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EEG-Pype: An accessible MNE-Python pipeline with graphical user interface for preprocessing and analysis of
D Yorben Lodema1, Herman J van Dellen1, Willem de Haan2
1Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands.
Plos Computational Biology
|March 2, 2026
Summary
EEG-Pype is a new graphical user interface that simplifies electroencephalography (EEG) data preprocessing for researchers without programming skills. It streamlines artifact removal and analysis, promoting reproducible neuroscience research.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Data Science
Background:
- Electroencephalography (EEG) data processing involves complex steps for noise and artifact removal, often requiring programming expertise.
- Existing powerful toolboxes like MNE-Python are command-line based, creating a barrier for non-programmers.
- Standardized and reproducible EEG preprocessing is crucial for clinical and research applications.
Purpose of the Study:
- To introduce EEG-Pype, an open-source graphical user interface (GUI) application.
- To provide an intuitive workflow for preprocessing resting-state EEG data using MNE-Python functions.
- To lower the entry barrier for standardized EEG preprocessing, enhancing reproducibility.
Main Methods:
- Developed EEG-Pype as an Apache-2.0 licensed GUI application.
- Integrated MNE-Python functions for EEG preprocessing tasks.
- Included features for frequency band filtering, independent component analysis, and atlas-based beamforming.
- Supported common raw EEG input formats and guided users through manual bad channel/epoch selection.
- Streamlined manual steps using MNE-Python's interactive plots.
- Enabled configuration saving/loading for batch processing and logging for reproducibility.
- Added a module for quantitative EEG (qEEG) measures (spectral, functional connectivity, network analysis).
Main Results:
- EEG-Pype offers a user-friendly interface for complex EEG data preprocessing.
- The application facilitates standardized preprocessing pipelines, including source-level analysis.
- Manual data selection is simplified through interactive visualizations.
- Batch processing and logging features enhance reproducibility and documentation.
- Quantitative EEG measures can be calculated on preprocessed data.
Conclusions:
- EEG-Pype effectively lowers the barrier to entry for EEG data analysis.
- The GUI promotes standardized and reproducible research practices in neuroscience and clinical settings.
- It empowers researchers without programming knowledge to perform advanced EEG preprocessing and analysis.
