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Journal of Healthcare Engineering
Volume 2017, Article ID 6819056, 9 pages
Research Article

A Feasibility Study of SSVEP-Based Passive Training on an Ankle Rehabilitation Robot

1School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, China
2Department of Mechanical Engineering, University of Auckland, Auckland 1142, New Zealand
3School of Mechanical Engineering, School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK

Correspondence should be addressed to Guoli Zhu; moc.361@hw_uhzlg

Received 5 May 2017; Revised 5 July 2017; Accepted 1 August 2017; Published 17 September 2017

Academic Editor: Duo Wai-Chi Wong

Copyright © 2017 Xiangfeng Zeng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Objective. This study aims to establish a steady-state visual evoked potential- (SSVEP-) based passive training protocol on an ankle rehabilitation robot and validate its feasibility. Method. This paper combines SSVEP signals and the virtual reality circumstance through constructing information transmission loops between brains and ankle robots. The robot can judge motion intentions of subjects and trigger the training when subjects pay their attention on one of the four flickering circles. The virtual reality training circumstance provides real-time visual feedback of ankle rotation. Result. All five subjects succeeded in conducting ankle training based on the SSVEP-triggered training strategy following their motion intentions. The lowest success rate is 80%, and the highest one is 100%. The lowest information transfer rate (ITR) is 11.5 bits/min when the biggest one of the robots for this proposed training is set as 24 bits/min. Conclusion. The proposed training strategy is feasible and promising to be combined with a robot for ankle rehabilitation. Future work will focus on adopting more advanced data process techniques to improve the reliability of intention detection and investigating how patients respond to such a training strategy.