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dc.contributor.authorMiah, Md Ochiuddin
dc.contributor.authorMuhammod, Rafsanjani
dc.contributor.authorMamun, Khondaker Abdullah Al
dc.contributor.authorFarid, Dewan Md
dc.contributor.authorKumar, Shiu
dc.contributor.authorSharma, Alok
dc.contributor.authorDehzangi, Abdollah
dc.date.accessioned2021-10-13T03:56:17Z
dc.date.available2021-10-13T03:56:17Z
dc.date.issued2021
dc.identifier.issn0165-0270
dc.identifier.doi10.1016/j.jneumeth.2021.109373
dc.identifier.urihttp://hdl.handle.net/10072/408997
dc.description.abstractBACKGROUND: The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal task in the biosignal classification process in the brain-computer interface (BCI) applications. Currently, this bio-engineering-based technology is being employed by researchers in various fields to develop cutting-edge applications. The classification of real-time MI-EEG signals is the most challenging task in these applications. The prediction performance of the existing classification methods is still limited due to the high dimensionality and dynamic behaviors of the real-time EEG data. PROPOSED METHOD: To enhance the classification performance of real-time BCI applications, this paper presents a new clustering-based ensemble technique called CluSem to mitigate this problem. We also develop a new brain game called CluGame using this method to evaluate the classification performance of real-time motor imagery movements. In this game, real-time EEG signal classification and prediction tabulation through animated balls are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. RESULTS: Our results demonstrate that CluSem is able to improve the classification accuracy between 5% and 15% compared to the existing methods on our collected as well as the publicly available EEG datasets. The source codes used to implement CluSem and CluGame are publicly available athttps://github.com/MdOchiuddinMiah/MI-BCI_ML.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherElsevier BV
dc.relation.ispartofpagefrom109373
dc.relation.ispartofjournalJournal of Neuroscience Methods
dc.subject.fieldofresearchNeurosciences
dc.subject.fieldofresearchInformation systems
dc.subject.fieldofresearchcode3209
dc.subject.fieldofresearchcode4609
dc.subject.keywordsBrain Computer Interface (BCI)
dc.subject.keywordsBrain Engineering
dc.subject.keywordsClustering
dc.subject.keywordsEnsemble Learning
dc.subject.keywordsHuman Machine Interface (HMI)
dc.titleCluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationMiah, MO; Muhammod, R; Mamun, KAA; Farid, DM; Kumar, S; Sharma, A; Dehzangi, A, CluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data, Journal of Neuroscience Methods, 2021, pp. 109373
dcterms.dateAccepted2021-09-27
dcterms.licensehttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2021-10-08T00:45:08Z
dc.description.versionAccepted Manuscript (AM)
gro.description.notepublicThis publication has been entered in Griffith Research Online as an advanced online version.
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.authorSharma, Alok


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