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MEG/EEG group study with MNE: recommendations, quality assessments and best practices

Mainak Jas, View ORCID ProfileEric Larson, View ORCID ProfileDenis Engemann, Jaakko Leppäkangas, Samu Taulu, Matti Hämäläinen, View ORCID ProfileAlexandre Gramfort
doi: https://doi.org/10.1101/240044
Mainak Jas
1Télécom ParisTech, Université Paris-Saclay, France
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Eric Larson
2University of Washington, Institute for Learning and Brain Sciences, Seattle WA, USA
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Denis Engemann
3NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
4INRIA, Université Paris-Saclay, Saclay, France
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Jaakko Leppäkangas
1Télécom ParisTech, Université Paris-Saclay, France
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Samu Taulu
2University of Washington, Institute for Learning and Brain Sciences, Seattle WA, USA
5University of Washington, Department of Physics, Seattle WA, USA
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Matti Hämäläinen
6Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and Harvard Medical School, Charlestown MA, USA
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Alexandre Gramfort
1Télécom ParisTech, Université Paris-Saclay, France
3NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
4INRIA, Université Paris-Saclay, Saclay, France
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Abstract

Cognitive neuroscience questions are commonly tested with experiments that involve a cohort of subjects. The cohort can consist of a handful of subjects for small studies to hundreds or thousands of subjects in open datasets.

While there exist various online resources to get started with the analysis of magnetoencephalography (MEG) or electroencephalography (EEG) data, such educational materials are usually restricted to the analysis of a single subject. This is in part because data from larger group studies are harder to share, but also analyses of such data are often require subject-specific decisions which are hard to document.

This work presents the results obtained by the reanalysis of an open dataset from Wakeman and Henson (2015) using the MNE software package. The analysis covers preprocessing steps, quality assurance steps, sensor space analysis of evoked responses, source localization, and statistics in both sensor and source space. Results with possible alternative strategies are presented and discussed at different stages such as the use of high-pass filtering versus baseline correction, tSSS versus SSS, the use of a minimum norm inverse versus LCMV beamformer, and the use of univariate or multivariate statistics. This aims to provide a comparative study of different stages of M/EEG analysis pipeline on the same dataset, with open access to all of the scripts necessary to reproduce this analysis.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted December 28, 2017.
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MEG/EEG group study with MNE: recommendations, quality assessments and best practices
Mainak Jas, Eric Larson, Denis Engemann, Jaakko Leppäkangas, Samu Taulu, Matti Hämäläinen, Alexandre Gramfort
bioRxiv 240044; doi: https://doi.org/10.1101/240044
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MEG/EEG group study with MNE: recommendations, quality assessments and best practices
Mainak Jas, Eric Larson, Denis Engemann, Jaakko Leppäkangas, Samu Taulu, Matti Hämäläinen, Alexandre Gramfort
bioRxiv 240044; doi: https://doi.org/10.1101/240044

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