Advanced EEG signal classification for neural prosthetic devices using metaheuristic and deep learning techniques

The combined evolved metaheatistic for feature subset selection with deep learning architectures is a powerful approach for an accurate classification EEG signals. The findings confirm that the COA-based approach provides a robust, computationally-efficient, and scalable method for achieving high-accuracy classification-essential for promoting the reliability and real-time operation of future neural prosthetic control systems.