# EEG-Pype: An accessible MNE-Python pipeline with graphical user interface for preprocessing and analysis of resting-state electroencephalography data

## TL;DR

EEG-Pype is a user-friendly software that simplifies EEG data preprocessing and analysis, making it accessible to researchers without programming skills.

## Contribution

EEG-Pype introduces a graphical interface for MNE-Python, enabling non-programmers to perform standardized EEG preprocessing and analysis.

## Key findings

- EEG-Pype streamlines preprocessing steps like filtering and artifact removal using an intuitive GUI.
- The software supports multiple EEG file formats and includes modules for spectral and connectivity analysis.
- EEG-Pype promotes reproducibility through configuration saving and detailed logging.

## Abstract

Processing of electroencephalography (EEG) data requires multiple steps to remove noise and artifacts and select good-quality data. While powerful open-source toolboxes like MNE-Python exist, their command-line nature can pose a barrier for researchers without programming experience. Here, we present EEG-Pype, an open-source (Apache-2.0 licensed) graphical user interface application using MNE-Python functions. EEG-Pype provides an intuitive workflow tailored for preprocessing of resting-state EEG data, including frequency band filtering, independent component analysis and atlas-based beamforming for source-level analysis. The application supports several common raw EEG input file formats and guides users through a comprehensive pipeline focused on manual bad channel and epoch selection. Manual steps are streamlined using MNE-Python’s interactive plots, resulting in a user-friendly experience. Configuration saving and loading allows for batch (re)runs, while a separate log is also saved, improving reproducibility and documentation. Output can be saved after filtering in canonical frequency bands, ready for further analysis. EEG-Pype includes a module for calculating quantitative EEG measures on preprocessed data, including spectral, functional connectivity and network analysis metrics. This software aims to lower the entry barrier for standardized EEG preprocessing, promoting reproducible research practices among neuroscientists and clinicians without requiring programming knowledge. EEG-Pype can be downloaded from: https://github.com/yorbenlodema/EEG-Pype and is not dependent on a specific operating system.

We developed EEG-Pype, a free and open-source software tool, to make the complex analysis of brain electrical activity (electroencephalography, or EEG) more accessible to the broader scientific community. When EEG is recorded, the raw data is inevitably contaminated by artifacts from sources like muscle activity or eye movement. The essential steps to clean the data before analysis, often called preprocessing, typically require significant programming expertise. This technical barrier can prevent researchers from directly processing their own data, creating a bottleneck in analysis. Our software addresses this challenge by offering an intuitive graphical user interface that provides a guided, end-to-end workflow for EEG preprocessing and analysis. It uses powerful, established computational libraries for filtering, artifact removal, and source analysis, all without requiring manual coding. We focused on resting-state EEG, a key modality for studying intrinsic brain networks. EEG-Pype also includes a module to compute advanced quantitative metrics, such as functional connectivity and network topology, directly from the processed data. By lowering the technical barrier to sophisticated EEG analysis, EEG-Pype facilitates more transparent, standardized, and reproducible neuroscience.