Motor-imagery brain-computer interfaces, as rehabilitation tools for motor-disabled individuals, could inherently enrich neuroplasticity and subsequently restore mobility. However, this endeavour's significant challenge is classifying left and right leg motor imagery tasks from non-stationary EEG signals. A subject-independent feature extraction method is essential in a BCI system, and this work involves developing a subject-independent algorithm to classify left/right leg motion intention. The Multivariate Empirical Mode Decomposition was used to decompose EEG during left and right foot movements during imagery tasks. We validated our proposed algorithm using open-access motor imagery data to detect the user's mental intention from EEG. Five subjects of various performance categories with almost 150 trials for each left/right leg MI of hand/leg/tongue, HaLT Paradigm, utilizing C3, C4, and Cz channels were examined to generalize this study to all subjects. A set of statistical features were extracted from the intrinsic mode functions, and the most relevant features were selected for classification using Sequential Floating Feature Selection. Different classifiers were trained using extracted features, and their performances' were evaluated. The findings suggest that the non-linear support vector machine is the best classification model, resulting in the mean classification sensitivity, specificity, precision, negative predictive value, F-measure, 98.15%, 90.74%, 91.97%, 98.33%, 94.72%, 94.44%, respectively. The proposed subject-independent signal processing method significantly improved the offline calibration mode by eliminating the frequency selection step, making it the common-used method for different types of MI-based BCI participants. Offline evaluations suggest that it can lead to significant increases in classification accuracy in comparison to current approaches.
Interpreting linear support vector machine models with heat map molecule coloring
