EMG Toolbox

Overview

EMG Toolbox is a Python package to preprocess, analyse and plot electromyography (EMG) signals, including high-density recordings from electrode grids.

The package is composed of the following modules:

• tools: rearrange channels between (samples, channels) and grid (rows, cols, samples) formats, and replace bad channels with the mean of their neighbours.
• prepro: Butterworth bandpass, highpass, lowpass and powerline (bandstop) filters.
• feats: amplitude features such as the moving-window root mean square (RMS).
• freq: amplitude spectrum and power spectral density (periodogram or Welch).
• plots: plot channels over time, PSDs, PSD maps across channels, and spectrograms.

Table of Contents

• Installation
• Quick start
• Documentation
• Contributing
• License
• Citation
• Contact

Installation

Install the latest release from PyPI (Python 3.10 or newer):

pip install emg-tools

The package is imported as emg_tools.

To work on the code itself, see the development setup in CONTRIBUTING.md.

Quick start

EMG data is an array of shape (samples, channels). The example below filters a synthetic 16-channel recording and computes its RMS envelope:

import numpy as np

from emg_tools import feats, prepro

fs = 2048                                   # sampling frequency (Hz)
timestamps = np.arange(5 * fs) / fs         # 5 s recording
rng = np.random.default_rng(0)
emg = rng.standard_normal((timestamps.size, 16))                # (samples, channels)
emg += 0.5 * np.sin(2 * np.pi * 50 * timestamps)[:, None]       # powerline noise

emg_filt = prepro.remove_powerline(emg, fs=fs, cutoff=50)
emg_filt = prepro.bandpass_filter(emg_filt, fs=fs, cutoff=[20, 500])

rms, t_win = feats.compute_rms(emg_filt, timestamps, win_len_s=0.25, win_step_s=0.125, fs=fs)
rms.shape                                   # (10240, 16): one RMS value per sample and channel

Documentation

The full documentation, with a worked example and the API reference, is at imendezguerra.github.io/emg-tools.

Contributing

Contributions are welcome! CONTRIBUTING.md explains how to set up a development environment, run the tests, preview the docs, and what the automated checks (pre-commit and CI) do.

License

This project is licensed under the MIT License.

Citation

If you use this code in your research, please cite this repository:

@software{Mendez_Guerra_EMG_Toolbox,
author = {Mendez Guerra, Irene},
title = {{EMG Toolbox}},
url = {https://github.com/imendezguerra/emg-tools},
version = {2.0.0}
}

Contact

For any questions or inquiries, please contact us at:

Irene Mendez Guerra
irene.mendez17@imperial.ac.uk