Simultaneous and proportional control of wrist and hand movements by decoding motor unit discharges in real time

Abstract

Objective. Surface electromyography (EMG) decomposition techniques can be used to establish human-machine interfacing (HMI), but most investigations are implemented offline due to the computational load of the approach. Here, we generalize the offline decomposition algorithm to identify the motor unit (MU) activities in real time, and we propose a MU-based approach for online simultaneous and proportional control (SPC) of multiple motor tasks. Approach. High-density surface EMG signals recorded from forearm muscles were decomposed into motor unit spike trains (MUSTs) with the proposed decomposition method. The MUSTs were first pooled into clusters in the calibration phase and the cumulative discharges of active MUs in each group were extracted as the control signal for each motor task. Then the subjects were instructed to control a virtual cursor with multiple motor tasks involving grasp and wrist movements. Fifteen able-bodied subjects and two patients with limb deficiency participated in the experiments to validate the proposed control scheme. Main results. On average, over 20 MUSTs were identified in real time with an estimated decomposition accuracy 85%. The cumulative discharge in each pool was highly correlated with the activation of the specific motion (R = 0.93 ± 0.05). Moreover, the proposed MU-based method had superior performance in online tests than conventional myo-control methods based on global EMG features. Significance. These results indicate the feasibility of real-time neural decoding in a non-invasive way. Moreover, the superior performance in online tests proves the potential of the MU-based approach for the SPC, promoting the application of EMG decomposition for HMI systems.