An optimized EEGNet decoder for decoding motor image of four class fingers flexion

• Published in: Brain research Vol. 1841; p. 149085
• Main Authors: Rao, Yongkang, Zhang, Le, Jing, Ruijun, Huo, Jiabing, Yan, Kunxian, He, Jian, Hou, Xiaojuan, Mu, Jiliang, Geng, Wenping, Cui, Haoran, Hao, Zeyu, Zan, Xiang, Ma, Jiuhong, Chou, Xiujian
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15-10-2024
• Subjects: Attention module; Brain–computer interface; Deep learning; Electroencephalogram; Motor imagery; Deep learning; Attention module; Motor imagery; Electroencephalogram; Brain–computer interface
• ISSN: 0006-8993; 1872-6240; 1872-6240
• DOI: 10.1016/j.brainres.2024.149085

The highest classification average accuracy of 87.29% with outstanding decoding performance is obtained for the EEGNet with SimAM attention module, which could be attributed to the fact that the neural feature of finger flexion could be reflected by the different motor cognition.
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•Optimized EEGNet for decoding motor imagery EEG singles of left and right hand of index and thumb with an average accuracy 72.91%.•The feature of event related desynchronization (ERD) and event related synchronization (ERS) of index and thumb were studied.•Optimized EEGNet provided an explanatory convolutional neural network for the neuroscience research based on EEG.
As a cutting-edge technology of connecting biological brain and external devices, brain-computer interface (BCI) exhibits promising applications on extensive fields such as medical and military. As for the disable individuals with four limbs losing the motor functions, it is a potential treatment way to drive mechanical equipments by the means of non-invasive BCI, which is badly depended on the accuracy of the decoded electroencephalogram (EEG) singles. In this study, an explanatory convolutional neural network namely EEGNet based on SimAM attention module was proposed to enhance the accuracy of decoding the EEG singles of index and thumb fingers for both left and right hand using sensory motor rhythm (SMR). An average classification accuracy of 72.91% the data of eight healthy subjects was obtained, which were captured from the one second before finger movement to two seconds after action. Furthermore, the character of event-related desynchronization (ERD) and event related synchronization (ERS) of index and thumb fingers was also studied in this study. These findings have significant importance for controlling external devices or other rehabilitation equipment using BCI in a fine way.