Variational Mode Decomposition for Classification of EEG motor Imagery Signals: A Comprehensive Study and Evaluation of Entropy based Measures

Nowadays, research on human movement related to Brain-Computer Interface (BCI) technology is increasing significantly. The objective of this research is primarily on improving neurological rehabilitation. The complexity and non-linearity of electroencephalogram (EEG) data present challenges for conventional signal processing techniques. The present study addresses these challenges by analyzing the dynamic nature of EEG signals. The Variational Mode Decomposition (VMD) method is used non-recursively to decompose the EEG signals into four different band-limited intrinsic mode functions (IMFs), specifically IMFs $8,9,10$, and 11. To reduce computational load and enhance system performance, 16 motor-cortex-based channels are chosen from a total of $\mathbf{1 1 8}$ channels. Approximate entropy and sample entropy are computed from the IMFs to form a feature vector. This generated feature vector is then fed into different classification algorithms such as LDA, SVM, Decision Tree, and Naive Bayes for classification. The highest average accuracy achieved across all classifiers using approximate entropy is $100 \%$ and corresponding F 1 score are $\mathbf{9 8. 6 7 \%}, \mathbf{9 5. 3 5 \%}$, $\mathbf{9 8. 6 7 \%}, \mathbf{9 7. 5 0 \%}$ respectively, while the sample entropy feature yields classification performances of $\mathbf{9 8. 7 5 \%}, \mathbf{9 9. 3 8 \%}, \mathbf{1 0 0 \%}$, $\mathbf{1 0 0 \%}$ and corresponding F1 score are $\mathbf{9 9. 3 5 \%}, \mathbf{9 8. 7 1 \%}, \mathbf{1 0 0 \%}$, $\mathbf{1 0 0 \%}$ respectively. VMD allows for more accurate decomposition of the signal for precise analysis. Both ApEn and SampEn yield state-of-the-art results when applied to the decomposed signals. This study demonstrates exceptionally well in feature extraction and classification compare to the recent study and making more efficient for real-time application.