Emgforge simulates muscle electrical signals from MRI images
emgforge: an automated end-to-end pipeline for simulating surface EMG on MRI-based volume conductors
Computational Engineering, Finance, and Science
Summary
Simulating electrical signals from muscles helps us understand what electrodes record and improve muscle signal analysis. The authors created emgforge, a tool that turns MRI images of limbs into detailed muscle electrical signal simulations using several physical and biological steps. They tested each part carefully to ensure accuracy and showed how it can be used to study electrode placement, muscle shape effects, and interference in signals. The software and example data are openly shared for others to use.
What this means in practice
- •For biomedical engineers: Generate realistic muscle electrical signals from MRI scans to validate and develop signal processing and decoding methods for EMG-based technologies.
- •For rehabilitation device developers: Create individualized EMG signal models to design and optimize wearable muscle sensors and control systems for prosthetics or exoskeletons.$Commercial implications: Enables personalized EMG-based control products by simulating signals on anatomically accurate limb models.
Authors
Dimitrios Halatsis, Noura Ezaz-Nikpay, Pranav Mamidanna, Dario Farina
Abstract
Simulated electromyograms are used to understand what an electrode records, to test decomposition and estimation algorithms on signals with known ground truth, and to train learning-based decoders. Most simulators fix the geometry to a cylinder or a slab, or stop at the lead field and leave the rest to the user. We present emgforge, an open pipeline that takes a labelled MRI segmentation of a limb to surface EMG in one command: a tetrahedral mesh with conductivity tensors aligned with each muscle's fibres; one reciprocal finite-element solve per electrode, valid for every fibre of every muscle; fibre beds that are straight or follow the muscle's shape; a motor-unit pool obeying the size principle; motor-unit action potentials on any electrode layout; and a motoneuron-pool and twitch layer that turns a drive or a movement into interference EMG and force. We describe the pipeline stage by stage, and at each stage we show, with an example, why the choice was made. The step that turns a lead field into a single-fibre action potential -- direct line-source synthesis -- is checked against a closed-form solution ($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.