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

TL;DR

This paper introduces a new method combining a modified optimization algorithm with deep learning to improve the accuracy of classifying EEG signals for neural prosthetics.

Contribution

The novelty lies in the dynamic, parameter-free coati optimization algorithm (COA) combined with deep learning for efficient and accurate EEG signal classification.

Findings

The COA + CNN model achieved 96.8% classification accuracy on EEG datasets.

COA outperformed PSO, GA, mRMR, and ReliefF by up to 6.5% in accuracy.

The method is computationally efficient and suitable for real-time neural prosthetic systems.

Abstract

For neural prosthetic devices, accurate classification of high dimensional electroencephalography (EEG) signals is significantly impaired by the existence of redundant and irrelevant features that deteriorate the classifier generalization and computation efficiency. This work presents a new and unified optimal-driven framework to challenge these issues and improve EEG-based MI signal decoding.

The proposed method combines a modified feature selection model of coati optimization algorithm (COA) and different machine/deep learning classifiers. The novelty of the COA is its dynamic and parameter-free adaptation mechanism, in association with opposition-based learning a better exploration exploitation balance can be maintained in high-dimensional feature space. The generated optimized feature subsets are then employed to train a battery of classifiers such as support vector machines (SVM), random forests (RF), convolutional neural networks (CNN) and recurrent neural networks (RNN) for motor imagery task recognition. In experiments, we verify SSRC on commonly used benchmark EEG datasets such as the PhysioNet Motor Movement/Imagery dataset.

The experimental results showed that the COA + CNN model had the best performance of classification. The model demonstrated a classification accuracy of 96.8% of prediction, with precision at moderate AH hour and predicted as either being more likely to discharge or remain in care = 96.4%, recall = 96.9% and F1-score = 96.6%. This presents a remarkable 6.5% gain in classification accuracy over the best rival feature selection technique and significantly outperformed conventional metaheuristic algorithms such as PSO (90.3% accuracy) and GA (89.7% accuracy) as well as filter-type techniques such as mRMR (86.8%) and ReliefF (84.3%).

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.

Figures