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Related Experiment Video

Updated: May 1, 2026

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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
PubMed
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.

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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.