Assessing Neuromorphic Computing for Fingertip Force Decoding from Electromyography

TL;DR

This study compares a neuromorphic spiking neural network and a temporal convolutional network for decoding fingertip force from electromyography signals, highlighting the potential of neuromorphic approaches in neural interface applications.

Contribution

It evaluates the performance of a neuromorphic SNN against a TCN in force decoding from EMG, demonstrating the feasibility of neuromorphic models as realistic neural interface baselines.

Findings

TCN achieved 4.44% MVC RMSE, SNN achieved 8.25%.

Both models showed high correlation with actual force.

SNN can be improved with architectural refinements.

Abstract

High-density surface electromyography (HD-sEMG) provides a noninvasive neural interface for assistive and rehabilitation control, but mapping neural activity to user motor intent remains challenging. We assess a spiking neural network (SNN) as a neuromorphic architecture against a temporal convolutional network (TCN) for decoding fingertip force from motor-unit (MU) firing derived from HD-sEMG. Data were collected from a single participant (10 trials) with two forearm electrode arrays; MU activity was obtained via FastICA-based decomposition, and models were trained on overlapping windows with end-to-end causal convolutions. On held-out trials, the TCN achieved 4.44% MVC RMSE (Pearson r = 0.974) while the SNN achieved 8.25% MVC (r = 0.922). While the TCN was more accurate, we view the SNN as a realistic neuromorphic baseline that could close much of this gap with modest architectural and hyperparameter refinements.