Lv, Renjie and Chang, Wenwen and Yan, Guanghui and Nie, Wenchao and Zheng, Lei and Guo, Bin and Sadiq, Muhammad Tariq (2024) A novel recognition and classification approach for motor imagery based on spatio-temporal features. IEEE Journal of Biomedical and Health Informatics, PP. pp. 1-15. DOI https://doi.org/10.1109/JBHI.2024.3464550 (In Press)
Lv, Renjie and Chang, Wenwen and Yan, Guanghui and Nie, Wenchao and Zheng, Lei and Guo, Bin and Sadiq, Muhammad Tariq (2024) A novel recognition and classification approach for motor imagery based on spatio-temporal features. IEEE Journal of Biomedical and Health Informatics, PP. pp. 1-15. DOI https://doi.org/10.1109/JBHI.2024.3464550 (In Press)
Lv, Renjie and Chang, Wenwen and Yan, Guanghui and Nie, Wenchao and Zheng, Lei and Guo, Bin and Sadiq, Muhammad Tariq (2024) A novel recognition and classification approach for motor imagery based on spatio-temporal features. IEEE Journal of Biomedical and Health Informatics, PP. pp. 1-15. DOI https://doi.org/10.1109/JBHI.2024.3464550 (In Press)
Abstract
Motor imagery, as a paradigm of brain- machine interfaces, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-machine interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain net- works are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88.39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99.31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional con- nectivity characteristics of different motor tasks and their corresponding brain regions.
Item Type: | Article |
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Uncontrolled Keywords: | Brain-computer Interface; Functional Brain Networks; graph convolutional networks; Graph Theory; motor imagery |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 19 Sep 2024 15:11 |
Last Modified: | 02 Nov 2024 00:20 |
URI: | http://repository.essex.ac.uk/id/eprint/39213 |
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