An EOG-based wheelchair robotic arm system for assisting patients with severe spinal cord injuries.

Journal: Journal of neural engineering
PMID: 30620927

Abstract

OBJECTIVE: In this study, we combine a wheelchair and an intelligent robotic arm based on an electrooculogram (EOG) signal to help patients with spinal cord injuries (SCIs) accomplish a self-drinking task. The main challenge is to accurately control the wheelchair to ensure that the randomly located object is within a limited reachable space of the robotic arm (length: 0.8 m; width: 0.4 m; height: 0.6 m), which requires decimeter-level precision, and is still undemonstrated for EOG-based systems as well as EEG-based systems.

Authors

  • Qiyun Huang
    Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou 510640, People's Republic of China.
  • Yang Chen
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.
  • Zhijun Zhang
  • Shenghong He
  • Rui Zhang
    Department of Cardiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
  • Jun Liu
    Department of Radiology, Second Xiangya Hospital, Changsha, Hunan, China.
  • Yuandong Zhang
  • Ming Shao
  • Yuanqing Li
    Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.

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