MNE-Python

Authors: Alexandre GramfortMartin LuessiEric LarsonDenis A. EngemannDaniel StrohmeierChristian BrodbeckRoman GojMainak JasTeon BrooksLauri ParkkonenMatti S. Hämäläinen
Updated: Thu 02 December 2021
Source: https://mne.tools/stable/index.html
Type: Python package
Languages: N/A
Keywords: experimentPythondatamachine-learningneuroscience
Open Access: yes
License: BSD-3-Clause License
Documentation: https://mne.tools/stable/overview/index.html
Publications: Gramfort A., Luessi M., Larson E., Engemann D.A., Strohmeier D., Brodbeck C., Goj R., Jas M., Brooks T., Parkkonen L. and Hämäläinen M. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience. 7:267. doi: 10.3389/fnins.2013.00267
Summary:

As part of the MNE software suite, MNE-Python is an open-source software package that provides 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. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers.