%0 Journal Article %1 dataAnalysisMNEPython %A Gramfort, Alexandre %A Luessi, Martin %A Larson, Eric %A Engemann, Denis %A Strohmeier, Daniel %A Brodbeck, Christian %A Goj, Roman %A Jas, Mainak %A Brooks, Teon %A Parkkonen, Lauri %A Hämäläinen, Matti %D 2013 %J Frontiers in Neuroscience %K magnetoencephalography %P 267 %R 10.3389/fnins.2013.00267 %T MEG and EEG data analysis with MNE-Python %U http://journal.frontiersin.org/article/10.3389/fnins.2013.00267 %V 7 %X Magnetoencephalography and electroencephalography (M/EEG) measure the weakelectromagnetic signals generated by neuronal activity in the brain. Using thesesignals to characterize and locate neural activation in the brain is achallenge that requires expertise in physics, signalprocessing, statistics, and numerical methods. As part of the MNE softwaresuite, MNE-Python is an open-sourcesoftware package that addresses this challenge by providingstate-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation offunctional 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 analysispipelines by writing Python scripts.Moreover, MNE-Python is tightly integrated with the core Python libraries for scientificcomptutation (Numpy, Scipy) and visualization (matplotlib and Mayavi), as wellas the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD licenseallowing code reuse, even in commercial products. Although MNE-Python has onlybeen under heavy development for a couple of years, it has rapidly evolved withexpanded 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 ofmethods by other researchers. Full documentation, including dozens ofexamples, is available at http://martinos.org/mne.

@article{dataAnalysisMNEPython, abstract = {Magnetoencephalography and electroencephalography (M/EEG) measure the weakelectromagnetic signals generated by neuronal activity in the brain. Using thesesignals to characterize and locate neural activation in the brain is achallenge that requires expertise in physics, signalprocessing, statistics, and numerical methods. As part of the MNE softwaresuite, MNE-Python is an open-sourcesoftware package that addresses this challenge by providingstate-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation offunctional 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 analysispipelines by writing Python scripts.Moreover, MNE-Python is tightly integrated with the core Python libraries for scientificcomptutation (Numpy, Scipy) and visualization (matplotlib and Mayavi), as wellas the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD licenseallowing code reuse, even in commercial products. Although MNE-Python has onlybeen under heavy development for a couple of years, it has rapidly evolved withexpanded 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 ofmethods by other researchers. Full documentation, including dozens ofexamples, is available at http://martinos.org/mne.}, added-at = {2017-04-19T15:35:00.000+0200}, author = {Gramfort, Alexandre and Luessi, Martin and Larson, Eric and Engemann, Denis and Strohmeier, Daniel and Brodbeck, Christian and Goj, Roman and Jas, Mainak and Brooks, Teon and Parkkonen, Lauri and Hämäläinen, Matti}, biburl = {https://www.bibsonomy.org/bibtex/2f9230f431e493a824da7e9b7e6695c83/kurkha}, doi = {10.3389/fnins.2013.00267}, interhash = {5705829a71906af1e61a50b76219eb29}, intrahash = {f9230f431e493a824da7e9b7e6695c83}, issn = {1662-453X}, journal = {Frontiers in Neuroscience}, keywords = {magnetoencephalography}, pages = 267, timestamp = {2017-04-19T15:35:00.000+0200}, title = {MEG and EEG data analysis with MNE-Python}, url = {http://journal.frontiersin.org/article/10.3389/fnins.2013.00267}, volume = 7, year = 2013 }