How to prepare before running your first preprocessing.
Start your endeavor here!
If you are interested in the details of the pEYEpline, this section is for you.
Answers to frequently asked questions.
Solutions to common warnings and errors.
The pEYEpline for the MultiplEYE corpus by Jakobi et al. [JSSH+25]. The pEYEpline is designed to process the raw eye-tracking data and psychometric test data collected in the MultiplEYE project, transforming it into a standardized format suitable for analysis and sharing with the research community.
The pEYEpline is built in Python and its core functionalities rely on the pymovements library,
which provides tools for processing eye-tracking data.
See pymovements website.
What you can do#
Run the pEYEpline: Process raw eye-tracking data (EyeLink
.edffiles) and score psychometric tests in one workflow.Score psychometric tests standalone: Re-score tests without re-running the full pEYEpline.
run_preprocessing # run the full pEYEpline
preprocess_psychometric_tests # score psychometric tests only
Quick start#
git clone https://github.com/MultiplEYE-COST/multipleye-preprocessing.git
cd multipleye-preprocessing/
uv sync
source .venv/bin/activate
Run the pEYEpline:
run_preprocessingUpdate settings in
multipleye_settings_preprocessing.yamlRerun the pEYEpline:
run_preprocessing
Setup and use#
To use the pEYEpline, please follow the instructions in the Getting Started section. This section will guide you through the setup, including how to install dependencies and run the pEYEpline on your data collection.
How to cite#
If you use the pEYEpline, or parts of it in your research, please cite it as specified in {cite:t}
Jakobi2026MultiplEYEPreprocessing. You can also find citation information for this project in the
CITATION.cff
file in the repository and cite it accordingly.
Acknowledgments#
This project has been partially funded by:
MultiplEYE COST Action, CA21131
Swiss National Science Foundation (SNSF), 212276 (MeRID)
swissuniversities, OpenEye