Simultaneous Decoding of Wrist Angles and Grasp Forces Based on Channel-Wise Cumulative Spike Trains

Understanding the underlying mechanism of neuromuscular system on motion/force generation is essential for human-machine interfacing. However, simultaneous decoding of wrist angles and grasp forces from neural signals remains an open challenge in the field of neural interfacing. In this study, we proposed a scheme leveraging channel-wise cumulative spike trains (cw-CSTs) of motor units to simultaneously decode wrist angles and grasp forces. Specifically, a spatial spike detection method was utilized to detect cw-CST from surface electromyography, observing as much as possible of motor unit activities. Accordingly, we extracted three neural features to drive the decoders, including a twitch force model-based (cw-MUdrive) and a discharge rate-based (DR-cwCST) neural features derived from cw-CSTs, and DR of motor units (DR-MUST) decomposed by a conventional blind source separation algorithm. Wrist- and hand-specific decoders were built to estimate wrist angles and grasp forces via Gaussian process regression. Experiments were conducted with ten subjects, in which they activated wrist motions and grasp forces concurrently. We evaluated the performance with both accuracy and output stability. Results demonstrated that the cwCST-based neural features outperformed the conventional DR-MUST features with both higher accuracy and stability metrics. Additionally, cw-MUdrive performed better than DR-cwCST in grasp force estimation and comparable to DR-cwCST in wrist angle estimation. The outcome provides an effective solution for simultaneously decoding wrist movements and hand grasp forces, promoting the development of natural control in neural interface.