1. An optimized EEGNet decoder for decoding motor image of four class fingers flexion.
- Author
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Rao Y, Zhang L, Jing R, Huo J, Yan K, He J, Hou X, Mu J, Geng W, Cui H, Hao Z, Zan X, Ma J, and Chou X
- Subjects
- Humans, Adult, Male, Female, Young Adult, Movement physiology, Brain physiology, Fingers physiology, Electroencephalography methods, Brain-Computer Interfaces, Neural Networks, Computer
- Abstract
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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)
- Published
- 2024
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