Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913778062327808 |
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| author | Dip, Muhammad Sudipto Siam Motin, Mohammod Abdul Hasan, Md. Anik Kabir, Sumaiya |
| author_facet | Dip, Muhammad Sudipto Siam Motin, Mohammod Abdul Hasan, Md. Anik Kabir, Sumaiya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03984 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals Dip, Muhammad Sudipto Siam Motin, Mohammod Abdul Hasan, Md. Anik Kabir, Sumaiya Signal Processing 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. |
| title | Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.03984 |