TY - JOUR
T1 - Motor Imagery BCI Classification Based on Multivariate Variational Mode Decomposition
AU - Sadiq, Muhammad Tariq
AU - Yu, Xiaojun
AU - Yuan, Zhaohui
AU - Aziz, Muhammad Zulkifal
AU - Rehman, Naveed ur
AU - Ding, Weiping
AU - Xiao, Gaoxi
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2022/10
Y1 - 2022/10
N2 - 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.
AB - 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.
KW - Brain-computer interfaces
KW - Electroencephalography
KW - Feature extraction
KW - Signal resolution
KW - Time series analysis
KW - Time-domain analysis
KW - Time-frequency analysis
KW - Training
KW - feature selection methods
KW - motor imagery
KW - multivariate variational mode decomposition
KW - subject independent
KW - subject specific
U2 - 10.1109/tetci.2022.3147030
DO - 10.1109/tetci.2022.3147030
M3 - Journal article
SN - 2471-285X
VL - 6
SP - 1177
EP - 1189
JO - IEEE Transactions on Emerging Topics in Computational Intelligence
JF - IEEE Transactions on Emerging Topics in Computational Intelligence
IS - 5
ER -