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Neural Decoding of Robot-Assisted Gait During Rehabilitation After Stroke

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.

Langue : ANGLAIS

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