Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162830
Title: Motor imagery BCI classification based on multivariate variational mode decomposition
Authors: Sadiq, Muhammad Tariq
Yu, Xiaojun
Yuan, Zhaohui
Aziz, Muhammad Zulkifal
Rehman, Naveed ur
Ding, Weiping
Xiao, Gaoxi
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Sadiq, 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.2022.3147030
Journal: IEEE Transactions on Emerging Topics in Computational Intelligence
Abstract: In 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.
URI: https://hdl.handle.net/10356/162830
ISSN: 2471-285X
DOI: 10.1109/TETCI.2022.3147030
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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