Conditional Generative Models for Simulation of EMG During Naturalistic Movements
This work addresses a bottleneck for motor neuroscience and human-machine interfaces by enabling faster EMG simulations, though it is incremental as it builds on existing numerical models with a transfer learning approach.
The authors tackled the computational expense of biophysical EMG simulations by proposing BioMime, a conditional generative model that mimics numerical models to predict motor unit activation potentials, reducing computational load and enabling rapid simulation of EMG signals during dynamic movements with high accuracy.
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.