Meggie – easy-to-use graphical user interface for M/EEG analysis based on MNE-python

Background In the last decades, electrophysiological imaging methodology has seen many advances and the computational power in the neuroscience laboratories has steadily increased. Still, the new methodologies are unavailable for many. There is a need for more versatile analysis approaches for neuroscience specialists without a programming background. New method Using a software which provides standard pipelines, provides good default values for parameters, has a good multi-subject support, and stores the used analysis steps with the parameters in one place for reporting, is efficient and fast. In addition to enabling analysis for people without background in programming, it enables analysis for people with background in programming but a limited background in neuroscience. When constructed with care, the GUI may guide the researcher to apply analysis steps in correct order with reasonable default parameters. Comparison to existing methods Two existing software, EEGLAB and Brainstorm, both provide an easy-to-use graphical user interface for end-to-end analysis for multiple subjects. The key difference to work presented here is the choice of language. The scientific community is moving en masse towards the python programming language, thus making it an ideal platform for extendable software. Another problem with Matlab is that it is not free - both from the perspective of open source and concrete monetary resources. Within the current trend towards increasing open science, covering data, analysis and reporting, the need for open source software is imperative. Results Meggie is an open source software for running MEG and EEG analysis with easy-to-use graphical user interface. It is written in Python 3, runs on Linux, macOS and Windows, and uses the MNE-python library under the hood to do heavy lifting. It is designed to allow end-to-end analysis of MEG and EEG datasets from multiple subjects with common sensor-level analysis steps such as preprocessing, epoching and averaging, spectral analysis and time-frequency analysis. Most of the analysis steps can be run for all the subjects in one go, and combining the results across subjects is made possible with grand averages. We have emphasized the extendibility of Meggie by implementing most of the Meggie itself as plugins, thus ensuring that new plugins have access to all necessary core features. Conclusion Meggie answers the demand for easy-to-use and extendable python-based graphical user interface that provides an end-to-end analysis environment for M/EEG data analysis. It is freely available at https://github.com/cibr-jyu/meggie under the BSD license. Installation instructions, documentation and tutorials are found on that website. Highlights - MNE-python-based graphical user interface for M/EEG analysis - Easy to use even without programming background - Has good support for multiple subjects - Written in python, and can be easily extended with plugins - Free and open source with BSD license