Ma Shihan, Kenneth Clarke Alexander, Maksymenko Kostiantyn, Deslauriers-Gauthier Samuel, Sheng Xinjun, Zhu Xiangyang, Farina Dario
IEEE Trans Neural Netw Learn Syst. 2025 May;36(5):9224-9237. doi: 10.1109/TNNLS.2024.3438368. Epub 2025 May 2.
Numerical models of electromyography (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, while modern biophysical simulations based on finite element methods (FEMs) are highly accurate, they are extremely computationally expensive and thus are generally limited to modeling static systems such as isometrically contracting limbs. As a solution to this problem, we propose to use a conditional generative model 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 (MU) 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.
肌电图（EMG）信号的数值模型为我们对人类神经生理学的基本理解做出了巨大贡献，并且仍然是运动神经科学和人机接口发展的核心支柱。然而，尽管基于有限元方法（FEM）的现代生物物理模拟非常准确，但它们的计算成本极高，因此通常仅限于对等长收缩肢体等静态系统进行建模。作为解决这一问题的方法，我们建议使用条件生成模型来模拟高级数值模型的输出。为此，我们提出了BioMime，这是一种条件生成神经网络，通过对抗训练在各种容积导体参数下生成运动单元（MU）激活电位波形。我们证明了这种模型能够在数量少得多的数值模型输出之间进行高精度的预测性插值。因此，计算负担显著降低，这使得在真正动态和自然的运动过程中能够快速模拟EMG信号。
