Conditional Generative Models for Simulation of EMG During Naturalistic Movements

TL;DR

This paper introduces BioMime, a conditional generative neural network that efficiently simulates EMG signals during natural movements by learning from complex numerical models, enabling faster and more dynamic simulations.

Contribution

The paper presents a novel transfer learning approach with BioMime, a neural network that mimics advanced EMG models to enable rapid, accurate simulations of dynamic movements.

Findings

BioMime accurately interpolates numerical model outputs.

The model significantly reduces computational load.

It enables real-time simulation of EMG during natural movements.

Abstract

Numerical models of electromyographic (EMG) signals have provided a huge contribution to our fundamental understanding of human neurophysiology and remain a central pillar of motor neuroscience and the development of human-machine interfaces. However, whilst modern biophysical simulations based on finite element methods are highly accurate, they are extremely computationally expensive and thus are generally limited to modelling static systems such as isometrically contracting limbs. As a solution to this problem, we propose a transfer learning approach, in which a conditional generative model is trained to mimic the output of an advanced numerical model. To this end, we present BioMime, a conditional generative neural network trained adversarially to generate motor unit activation potential waveforms under a wide variety of volume conductor parameters. We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy. Consequently, the computational load is dramatically reduced, which allows the rapid simulation of EMG signals during truly dynamic and naturalistic movements.