsewhere classified
dc.subject.fieldofresearchArtificial intelligence not elsewhere classified
dc.subject.fieldofresearchDeep learning
dc.subject.fieldofresearchcode400399
dc.subject.fieldofresearchcode460299
dc.subject.fieldofresearchcode461103
dc.titleEEG Channel Correlation Based Model for Emotion Recognition
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationIslam, 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.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2021-08-11T23:28:12Z
dc.description.versionAccepted Manuscript (AM)
gro.description.notepublicThis 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.hasfulltextFull Text
gro.griffith.authorIslam, Saiful


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