Multi-Domain Feature Fusion with OVO-SVM for Multi-Class Motor Imagery EEG Classification and Motor Control Validation
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The number of people in the world who suffer from a stroke, multiple sclerosis and spinal cord injuries is estimated to reach 2 to 4 percent of the population, often with severe motor disabilities. Electroencephalogram (EEG) based brain-computer interfaces (BCIs) are an effective way of communicating with a person by translating brain signals to meaningful control commands. Motor imagery (MI) based BCIs allow users to control external devices by imagining certain movements. Although conventional methods, such as common spatial pattern (CSP) and power spectral density (PSD), have shown good performance, they may not be able to capture complementary time-frequency and nonlinear characteristics of EEG signals. This study introduces a robust multi-domain framework for four class MI classification using right hand, left hand, foot and tongue imagery. The proposed system uses a combination of CSP, PSD, log variance, wavelet transform and bispectrum analysis to extract the spatial, spectral, temporal and nonlinear features. The one vs. one support vector machine (OVO-SVM) classifier is used to improve the class separability and to address the class imbalance. In addition to classification offline, an end-to-end EEG-driven control architecture is developed, in which decoded motor imagery decisions are directly mapped into coordinated control commands for two independent motors, allowing for continuous translation of cognitive intentions into physical motion. Experimental results show that the performance is good for all subjects, and Subject 3 shows the highest accuracy (95.83%) and Kappa (0.94), while the average accuracy and Kappa are 74.3% and 0.65, respectively. System-Level Validation with Independent Evaluation Dataset Stable temporal prediction behaviour Reliable motor command generation Coordination of multi-motor control Coordination of spatial motion trajectories Stable temporal prediction behaviour Reliable motor command generation Coordination of multi-motor control Coordination of spatial motion trajectories Bridging the Gap between EEG- based Classification and Practical Motor Control Implementation These findings reveal the great potential of the proposed framework for real world MI-based BCI applications.
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