Motor Imagery BCI Classification Based on Multivariate Variational Mode Decomposition

Muhammad Tariq Sadiq, Xiaojun Yu, Zhaohui Yuan, Muhammad Zulkifal Aziz, Naveed ur Rehman, Weiping Ding, Gaoxi Xiao

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)1177-1189
Number of pages13
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Issue number5
Publication statusPublished - 16 Feb 2022


  • Brain-computer interfaces
  • feature selection methods
  • motor imagery
  • multivariate variational mode decomposition
  • subject independent
  • subject specific


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