Real-Time Myocontrol of a Human-Computer Interface by Paretic Muscles After Stroke

Biosignals from skeletal muscle have been used to control human-machine interface. Signals from paretic muscles of humans with stroke are distorted and highly variable. Here, we examine the stability of surface electromyography (sEMG) features from paretic hand muscles to enable continuous, real-time multicommand control of a human-computer interface (HC!). Subjects with long standing cortical strokes (>6 months, n = 12) and neurologically intact controls (n = 12) performed two wrist rotations (wrist extend and wrist flex) and two grips (power grip and fine grip) with the nondominant (controls) or paretic (stroke patients) hand. Data reduction analyses revealed a distinct pattern of coactivation across muscles for each gesture. These synergies were similar for control and stroke groups and stable across sessions. Results of offline experiments involving wrist rotation and hand grips confirmed that gestures performed in isolation or combination were recognized at greater than chance level in both groups (p