• MNE-Python-based graphical user interface for M/EEG analysis. • Easy to use even without programming background. • Good support for analysis of multiple subjects. • Written in Python, and can be easily extended with plugins. • Free and open source with BSD license. MNE-Python-based graphical user interface for M/EEG analysis. Easy to use even without programming background. Good support for analysis of multiple subjects. Written in Python, and can be easily extended with plugins. Free and open source with BSD license. In the last decades, electrophysiological imaging has made significant advances, yet many of these new methodologies remain inaccessible to neuroscience researchers without programming skills. Meggie, an open-source software, bridges this gap. It provides standard pipelines and sensible default parameters for multi-subject MEG and EEG analysis, allowing efficient and reproducible research even for those with limited programming knowledge. In addition to existing platforms like EEGLAB and Brainstorm, which are based on MATLAB, and other Python-based GUIs like MNELAB, Meggie is developed in Python, catering to the community's trend towards this open-source language. It uses MNE-Python for processing, offering functionalities such as preprocessing, epoching, averaging, spectral analysis, and time-frequency analysis, which can be executed for multiple subjects simultaneously. Meggie thus meets the demand for an easy-to-use, extendable Python-based GUI for end-to-end M/EEG data analysis. Available under the BSD license, Meggie supports the open science ecosystem, with comprehensive documentation and tutorials hosted at https://cibr-jyu.github.io/meggie .
Heinilä et al. (Sat,) studied this question.