For documentation, see the:

• stable documentation
• latest (development) documentation

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.

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.

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 .

If you use this software in academic work, please cite it using the Zenodo entry. Please also consider citing the original publication on PREP (see "References" below). Metadata is encoded in the CITATION.cff file.

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