Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162830
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dc.contributor.authorSadiq, Muhammad Tariqen_US
dc.contributor.authorYu, Xiaojunen_US
dc.contributor.authorYuan, Zhaohuien_US
dc.contributor.authorAziz, Muhammad Zulkifalen_US
dc.contributor.authorRehman, Naveed uren_US
dc.contributor.authorDing, Weipingen_US
dc.contributor.authorXiao, Gaoxien_US
dc.date.accessioned2022-11-10T08:23:49Z-
dc.date.available2022-11-10T08:23:49Z-
dc.date.issued2022-
dc.identifier.citationSadiq, M. T., Yu, X., Yuan, Z., Aziz, M. Z., Rehman, N. U., Ding, W. & Xiao, G. (2022). Motor imagery BCI classification based on multivariate variational mode decomposition. IEEE Transactions On Emerging Topics in Computational Intelligence, 6(5), 1177-1189. https://dx.doi.org/10.1109/TETCI.2022.3147030en_US
dc.identifier.issn2471-285Xen_US
dc.identifier.urihttps://hdl.handle.net/10356/162830-
dc.description.abstractIn this article, a novel computer-aided diagnosis framework is proposed for the classification of motor imagery (MI) electroencephalogram (EEG) signals. First, a multivariate variational mode decomposition (MVMD) method was employed to obtain joint modes in frequency scale across all channels. Second, several multi-domain features (time domain, frequency domain, nonlinear and geometrical) were extracted from each EEG signal, and to further enhance the classification performance of different MI EEG signals, a variety of wrapper and filter feature selection methods were utilized with different channel combinations. Finally, to avoid a large number of training sessions for a particular device, extensive subject-independent experiments were performed. The MVMD applied to 18-channel EEG from the motor cortex area in combination with the ReliefF feature selection method achieved an average classification accuracy of 99.8% for a subject-dependent while 98.3% for subject-independent experiments. Besides the aforementioned combination provide above 99% accuracy for subjects with sufficient or small training samples for both subject-dependent or independent cases. These promising findings suggest that the proposed framework is flexible to use for subject-dependent or independent BCI systems.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Emerging Topics in Computational Intelligenceen_US
dc.rights© 2022 IEEE. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleMotor imagery BCI classification based on multivariate variational mode decompositionen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1109/TETCI.2022.3147030-
dc.identifier.scopus2-s2.0-85124817158-
dc.identifier.issue5en_US
dc.identifier.volume6en_US
dc.identifier.spage1177en_US
dc.identifier.epage1189en_US
dc.subject.keywordsFeature Extractionen_US
dc.subject.keywordsElectroencephalographyen_US
dc.description.acknowledgementThis work was supported in part by the Key Research and Development Program of Shaanxi, China under Grant 2021SF-342, in part by China Postdoctoral Science Foundation under Grant 2018M641013, and in part by the Postdoctoral Science Foundation of Shaanxi Province, China under Grant 2018BSHYDZZ05.en_US
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item.fulltextNo Fulltext-
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