emgforge：基于MRI体导体的表面肌电模拟自动化端到端流程
emgforge: an automated end-to-end pipeline for simulating surface EMG on MRI-based volume conductors
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中文总结 AI 辅助
emgforge是一个自动化端到端流程，从MRI分割模拟表面肌电，通过互易有限元求解和直接线源合成生成动作电位，经严格验证，支持多场景研究。
中文摘要 AI 辅助
模拟肌电图用于理解电极记录的内容、在具有已知真实值的信号上测试分解和估计算法，以及训练基于学习的解码器。大多数模拟器将几何形状固定为圆柱体或平板，或者止步于铅场并将其余部分留给用户。我们提出了emgforge，一个开放流程，通过一条命令将带标签的肢体MRI分割转换为表面肌电：一个具有与每块肌肉纤维对齐的电导率张量的四面体网格；每个电极进行一次互易有限元求解，适用于每块肌肉的每条纤维；纤维束可以是直的或遵循肌肉形状；遵循大小原则的运动单位池；任何电极布局上的运动单位动作电位；以及一个运动神经元池和颤搐层，将驱动或运动转换为干扰肌电和力。我们逐步描述该流程，并在每个阶段通过示例说明选择的原因。将铅场转换为单纤维动作电位的步骤——直接线源合成——已与闭式解进行验证（r = 1.0000，零滞后），整个链通过五十项具有生理学文献数值标准的检查。然后我们使用该流程进行四项研究：电极作为深度、脂肪、间距和导联组合函数的所见；纤维几何是否改变信号；覆盖一块肌肉的网格从其邻居接收多少串扰；以及干扰肌电如何随驱动缩放。代码、验证套件和三个具有真实值的数据集已发布。
英文摘要
Simulated electromyograms are used to understand what an electrode records, to test decomposition and estimation algorithms on signals with known ground truth, and to train learning-based decoders. Most simulators fix the geometry to a cylinder or a slab, or stop at the lead field and leave the rest to the user. We present emgforge, an open pipeline that takes a labelled MRI segmentation of a limb to surface EMG in one command: a tetrahedral mesh with conductivity tensors aligned with each muscle's fibres; one reciprocal finite-element solve per electrode, valid for every fibre of every muscle; fibre beds that are straight or follow the muscle's shape; a motor-unit pool obeying the size principle; motor-unit action potentials on any electrode layout; and a motoneuron-pool and twitch layer that turns a drive or a movement into interference EMG and force. We describe the pipeline stage by stage, and at each stage we show, with an example, why the choice was made. The step that turns a lead field into a single-fibre action potential -- direct line-source synthesis -- is checked against a closed-form solution ($r = 1.0000$, zero lag), and the whole chain against fifty checks with numeric criteria from the physiological literature. We then use the pipeline for four studies: what an electrode sees as a function of depth, fat, spacing and montage; whether fibre geometry changes the signal; how much crosstalk a grid over one muscle receives from its neighbours; and how interference EMG scales with drive. The code, the validation suite and three datasets with ground truth are released.
发表机构
- Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。
