Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals

Muhammad Sudipto Siam Dip; Mohammod Abdul Motin; Md. Anik Hasan; Sumaiya Kabir

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arXiv · 2504.03984

Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals

Muhammad Sudipto Siam Dip·Mohammod Abdul Motin·Md. Anik Hasan·Sumaiya Kabir

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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.

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BibTeX RIS

Muhammad Sudipto Siam Dip, Mohammod Abdul Motin, Md. Anik Hasan, Sumaiya Kabir. 2025-04-04. Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals. https://arxiv.org/abs/2504.03984

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