MSVTNet: Multi-Scale Vision Transformer Neural Network for EEG-Based Motor Imagery Decoding.

Journal: IEEE journal of biomedical and health informatics
Published Date: Dec 5, 2024

Abstract

OBJECT: Transformer-based neural networks have been applied to the electroencephalography (EEG) decoding for motor imagery (MI). However, most networks focus on applying the self-attention mechanism to extract global temporal information, while the cross-frequency coupling features between different frequencies have been neglected. Additionally, effectively integrating different neural networks poses challenges for the advanced design of decoding algorithms.

Authors

  • Ke Liu
    State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, P.R. China.
  • Tao Yang
    The First Clinical Medical College, The Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.
  • Zhuliang Yu
    College of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong Province, China. Electronic address: zlyu@scut.edu.cn.
  • Weibo Yi
    Beijing Machine and Equipment Institute, Beijing 100854, China.
  • Hong Yu
    University of Massachusetts Medical School, Worcester, MA.
  • Guoyin Wang
  • Wei Wu
    Department of Pharmacy, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.

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