While MNE-Python is designed to integrate with packages within the Python community, it also seamlessly interfaces with the other components of the MNE suite (and other M/EEG analysis tools) because it uses the same Neuromag FIF file format, with consistent analysis steps and compatible intermediate files. MNE-Python and the related MNE-Matlab sub-package that ship with MNE are both open source and distributed under the new BSD license, a.k.a 3-clause BSD, allowing their use in free as well as commercial software. The MNE-Python code is the most recent addition to the MNE suite. After an intensive collaborative software development effort, MNE-Python now provides a large number of additional features, such as time–frequency analysis, non-parametric statistics, connectivity estimation, independent component analysis (ICA), and decoding, a.k.a. multivariate pattern analysis (MVPA) or simply supervised learning, each of which is readily integrated into the standard MNE analysis pipeline. This comprehensive and still growing set of features available in the MNE-Python package is made possible by a group of dedicated contributors coming from multiple institutions, countries, and research areas of expertise who collaborate closely. These interactions
