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A Python implementation of the preprocessing pipeline (PREP) for EEG data.

Project description

Python build Python tests codecov Documentation Status PyPI version Conda version

pyprep

For documentation, see the:

pyprep is a Python implementation of the Preprocessing Pipeline (PREP) for EEG data, working with MNE-Python.

ALPHA SOFTWARE. This package is currently in its early stages of iteration. It may change both its internals or its user-facing API in the near future. Any feedback and ideas on how to improve either of these is welcome! Use this software at your own risk.

Installation

pyprep requires Python version 3.7 or higher to run properly. We recommend to run pyprep in a dedicated virtual environment (for example using conda).

For installing the stable version of pyprep, call:

pip install pyprep

or, as an alternative to pip, call:

conda install -c conda-forge pyprep

For installing the latest (development) version of pyprep, call:

pip install git+https://github.com/sappelhoff/pyprep.git@master

Both the stable and the latest installation will additionally install all required dependencies automatically. The dependencies are defined in the setup.cfg file under the options.install_requires section.

Contributions

We are actively looking for contributors!

Please chime in with your ideas on how to improve this software by opening a GitHub issue, or submitting a pull request.

See also our CONTRIBUTING.md file for help with submitting a pull request.

Potential contributors should install pyprep in the following way:

  1. First they should fork pyprep to their own GitHub account.

  2. Then they should run the following commands, adequately replacing <gh-username> with their GitHub username.

git clone https://github.com/<gh-username>/pyprep
cd pyprep
pip install -r requirements-dev.txt
pre-commit install
pip install -e .

References

  1. Bigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K.-M., & Robbins, K. A. (2015). The PREP pipeline: standardized preprocessing for large-scale EEG analysis. Frontiers in Neuroinformatics, 9, 16. doi: 10.3389/fninf.2015.00016

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