For documentation, see the:
pyprep is a Python implementation of the
Preprocessing Pipeline (PREP)
for EEG data, working with MNE-Python.
pyprep runs on Python version 3.10 or higher.
We recommend to run pyprep in a dedicated virtual environment
(for example using conda).
For installing the stable version of pyprep, call:
python -m pip install --upgrade pyprep
or if you use conda:
conda install --channel conda-forge pyprep
For installing the latest (development) version of pyprep, call:
python -m pip install --upgrade https://github.com/sappelhoff/pyprep/archive/refs/heads/main.zip
Both the stable and the latest installation will additionally install
all required dependencies automatically.
The dependencies are defined in the pyproject.toml file under the
dependencies and project.optional-dependencies sections.
pyprep logs through the standard logging module and configures nothing
when it is imported, so it is quiet by default but never silent: warnings and
errors reach stderr without any setup.
Call pyprep.setup_logging("info") to see what the pipeline decided, or
pyprep.set_log_level("info") if your application already routes logging
somewhere of its own.
See the Logging section of the API documentation for the details.
The development of pyprep is taking place on
GitHub.
For more information, please see CONTRIBUTING.md.
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.
- 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