by A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck, M. Hamalainen
Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals induced by brain electrical activity. Using these signals to characterize and locate brain activations is a challenging task as confirmed by three decades of methodological developments. MNE, which holds its name from its historical ability to compute minimum norm estimates, is a software package that addresses this particular challenge by providing a state-of-the-art analysis workflow spanning preprocessing, various source localization methods, statistical analysis, and estimation of functional connectivity between distributed brain regions. This contribution aims to present the MNE-Python package which implements the full M/EEG analysis workflow (except forward modeling) in Python, exploiting the core scientific Python libraries (Numpy, Scipy and matplotlib). The MNE-Python code is provided under the simplified BSD license allowing code reuse, even in commercial products. The code is presently evolving quickly thanks to many contributors striving to share best practices in an open development process. MNE-Python gives access to a sample dataset, enabling users to get started quickly and methodological contributions to be reproduced by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.