Neural Control and Learning of Simulated Hand Movements With an EMG-Based Closed-Loop Interface

TL;DR

This paper introduces a comprehensive in silico neuromechanical model combining musculoskeletal simulation, reinforcement learning, and EMG synthesis to study hand movements and neural control in a virtual environment.

Contribution

It presents a novel integrated framework that models feedback, control, and neural activity for virtual neurophysiological experiments, enabling adaptive hand movement tasks.

Findings

Virtual hand movements learned via RL are robust to perturbations

The model synchronizes kinematics, dynamics, and neural signals

Framework facilitates evaluation of neural controllers and synthetic data generation

Abstract

The standard engineering approach when facing uncertainty is modelling. Mixing data from a well-calibrated model with real recordings has led to breakthroughs in many applications of AI, from computer vision to autonomous driving. This type of model-based data augmentation is now beginning to show promising results in biosignal processing as well. However, while these simulated data are necessary, they are not sufficient for virtual neurophysiological experiments. Simply generating neural signals that reproduce a predetermined motor behaviour does not capture the flexibility, variability, and causal structure required to probe neural mechanisms during control tasks. In this study, we present an in silico neuromechanical model that combines a fully forward musculoskeletal simulation, reinforcement learning, and sequential, online electromyography synthesis. This framework provides not only synchronised kinematics, dynamics, and corresponding neural activity, but also explicitly models feedback and feedforward control in a virtual participant. In this way, online control problems can be represented, as the simulated human adapts its behaviour via a learned RL policy in response to a neural interface. For example, the virtual user can learn hand movements robust to perturbations or the control of a virtual gesture decoder. We illustrate the approach using a gesturing task within a biomechanical hand model, and lay the groundwork for using this technique to evaluate neural controllers, augment training datasets, and generate synthetic data for neurological conditions.