MEG and EEG data analysis with MNE-Python

Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statisti...

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Main Authors:	Gramfort, Alexandre, Luessi, Martin, Larson, Eric, Engemann, Denis A., Strohmeier, Daniel, Brodbeck, Christian, Goj, Roman, Jas, Mainak, Brooks, Teon, Parkkonen, Lauri, Hämäläinen, Matti
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3872725/

id	pubmed-3872725
recordtype	oai_dc
spelling	pubmed-38727252014-01-15 MEG and EEG data analysis with MNE-Python Gramfort, Alexandre Luessi, Martin Larson, Eric Engemann, Denis A. Strohmeier, Daniel Brodbeck, Christian Goj, Roman Jas, Mainak Brooks, Teon Parkkonen, Lauri Hämäläinen, Matti Neuroscience Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne. Frontiers Media S.A. 2013-12-26 /pmc/articles/PMC3872725/ /pubmed/24431986 http://dx.doi.org/10.3389/fnins.2013.00267 Text en Copyright © 2013 Gramfort, Luessi, Larson, Engemann, Strohmeier, Brodbeck, Goj, Jas, Brooks, Parkkonen and Hämäläinen. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Gramfort, Alexandre Luessi, Martin Larson, Eric Engemann, Denis A. Strohmeier, Daniel Brodbeck, Christian Goj, Roman Jas, Mainak Brooks, Teon Parkkonen, Lauri Hämäläinen, Matti
spellingShingle	Gramfort, Alexandre Luessi, Martin Larson, Eric Engemann, Denis A. Strohmeier, Daniel Brodbeck, Christian Goj, Roman Jas, Mainak Brooks, Teon Parkkonen, Lauri Hämäläinen, Matti MEG and EEG data analysis with MNE-Python
author_facet	Gramfort, Alexandre Luessi, Martin Larson, Eric Engemann, Denis A. Strohmeier, Daniel Brodbeck, Christian Goj, Roman Jas, Mainak Brooks, Teon Parkkonen, Lauri Hämäläinen, Matti
author_sort	Gramfort, Alexandre
title	MEG and EEG data analysis with MNE-Python
title_short	MEG and EEG data analysis with MNE-Python
title_full	MEG and EEG data analysis with MNE-Python
title_fullStr	MEG and EEG data analysis with MNE-Python
title_full_unstemmed	MEG and EEG data analysis with MNE-Python
title_sort	meg and eeg data analysis with mne-python
description	Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.
publisher	Frontiers Media S.A.
publishDate	2013
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3872725/
_version_	1612041441740062720

MEG and EEG data analysis with MNE-Python

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