MNE-Python specifications


Unique identifier OMICS_14795
Name MNE-Python
Software type Application/Script, Package/Module
Interface Command line interface, Graphical user interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages Python
License BSD 3-clause “New” or “Revised” License
Computer skills Medium
Version 0.15
Stability Stable
Maintained Yes


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  • person_outline Alexandre Gramfortl <>

Publications for MNE-Python

MNE-Python in publications

PMCID: 5864515
PMID: 28893608
DOI: 10.1016/j.neuroimage.2017.09.009

[…] computation of the noise covariance matrices). mri preprocessing was performed in the freesurfer software (version 5.3; and eeg/meg analyses were performed in the mne python software package (version 0.9) the ica analysis was performed using fastica algorithm () as included in scikit-learn python package () […]

PMCID: 5806592
PMID: 29435487
DOI: 10.1523/ENEURO.0441-17.2018

[…] matrix and stimulus-response cross-correlation were both calculated via their fourier counterparts using frequency-domain multiplication. these specific methods have been incorporated into the mne-python package (; rrid:scr_005972). the stimulus regressors were sufficiently broadband such that no regularization was necessary, so none was used (had there been near-zeros in their amplitude […]

PMCID: 5751446
PMID: 29258189
DOI: 10.3390/s17122926

[…] and produces a cleaner version of the meg data by backprojecting the internal components exclusively. , sss is a popular technique for denoising meg data. it was recently made publicly available in mne python for application to all whole-head meg systems []. however, the use of sss is particularly widespread in modern elekta neuromag®, helsinki, finland (vectorview and triux) meg systems, since […]

PMCID: 5723294
PMID: 29259561
DOI: 10.3389/fpsyt.2017.00274

[…] from that summed f value of the original dataset according to the proportion of the randomization distribution (h0 distribution) (detailed in figure ). we used a “spatio_temporal_cluster_test” in mne-python () for the cluster-based non-parametric randomization test of the nirs data., we also performed a correlation analysis between clinical measurements including aq, caars, and vft […]

PMCID: 5461867
PMID: 28570739
DOI: 10.1167/17.6.1

[…] meg data were also recorded in the same session, and used to estimate the covariance matrix of the sensor noise., the meg data in the face category learning experiment were preprocessed using mne/mne-python (gramfort et al., , ) in the following steps. (a) the raw data were filtered with a 1–110 hz bandpass filter, and then with a notch filter at 60 hz to reduce the power-line interference. […]

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MNE-Python institution(s)
Télécom ParisTech, Université Paris-Saclay, France; University of Washington, Institute for Learning and Brain Sciences, Seattle WA, USA; NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France; INRIA, Université Paris-Saclay, Saclay, France; University of Washington, Department of Physics, Seattle, WA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and Harvard Medical School, Charlestown, MA, USA
MNE-Python funding source(s)
Supported by National Institute of Biomedical Imaging and Bioengineering grants 5R01EB009048, NIH R01 MH106174 and P41RR014075, National Institute on Deafness and Other Communication Disorders fellowship F32DC012456, NSF awards 0958669 and 1042134, the French National Research Agency (ANR-14-NEUC-0002-01) and the European Research Council Starting Grant SLAB ERC-YStG-676943.

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