Optimization of Signal Features in EEG-BCI Based on Multi-Class Subject-Specific Motor Imagery Classification

Motor Imagery (MI)-based Brain–Computer Interfaces (BCIs) often rely heavily on subject-specific feature selection to achieve high classification accuracy. However, this introduces substantial computational overhead and calibration friction that can limit clinical translation. This study benchmarks three distinct feature selection algorithms (Genetic Algorithm (GA), Differential Evolution (DE), Relief-F) to determine whether a universal feature set comprising common, shared features identified across subjects can be reliably established, thereby reducing or eliminating the need for subject-specific feature optimization. To evaluate this utilizing a within-subject approach, the methodology analyzes multi-class MI data sourced from the BCI Competition IV Dataset 2A and the PhysioNet EEGMI dataset. The processing pipeline applies Independent Component Analysis (ICA) for artifact removal, selecting a single clean representative component for each subject for analysis. Data augmentation is applied to increase trial size and improve classifier generalization. Features are extracted across multiple domains, consisting of time-domain metrics, Wavelet Packet Decomposition (WPD) nodes, band ratios, and absolute and relative spectral power. Classification is performed using K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and a hybrid deep learning model leveraging the combined strengths of EEGNet and DeepConvNet. The evaluation reveals that DE-SVM achieves the best overall performance on the BCI Competition dataset with an average accuracy of 93.48%, while GA-SVM emerges as the top performer on PhysioNet with an accuracy of 89.71%. Furthermore, an ablation study confirms that SVM is the top-performing classifier across both datasets with average accuracies of 88.29% (BCI Competition) and 90.26% (PhysioNet). Statistical analysis of these ablation results confirms that a universal feature set can be robustly utilized within an MI-BCI system. The findings demonstrate that fixed, generalized feature subsets can effectively replace dynamic subject-specific optimization, offering a streamlined pathway toward more efficient and scalable BCI deployment.