Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
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.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.
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.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.subject.keywordsFeature Extractionen_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
item.fulltextNo Fulltext-
Appears in Collections:EEE Journal Articles

Citations 50

Updated on Jan 28, 2023

Web of ScienceTM
Citations 50

Updated on Feb 4, 2023

Page view(s)

Updated on Feb 4, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.