Subject based feature selection for hybrid brain computer interface using genetic algorithm and support vector machine

Feature selection is of great importance in hybrid BCI systems to reduce dimensionality, improve interpretability, and optimize the classification performance. In this research, we present a subject-specific feature selection technique based on the modified genetic algorithm (GA) and support vector machine (SVM) classifier. The GA includes an explored list feature and logical checkpoints to prevent premature convergence and efficiently search the space of features. Experiments were performed on publicly available hybrid EEG-EMG and EEG-fNIRS datasets. Evaluation of the proposed method was performed for different channel counts, window frame sizes and lengths of feature combination (2, 3 and 4). On average, classification accuracy improved by 4 % and 5 % for EEG-EMG and EEG-fNIRS modalities, respectively, compared to baseline. The framework outperforms traditional filter- and wrapper-based feature selection methods on representative subjects, confirming its robustness and adaptability across individual neural patterns. These results highlight the importance of personalized feature selection in hybrid BCIs and demonstrate the viability of evolutionary algorithms for real-time, low-latency brain–machine applications.