MNE software for processing MEG and EEG data
Alexandre Gramforta b c d e f✉, Martin Luessib c, Eric Larsong, Denis Alexander Engemannh i j, Daniel Strohmeierk, Christian J. Brodbeckl, Lauri T. Parkkonenm n, Matti Hämäläinenb c
- aInstitut Mines-Telecom, Telecom ParisTech, CNRS LTCI, 37-39 Rue Dareau, 75014 Paris, France
- bAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- cHarvard Medical School, Charlestown, MA, USA
- dInstitut Mines-Telecom, Telecom ParisTech, CNRS LTCI, Paris, France
- eNeuroSpin, CEA Saclay, Bat. 145, 91191 Gif-sur-Yvette Cedex, France. Electronic address: alexandre.gramfort@telecom-paristech.fr
- fNeuroSpin, CEA Saclay, Bat. 145, 91191 Gif-sur-Yvette Cedex, France
- gUniversity of Washington, Institute for Learning and Brain Sciences, Seattle, WA, USA
- hInstitute of Neuroscience and Medicine - Cognitive Neuroscience (INM-3), Forschungszentrum Juelich, Germany
- iBrain Imaging Lab, Department of Psychiatry, University Hospital of Cologne, Germany
- jInstitute of Neuroscience and Medicine — Cognitive Neuroscience (INM-3), Forschungszentrum Juelich, Germany
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Abstract
Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals originating from neural currents in the brain. Using these signals to characterize and locate brain activity is a challenging task, as evidenced by several decades of methodological contributions. MNE, whose name stems from its capability to compute cortically-constrained minimum-norm current estimates from M/EEG data, is a software package that provides comprehensive analysis tools and workflows including preprocessing, source estimation, time–frequency analysis, statistical analysis, and several methods to estimate functional connectivity between distributed brain regions. The present paper gives detailed information about the MNE package and describes typical use cases while also warning about potential caveats in analysis. The MNE package is a collaborative effort of multiple institutes striving to implement and share best methods and to facilitate distribution of analysis pipelines to advance reproducibility of research. Full documentation is available at http://martinos.org/mne.
TL;DRDetailed information about the MNE package is given and typical use cases are described while also warning about potential caveats in analysis. (Semantic Scholar)

