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

Abstract

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. 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. 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. 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. 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.

Heart-brain coupling and its relevance for individual trait characteristics

“…MEG data was downsampled resulting in a sampling rate of 500 Hz. Thereafter MEG data was exported to Meggie, an MNE-python-based graphical user interface (Heinilä and Parviainen, 2022), where independent component analysis (ICA) was used to extract, visually inspect, and manually remove common artifacts of cardiac and ocular origin. Since gradiometers are optimal for recording data from superficial sources and they have a narrow spatial sensitivity pattern, the subsequent analyses were conducted using only planar gradiometers.…”

Spectral Changes of EEG Following a 6-Week Low-Dose Oral Ketamine Treatment in Adults With Major Depressive Disorder and Chronic Suicidality

“…An in-house EEG data processing pipeline, EEG-pyline, was used for signal preprocessing and spectral analysis efforts ( Anijärv, 2022 ). The specific code (i.e., Jupyter notebook) used for this study can be found in the ‘studies’ folder within the GitHub repository.…”

Spectral signatures of cross-modal attentional control in the adolescent brain and their link with physical activity and aerobic fitness levels

“…Possible head movements occurring during the measurement with respect to the initial head position were also corrected using Maxfilter™ 2.2 software. The rest of the pre-processing was performed with Meggie (CIBR, Jyväskylä, Finland; Heinilä & Parviainen, 2022), a graphical user interface for MNE-Python (Gramfort et al, 2014). With the use of this software, epochs contaminated with eye movements (as measured with EOG) or cardiac artifacts (measured by an MEG channel) were removed from the analysis.…”