What is EMGFlow?
EMGFlow is an open-source Python package for preprocessing and extracting features from surface electromyographic (sEMG) signals. It has been designed to facilitate the analysis of large datasets through batch processing of signal files, a common requirement in machine learning.
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Statement of need
Although several packages process physiological and neurological signals, support for sEMG has remained limited. Many lack a comprehensive feature set for sEMG, forcing researchers to use a patchwork of tools. Others focus on event detection with GUI-centric workflows that suit continuous recordings of a single participant, but complicate batch feature extraction common in machine learning.
EMGFlow, a portmanteau of EMG and Workflow, fills this gap by providing a flexible pipeline for extracting a wide range of sEMG features, with a scalable design suited for large datasets. An overview of package metadata is presented in Table 1.
| Metadata | Description |
|---|---|
| License | GPLv3 |
| Implementation | Python >= 3.9 |
| Code repository | https://github.com/WiIIson/EMGFlow-Python-Package |
| Documentation | https://wiiison.github.io/EMGFlow-Python-Package |
| PyPI installation | pip install EMGFlow |
Table 1. EMGFlow package metadata.
Use cases
Machine learning
Extracting features from large datasets is a common task in machine learning and quantitative domains. EMGFlow supports this need through batch-processing, allowing users to either semi- or fully automate the treatment of sEMG recordings.Experimental methods
EMGFlow is ideal for analysing data recorded from participants using standard experimental techniques (e.g., within/between/mixed designs). Researchers and clinicians without extensive knowledge of sEMG processing can analyse electromyographyic recordings with only a few lines of code. EMGFlow was designed with researchers in mind to streamline the process of cleaning and extraction of research-relevant features. Most functions in EMGFlow use common sense defaults, drawn from standard in the physiological and psychologcical literature, to generate reliable and reproducible results.
Community Guidelines
We welcome contributions to the project. Contributions are welcome via issues or pull requests. Suggestions for features, usage tips, and questions can also be raised through GitHub Discussions. You can also contact us over email.