Objective: Stroke is a leading cause of long-term motor disability. Stroke
patients with severe hand weakness do not profit from rehabilitative treatments.
Recently, brain-controlled robotics and sequential functional electrical stimulation
allowed some improvement. However, for such therapies to succeed, it is
required to decode patients’ intentions for different arm movements. Here, we
evaluated whether residual muscle activity could be used to predict movements
from paralyzed joints in severely impaired chronic stroke patients. Methods:
Muscle activity was recorded with surface-electromyography (EMG) in 41
patients, with severe hand weakness (Fugl-Meyer Assessment [FMA] hand subscores
of 2.93 2.7), in order to decode their intention to perform six different
motions of the affected arm, required for voluntary muscle activity and to
control neuroprostheses. Decoding of paretic and nonparetic muscle activity
was performed using a feed-forward neural network classifier. The contribution
of each muscle to the intended movement was determined. Results: Decoding
of up to six arm movements was accurate (>65%) in more than 97% of
nonparetic and 46% of paretic muscles. Interpretation: These results demonstrate
that some level of neuronal innervation to the paretic muscle remains
preserved and can be used to implement neurorehabilitative treatments in 46%
of patients with severe paralysis and extensive cortical and/or subcortical
lesions. Such decoding may allow these patients for the first time after stroke to
control different motions of arm prostheses through muscle-triggered rehabilitative
treatments.