Attention-controlled assistive wrist rehabilitation using a low-cost EEG sensor

Min Li, Ziting Liang, Bo He, Chen-Guang Zhao, Wei Yao, Guanghua Xu, Jun Xie, Lei Cui

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)
72 Downloads (Pure)


It is essential to make sure patients be actively involved in motor training using robot-assisted rehabilitation to achieve better rehabilitation outcomes. This paper introduces an attention-controlled wrist rehabilitation method using a low-cost EEG sensor. Active rehabilitation training is realized using a threshold of the attention level measured by the low-cost EEG sensor as a switch for a flexible wrist exoskeleton assisting wrist flexion/extension and radial/ulnar deviation. We present a prototype implementation of this active training method and provide a preliminary evaluation. The feasibility of the attention-based control was proven with the overall actuation success rate of 95%. The experimental results also proved that the visual guidance was helpful for the users to concentrate on the wrist rehabilitation training: two types of visual guidance, namely, looking at the hand motion shown on a video and looking at the user's own hand had no significant performance difference. A general threshold of a certain group of users can be utilized in the wrist robot control rather than a customized threshold to simplify the procedure.
Original languageEnglish
Article number8686128
Pages (from-to)6497-6507
Number of pages11
JournalIEEE Sensors Journal
Issue number15
Early online date11 Apr 2019
Publication statusPublished - 1 Aug 2019


  • rehabilitation robots
  • wrist rehabilitation
  • exoskeleton
  • brain-controlled robots
  • brain-computer interface


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