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Neuroimage. 2014 Feb 1;86:446-60. doi: 10.1016/j.neuroimage.2013.10.027. Epub 2013 Oct 24.

MNE software for processing MEG and EEG data.

Author information

1
Institut Mines-Telecom, Telecom ParisTech, CNRS LTCI, 37-39 Rue Dareau, 75014 Paris, France; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Charlestown, MA, USA; Institut Mines-Telecom, Telecom ParisTech, CNRS LTCI, Paris, France; NeuroSpin, CEA Saclay, Bat. 145, 91191 Gif-sur-Yvette Cedex, France. Electronic address: alexandre.gramfort@telecom-paristech.fr.
2
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Charlestown, MA, USA.
3
University of Washington, Institute for Learning and Brain Sciences, Seattle, WA, USA.
4
Institute of Neuroscience and Medicine - Cognitive Neuroscience (INM-3), Forschungszentrum Juelich, Germany; Brain Imaging Lab, Department of Psychiatry, University Hospital of Cologne, Germany.
5
Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany.
6
Department of Psychology, New York University, New York, NY, USA.
7
Department of Biomedical Engineering and Computational Science, Aalto University School of Science, Espoo, Finland; Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University School of Science, Espoo, Finland.

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.

KEYWORDS:

Connectivity; Electroencephalography (EEG); Inverse problem; Magnetoencephalography (MEG); Non-parametric statistics; Software; Time–frequency analysis

PMID:
24161808
PMCID:
PMC3930851
DOI:
10.1016/j.neuroimage.2013.10.027
[Indexed for MEDLINE]
Free PMC Article

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