Background Stroke is a leading cause of disability, with up to 80% of survivors experiencing motor impairments. These impairments are attributed to various factors, including reduced neural drive and altered motor unit firing patterns. Rehabilitation aims to restore motor function by enhancing motor unit recruitment and synchronization. High-density electromyography (HD-EMG) is a valuable tool for evaluating these changes in motor unit activity.

Methods We tested a HD-EMG wearable forearm sleeve to investigate the relationship between motor function and motor unit properties including firing rate, motor unit module activation, and coherence. Seven individuals with chronic stroke and seven able-bodied individuals attempted 12 controlled hand and wrist movements while EMG was recorded. Motor units were decomposed across all movements using convolutive blind source separation.

Results Fewer motor units were detectable in individuals with stroke compared to able-bodied participants. There was a significant reduction in motor unit firing rate during specific movements such as wrist flexion and hand open. Motor unit coupling and activation were altered following stroke, with reduced module activation in 8 of the 12 movements attempted. Furthermore, a reduction in coherence for gross movements and an increase in coherence for more dexterous thumb movements suggest altered neural drive to motor units after stroke that is differentially tuned to the complexity of movement. A combined neural control signature, consisting of multiple motor unit features, demonstrated strong correlation (R2 = 0.81) with clinical motor function scores.

Conclusions This study demonstrates that HD-EMG can capture detailed motor unit activity and neural control characteristics across multiple forearm muscles in individuals with chronic stroke. By integrating multiple HD-EMG features, this approach provides new insights into neuromuscular alterations linked to hand motor function after stroke. These findings support the use of HD-EMG for monitoring recovery, predicting outcomes, and guiding more targeted rehabilitation, thus advancing both stroke research and patient care.

The authors have declared no competing interest.

Data collection, algorithm development, data analysis, and manuscript drafting were funded through Battelle Memorial Institute internal research and development funds.

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IRB of Battelle Memorial Institute gave ethical approval for this work.

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