  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.
