MNE-Python

MNE is a software package for processing electrophysiological signals primarily from magnetoencephalographic (MEG) and electroencephalographic (EEG) recordings, and more recently sEEG, ECoG and fNIRS. It provides a comprehensive solution for data preprocessing, forward modeling (with boundary element models), distributed source imaging, time–frequency analysis, non-parametric multivariate statistics, multivariate pattern analysis, and connectivity estimation. Importantly, this package allows all of these analyses to be applied in both sensor or source space. MNE is developed by an international team, with particular care for computational efficiency, code quality, and readability, as well as the common goal of facilitating reproducibility in neuroscience.

Project Author(s)

Alexandre Gramfort;Eric Larson;Denis Engemann;Daniel Strohmeier;Christian Brodbeck;Roman Goj;Mainak Jas;Teon Brooks;Lauri Parkkonen;Matti Hämäläinen;Jaakko Leppakangas;Jona Sassenhagen;Jean-Rémi King;Daniel McCloy;Marijn van Vliet;Clemens Brunner;Chris Holdgraf;Martin Luessi;Joan Massich;Guillaume Favelier;Andrew R. Dykstra;Mikolaj Magnuski;Stefan Appelhoff;Britta Westner;Richard Höchenberger;Robert Luke;Luke Bloy;Thomas Hartmann;Olaf Hauk;Adam Li

https://mne.tools


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