EMG signal processing pipeline
Project description
EMG Signal Processing Pipeline
pyemgpipeline is an electromyography (EMG) signal processing pipeline package.
This package implements internationally accepted EMG processing conventions and provides a high-level interface for ensuring user adherence to those conventions, in terms of (1) processing parameter values, (2) processing steps, and (3) processing step order.
The processing steps included in the package are DC offset removal, bandpass filtering, full wave rectification, linear envelope, end frame cutting, amplitude normalization, and segmentation.
Scope
This package defines the processing pipeline for both surface EMG and intramuscular EMG but not for high density EMG. The EMG recording requires that the minimum sample rate be at least twice the highest cutoff frequency of the bandpass filter based on the Nyquist theorem.
Overview
In pyemgpipeline, class DataProcessingManager in module wrappers is
designed as the main wrapper for high-level, guided processing,
and users are encouraged to use it to adhere to accepted EMG processing conventions.
The other classes, methods, and functions are considered as lower level processing
options.
The package is organized in modules processors, wrappers, and plots.
Module processors includes the base class BaseProcessor of all signal
processors and seven classes for different processing steps:
DCOffsetRemover, BandpassFilter, FullWaveRectifier, LinearEnvelope,
EndFrameCutter, AmplitudeNormalizer, and Segmenter.
Module wrappers includes three wrapper classes to facilitate the signal
processing by integrating data and individual processors.
Class EMGMeasurement works for data of a single trial,
class EMGMeasurementCollection works for data of multiple trials,
and class DataProcessingManager is the high-level, guided processing wrapper
with EMG processing conventions.
Module plots includes
the function plot_emg to plot EMG signals on matplotlib figures
and the class EMGPlotParams to manage the plot-related parameters.
Documentation
The documentation describes how to use this package, including package installation, quick start, examples explaining the breadth of the package’s functionality, and API reference.
Community Guidelines
For contribution, please clone the repository, make changes, and create a pull request.
For reporting any issues, please use github issues.
For support, please contact the authors via their emails or github issues.
Citation
If you use this package in your project, please cite this work.
