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.

翻译：肌电图（EMG）信号的数值模型极大地促进了我们对人类神经生理学的基础认知，仍是运动神经科学与人机接口发展的核心支柱。然而，尽管基于有限元方法的现代生物物理模拟具有高精度，但其计算代价极为高昂，因此通常仅限于模拟静态系统（如等长收缩的肢体）。针对这一问题，我们提出一种迁移学习方法，通过训练条件生成模型来模拟高级数值模型的输出。为此，我们引入BioMime——一种通过对抗性训练的条件生成神经网络，可在多种容积导体参数条件下生成运动单位动作电位波形。我们证明了此类模型能以高精度预测性地插值远少于数值模型输出的样本点。由此，计算负载得以显著降低，从而实现对真实动态自然运动过程中的肌电信号进行快速模拟。