Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals
Abstract
Objective: Machine learning- and deep learning-based models have recently been employed in motor imagery intention classification from electroencephalogram (EEG) signals. Nevertheless, there is a limited understanding of feature selection to assist in identifying the most significant features in different spatial locations. Methods: This study proposes a feature selection technique using sequential forward feature selection with support vector machines and feeding the selected features to deep neural networks to classify motor imagery intention using multi-channel EEG. Results: The proposed model was evaluated with a publicly available dataset and achieved an average accuracy of 79.70 percent with a standard deviation of 7.98 percent for classifying two motor imagery scenarios. Conclusions: These results demonstrate that our method effectively identifies the most informative and discriminative characteristics of neural activity at different spatial locations, offering potential for future prosthetics and brain-computer interface applications. Significance: This approach enhances model performance while identifying key spatial EEG features, advancing brain-computer interfaces and prosthetic systems.
Cite
@article{arxiv.2504.03984,
title = {Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals},
author = {Muhammad Sudipto Siam Dip and Mohammod Abdul Motin and Md. Anik Hasan and Sumaiya Kabir},
journal= {arXiv preprint arXiv:2504.03984},
year = {2025}
}