Decoding upper limb residual muscle activity in severe chronic stroke.

Area of Science:

• Neuroscience
• Rehabilitation Medicine
• Biomedical Engineering

Background:

• Stroke is a primary cause of long-term motor disability, particularly affecting hand function.
• Current rehabilitative treatments offer limited benefits for stroke patients with severe hand weakness.
• Emerging therapies like brain-controlled robotics and functional electrical stimulation show promise but require accurate intention decoding.

Purpose of the Study:

• To investigate if residual muscle activity can predict intended arm movements in chronic stroke patients with severe paralysis.
• To evaluate the feasibility of using surface electromyography (sEMG) for decoding motor intentions in this patient population.

Main Methods:

• Surface electromyography (sEMG) recorded muscle activity from 41 severely impaired chronic stroke patients.
• A feed-forward neural network classifier was used to decode intended movements from both paretic and nonparetic muscles.
• The contribution of individual muscles to intended movements was analyzed.

Main Results:

• Accurate decoding ( >65%) of up to six arm movements was achieved in over 97% of nonparetic muscles.
• Decoding was successful in 46% of paretic muscles, indicating preserved neuronal innervation.
• Movement prediction was reliable across a range of intended arm motions.

Conclusions:

• Residual muscle activity in severely paralyzed stroke patients retains predictive information about intended movements.
• This decoding capability can be leveraged for neurorehabilitative treatments, including controlling arm prostheses.
• The findings offer a pathway for restoring functional arm movement in a significant subset of chronic stroke survivors.