Neural Decoding of Robot-Assisted Gait during Rehabilitation after Stroke

Jose L. Contreras-Vidal, Magdo Bortole, Fangshi Zhu, Kevin Nathan*, Anusha Venkatakrishnan, Gerard E. Francisco, Rogelio Soto, Jose L Pons

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Objective Advancements in robot-assisted gait rehabilitation and brain-machine interfaces may enhance stroke physiotherapy by engaging patients while providing information about robot-induced cortical adaptations. We investigate the feasibility of decoding walking from brain activity in stroke survivors during therapy using a powered exoskeleton integrated with an electroencephalography-based brain-machine interface. Design The H2 powered exoskeleton was designed for overground gait training with actuated hip, knee, and ankle joints. It was integrated with active-electrode electroencephalography and evaluated in hemiparetic stroke survivors for 12 sessions per 4 wks. A continuous-time Kalman decoder operating on delta-band electroencephalography was designed to estimate gait kinematics. Results Five chronic stroke patients completed the study with improvements in walking distance and speed training for 4 wks, correlating with increased offline decoding accuracy. Accuracies of predicted joint angles improved with session and gait speed, suggesting an improved neural representation for gait, and the feasibility to design an electroencephalography-based brain-machine interface to monitor brain activity or control a rehabilitative exoskeleton. Conclusions The Kalman decoder showed increased accuracies as the longitudinal training intervention progressed in the stroke participants. These results demonstrate the feasibility of studying changes in patterns of neuroelectric cortical activity during poststroke rehabilitation and represent the first step in developing a brain-machine interface for controlling powered exoskeletons.

Original languageEnglish (US)
Pages (from-to)541-550
Number of pages10
JournalAmerican Journal of Physical Medicine and Rehabilitation
Volume97
Issue number8
DOIs
StatePublished - Aug 1 2018

Keywords

  • BCI
  • BMI
  • Brain-Machine Interface
  • Exoskeleton
  • Gait
  • Legs
  • Rehabilitation
  • Stroke

ASJC Scopus subject areas

  • Physical Therapy, Sports Therapy and Rehabilitation
  • Rehabilitation

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