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

Journal: Brain research
Published Date: Jun 12, 2024

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.

Authors

  • Yongkang Rao
    Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China.
  • Le Zhang
    State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China; College of Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Science and Technology on Particle Materials, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 361021, China.
  • Ruijun Jing
    Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China.
  • Jiabing Huo
    Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China.
  • Kunxian Yan
    Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China.
  • Jian He
    School of Software Engineering, Beijing University of Technology, Beijing, China. Electronic address: jianhee@bjut.edu.cn.
  • Xiaojuan Hou
    Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China.
  • Jiliang Mu
    Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China.
  • Wenping Geng
    Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China.
  • Haoran Cui
    Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China.
  • Zeyu Hao
    Science and Technology on Electronic Test & Measurement Laboratory, The 41st Institute of China Electronic Technology Group Corporation, Qingdao 266555, China.
  • Xiang Zan
    Shanxi Provincial People's Hospital, the Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, China.
  • Jiuhong Ma
    Shanxi Provincial People's Hospital, the Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, China.
  • Xiujian Chou
    Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China.

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