Denoising Algorithm for Event-Related Desynchronization-Based Motor Intention Recognition in Robot-assisted Stroke Rehabilitation Training with Brain-Machine Interaction.

Journal: Journal of neuroscience methods
Published Date: Aug 15, 2020

Abstract

BACKGROUND: Rehabilitation robots integrated with brain-machine interaction (BMI) can facilitate stroke patients' recovery by closing the loop between motor intention and actual movement. The main challenge is to identify the patient's motor intention based on large training datasets with noise contamination in the Electroencephalogram (EEG) signal.

Authors

  • Tianyu Jia
    Department of Mechanical Engineering, Division of Intelligent and Biomimetic Machinery, State Key Laboratory of Tribology, Tsinghua University, Beijing, China.
  • Ke Liu
    State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, P.R. China.
  • Chao Qian
    Division of Intelligent and Bio-mimetic Machinery, The State Key Laboratory of Tribology, Tsinghua University, Beijing, 100084, China. Electronic address: qianchao@mail.tsinghua.edu.cn.
  • Chong Li
    Division of Intelligent and Biomechanical System, State Key Laboratory of Tribology, Tsinghua University, Haidian, Beijing, China.
  • Linhong Ji
    Division of Intelligent and Biomechanical System, State Key Laboratory of Tribology, Tsinghua University, Haidian, Beijing, China.

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