emg-toolbox 1.1.0

EMG Toolbox is a Python toolkit for processing and analysing surface electromyography (sEMG) data. It includes a variety of feature extraction methods, signal filtering, and plotting functions, helping users efficiently preprocess and analyse EMG signals.

EMG Toolbox

EMG Toolbox is a Python toolkit for processing and analysing surface electromyography (sEMG) data. It includes a variety of feature extraction methods, signal filtering, and plotting functions, helping users efficiently preprocess and analyse EMG signals.

Features

• Various EMG feature extraction methods: Root Mean Square (RMS), Mean Absolute Value (MAV), Zero Crossing (ZC), Waveform Length (WL), Slope Sign Change (SSC), etc.
• Signal filtering: Supports Butterworth filter and powerline interference filter.
• Visualisation tools: Includes plotting functions for time-domain signals, frequency spectra, and power spectral density.
• Supports sliding window processing and multithreaded computation to improve feature extraction efficiency.

Installation

Install EMG Toolbox using pip:

pip install emg-toolbox

Quick Start

Below is an example of how to use EMG Toolbox for basic EMG signal processing and feature extraction:

1. Import Modules

import numpy as np
from emg_toolbox.features import sliding_window, fRMS, fMAV, fZC
from emg_toolbox.plots import plot_time_domain_signal

2. Generate Example Data

# Create random sEMG signal example
data = np.random.randn(1000)
fs = 1000  # Sampling frequency 1000 Hz

3. Sliding Window Feature Extraction

# Calculate RMS feature using sliding window
window_size = 200
step_size = 100
rms_values = sliding_window(data, window_size, step_size, fRMS)
print(f"RMS (Sliding Window): {rms_values}")

4. Plot Time-Domain Signal

# Plot time-domain signal
plot_time_domain_signal(data, fs)

Module Overview

1. features.py - Feature Extraction

features.py provides a series of functions for extracting features from EMG signals, such as:

• fRMS(data): Calculates the root mean square.
• fMAV(data): Calculates the mean absolute value.
• fZC(data, dead_zone): Calculates zero crossings.
• Other features include median frequency, mean power frequency, spectral entropy, etc.

The sliding_window function can be used to segment the signal and calculate features.

2. plots.py - Plotting Tools

plots.py offers several plotting tools for visualising EMG signals:

• plot_time_domain_signal(raw, fs): Plots the time-domain signal.
• plot_frequency_spectrum(raw, fs): Plots the frequency spectrum.
• plot_power_spectral_density(raw, fs): Plots the power spectral density.

3. filters.py - Filters

filters.py includes a powerline interference filter (PlifEegSubtractFilter) to reduce powerline interference in EMG signals.

Contribution

If you would like to contribute to EMG Toolbox, please fork this repository and submit a pull request. Bug reports and feature suggestions are welcome!

License

GPL License

Contact

If you have any questions or suggestions, please contact me:

• Email: [e-mail]