MEG and EEG data analysis with MNE-Python

By Alexandre Gramfort, Martin Luessi, Eric Larson, Denis-Alexander Engemann, Daniel Strohmeier, Christian Brodbeck, Roman Goj, Mainak Jas, Teon L Brooks, Lauri Parkkonen and 1 more. Front. Neuroinform., 2013

3,690 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/be6638e641c5e993474703de6e0261357da71736

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Citations in the corpus

Deep Feature Learning for EEG Recordings (2015, methodology): “The following common-practice pre-processing steps were applied to the raw EEG and EOG data using the MNE-python toolbox by Gramfort et al. (2013) to remove unwanted artifacts.”
Autoreject: Automated artifact rejection for MEG and EEG data (2016, methodology): “All general data processing was done using the open source software MNE-Python (Gramfort et al., 2013).”
Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data (2016, methodology): “…www.pymvpa.org) handles both fMRI and M/EEG data using the open-source Python language (Hanke, Halchenko, Sederberg, Olivetti, et al., 2009), MNE (Gramfort et al., 2013, 2014; martinos.org/mne) is a Python toolbox (and can be accessed in MATLAB [The MathWorks, Natick, MA]) designed for M/EEG…”
Recurrence is required to capture the representational dynamics of the human visual system (2019, methodology): “15 MEG source reconstruction Source reconstructions Source reconstructions were performed using MNE (18), as implemented in the MNE python toolbox (47).”
Aperiodic neural timescales in prefrontal cortex dilate with increased task abstraction (2025, unknown): “All preprocessing performed on these resting-state data were performed using MNE-Python version 1.61 (38,39).”
EEG Blink Artifacts Can Identify Read Music in Listening and Imagery (2025, unknown): “Visualizations of IC components were made using MNE-Python [23].”
Review of Multimodal Data Acquisition Approaches for Brain–Computer Interfaces (2024, unknown): “…we have found python and R to be rich in libraries that aid in data filtering and analysis in the course of this review, such as the MNE-Python [31] and nin-Py [32] modules with a multitude of functionalities. that aid in data ﬁ ltering and analysis in the course of this review, such as the…”
Processing of auditory feedback in perisylvian and insular cortex (2024, methodology): “Data preprocessing Data were preprocessed offline using a combination of custom MATLAB scripts and custom Python scripts built off the MNE-python software package (Gramfort et al., 2013).”
An intracranial dissection of human escape circuits (2024, unknown): “We extracted the HGA using the MNE software in Python, following its recommended time-frequency analysis pipeline 103 .”