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Journal of Healthcare Engineering
Volume 2017, Article ID 6819056, 9 pages
https://doi.org/10.1155/2017/6819056
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.

Linked References

  1. R. N. Goodman, J. C. Rietschel, A. Roy et al., “Increased reward in ankle robotics training enhances motor control and cortical efficiency in stroke,” Journal of Rehabilitation Research and Development, vol. 51, pp. 213–227, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. N. Hogan, H. I. Krebs, B. Rohrer et al., “Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery,” The Journal of Rehabilitation Research and Development, vol. 43, pp. 605–618, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. Z. Zhou, Y. Zhou, N. Wang, F. Gao, K. Wei, and Q. Wang, “A proprioceptive neuromuscular facilitation integrated robotic ankle–foot system for post stroke rehabilitation,” Robotics and Autonomous Systems, vol. 73, pp. 111–122, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. J. T. Gwin and D. P. Ferris, “Beta- and gamma-range human lower limb corticomuscular coherence,” Frontiers in Human Neuroscience, vol. 6, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Roy, H. I. Krebs, D. J. Williams et al., “Robot-aided neurorehabilitation: a novel robot for ankle rehabilitation,” IEEE Transactions on Robotics, vol. 25, pp. 569–582, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. C. M. McCrimmon, C. E. King, P. T. Wang, S. C. Cramer, Z. Nenadic, and A. H. Do, “Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: a safety study,” Journal of Neuroengineering and Rehabilitation, vol. 12, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Pittaccio, F. Zappasodi, S. Viscuso et al., “Primary sensory and motor cortex activities during voluntary and passive ankle mobilization by the SHADE orthosis,” Human Brain Mapping, vol. 32, pp. 60–70, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. R. W. Selles, X. Li, F. Lin, S. G. Chung, E. J. Roth, and L. Q. Zhang, “Feedback-controlled and programmed stretching of the ankle plantarflexors and dorsiflexors in stroke: effects of a 4-week intervention program,” Archives of Physical Medicine and Rehabilitation, vol. 86, pp. 2330–2336, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Roy, H. I. Krebs, C. T. Bever, L. W. Forrester, R. F. Macko, and N. Hogan, “Measurement of passive ankle stiffness in subjects with chronic hemiparesis using a novel ankle robot,” Journal of Neurophysiology, vol. 105, pp. 2132–2149, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. L. W. Forrester, A. Roy, A. Krywonis, G. Kehs, H. I. Krebs, and R. F. Macko, “Modular ankle robotics training in early subacute stroke: a randomized controlled pilot study,” Neurorehabilitation and Neural Repair, vol. 28, pp. 678–687, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Zhang, Improving Effectiveness of Robot-Assisted Ankle Rehabilitation via Biomechanical Assessment and Interaction Control, [Ph.D. thesis], Department of Mechanical Engineering, The University of Auckland, Auckland 1142, New Zealand, 2016.
  12. L. Q. Zhang, S. G. Chung, Z. Bai et al., “Intelligent stretching of ankle joints with contracture/spasticity,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 10, pp. 149–157, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. T. Sukal-Moulton, T. Clancy, L. Q. Zhang, and D. Gaebler-Spira, “Clinical application of a robotic ankle training program for cerebral palsy compared to the research laboratory application: does it translate to practice?” Archives of Physical Medicine and Rehabilitation, vol. 95, pp. 1433–1440, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. G. C. Burdea, D. Cioi, A. Kale, W. E. Janes, S. A. Ross, and J. R. Engsberg, “Robotics and gaming to improve ankle strength, motor control and function in children with cerebral palsy—a case study series,” IEEE Transaction on Neural System and Rehabilitation Engineering, vol. 21, no. 2, pp. 165–173, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Girone, G. Burdea, and M. Bouzit, “The ‘Rutgers ankle’ orthopedic rehabilitation interface,” in Proceedings of the ASME Haptics Symposium, vol. DSC-67, pp. 305–312, 1999.
  16. K. P. Michmizos, S. Rossi, E. Castelli, P. Cappa, and H. I. Krebs, “Robot-aided neurorehabilitation: a pediatric robot for ankle rehabilitation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, pp. 1056–1067, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. J. Chang, J. N. Liang, M. J. Hsu, H. Y. Lien, C. Y. Fang, and C. H. Lin, “Effects of continuous passive motion on reversing the adapted spinal circuit in humans with chronic spinal cord injury,” Archives of Physical Medicine and Rehabilitation, vol. 94, pp. 822–828, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. Z. Lin, C. Zhang, W. Wu, and X. Gao, “Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs,” IEEE Transactions on Biomedical Engineering, vol. 54, pp. 1172–1176, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. P. F. Diez, S. M. Torres Müller, V. A. Mut et al., “Commanding a robotic wheelchair with a high-frequency steady-state visual evoked potential based brain-computer interface,” Medical Engineering & Physics, vol. 35, pp. 1155–1164, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Zhao, W. Li, and M. Li, “Comparative study of SSVEP- and P300-based models for the telepresence control of humanoid robots,” PLoS One, vol. 10, article e0142168, 2015. View at Publisher · View at Google Scholar · View at Scopus
  21. B. Choi and S. Jo, “A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition,” PLoS One, vol. 8, article e74583, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. P. Stawicki, F. Gembler, and I. Volosyak, “Driving a semiautonomous mobile robotic car controlled by an SSVEP-based BCI,” Computational Intelligence and Neuroscience, vol. 2016, Article ID 4909685, 14 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  23. P. Horki, T. Solis-Escalante, C. Neuper, and G. Müller-Putz, “Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb,” Medical & Biological Engineering & Computing, vol. 49, pp. 567–577, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. I. Volosyak, H. Cecotti, and A. Graser, “Optimal visual stimuli on LCD screens for SSVEP based brain-computer interfaces,” in International IEEE/EMBS Conference on Neural Engineering, pp. 447–450, Antalya, Turkey, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. T. D. Lagerlund, F. W. Sharbrough, C. R. Jack Jr. et al., “Determination of 10–20 system electrode locations using magnetic resonance image scanning with markers,” Electroencephalography and Clinical Neurophysiology, vol. 86, pp. 7–14, 1993. View at Publisher · View at Google Scholar · View at Scopus
  26. C. Lu, J. W. Cooley, and R. Tolimieri, “FFT algorithms for prime transform sizes and their implementations on VAX, IBM3090VF, and IBM RS/6000,” IEEE Transactions on Signal Processing, vol. 41, pp. 638–648, 1993. View at Publisher · View at Google Scholar · View at Scopus
  27. J. R. Wolpaw, N. Birbaumer, W. J. Heetderks et al., “Brain-computer interface technology: a review of the first international meeting,” IEEE Transactions on Rehabilitation Engineering, vol. 8, p. 164, 2000. View at Publisher · View at Google Scholar · View at Scopus
  28. X. Chen, Z. Chen, S. Gao, and X. Gao, “Brain-computer interface based on intermodulation frequency,” Journal of Neural Engineering, vol. 10, article 066009, 2013. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Cheng, X. Gao, S. Gao, and D. Xu, “Design and implementation of a brain-computer interface with high transfer rates,” IEEE Transactions on Biomedical Engineering, vol. 49, pp. 1181–1186, 2002. View at Publisher · View at Google Scholar · View at Scopus
  30. J. T. Gwin and D. P. Ferris, “An EEG-based study of discrete isometric and isotonic human lower limb muscle contractions,” Journal of Neuroengineering and Rehabilitation, vol. 9, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. E. Formaggio, S. F. Storti, I. Boscolo Galazzo et al., “Modulation of event-related desynchronization in robot-assisted hand performance: brain oscillatory changes in active, passive and imagined movements,” Journal of Neuroengineering and Rehabilitation, vol. 10, 2013. View at Publisher · View at Google Scholar · View at Scopus
  32. M. Takahashi, K. Takeda, Y. Otaka et al., “Event related desynchronization-modulated functional electrical stimulation system for stroke rehabilitation: a feasibility study,” Journal of Neuroengineering and Rehabilitation, vol. 9, 2012. View at Publisher · View at Google Scholar · View at Scopus