MNE software for processing MEG and EEG data

By Alexandre Gramfort, Martin Luessi, Eric Larson, Denis-Alexander Engemann, Daniel Strohmeier, Christian Brodbeck, Lauri Parkkonen, Matti S. Hämäläinen. NeuroImage, 2013

1,925 citations recorded by the source corpus.

Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals originating from neural currents in the brain. Using these signals to characterize and locate brain activity is a challenging task, as evidenced by several decades of methodological contributions. MNE, whose name stems from its capability to compute cortically-constrained minimum-norm current estimates from M/EEG data, is a software package that provides comprehensive analysis tools and workflows including preprocessing, source estimation, time–frequency analysis, statistical analysis, and several methods to estimate functional connectivity between distributed brain regions. The present paper gives detailed information about the MNE package and describes typical use cases while also warning about potential caveats in analysis. The MNE package is a collaborative effort of multiple institutes striving to implement and share best methods and to facilitate distribution of analysis pipelines to advance reproducibility of research. Full documentation is available at http://martinos.org/mne.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/4187ef89f3e4318f1ad721be4c1b26ab36be606c

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In Cognitive Neuroscience

Citations in the corpus

MEG and EEG data analysis with MNE-Python (2013, methodology): “In addition, it also implements new algorithms, proposed and only recently published by the MNE-Python authors, making them publicly available for the ﬁrst time (Gramfort et al., 2010a, 2011, 2013b; Larson and Lee, 2013).”
Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas (2016, neutral): “Both, the FreeSurfer and Caret cortical sheet representations contain more than 10 vertices per hemisphere and were subsampled for MEG source analysis with the MNE software suite (Gramfort et al., 2014).”
Parameterizing neural power spectra into periodic and aperiodic components (2020, methodology): “All EEG data were processed with the MNE Python toolbox 58 , the algorithm described herein and custom scripts.”
Brains and algorithms partially converge in natural language processing (2022, methodology): “A single-layer forward model was generated with the Freesurfer-wrapper implemented in MNE-Python 92 .”
An open-source, high-performance tool for automated sleep staging (2021, methodology): “Assuming that the PSG data are stored in the gold-standard EDF, this can be done in one line of code using the MNE package ( Gramfort et al., 2014 ), which has a dedicated function to load EDF files (https:// mne. tools/ stable/ generated/ mne. io. read_ raw_ edf. html).”
Assessing and tuning brain decoders: Cross‐validation, caveats, and guidelines (2016, methodology): “MEG data analysis was performed with the MNE-Python software [13, 14].”
Speech and music recruit frequency-specific distributed and overlapping cortical networks (2024, background): “The cohort consists of 13 unilateral implantations (10 left, 3 right) and 5 bilateral implantations, yielding a total of 271 electrodes and 3371 contacts (see Figure 1C-D for electrodes localization).”
Macroscale traveling waves evoked by single-pulse stimulation of the human brain (2023, unknown): “Data were visualized using matplotlib (Hunter, 2007) , seaborn (Waskom, 2021), and MNE (Gramfort et al., 2014).”
Age-related neural dynamics of empathy in preschoolers: an EEG study with naturalistic viewing (2026, unknown): “Topographic maps were generated via surface Laplacian interpolation (Gramfort et al. 2014).”
Alterations in aperiodic and periodic EEG activity in young children with Down syndrome (2024, unknown): “Group differences in the power spectra were examined using a non-parametric clustering method, controlling for multiple comparisons using Monte Carlo estimation with 10,000 permutations (Maris and Oostenveld, 2007), employed with MNE-Python (Gramfort et al., 2014).”
Alterations in aperiodic and periodic EEG activity in young children with Down syndrome (2024, unknown): “Group differences in the power spectra were examined using a non-Fig parametric clustering method, controlling for multiple comparisons using Monte Carlo estimation with 10,000 permutations (Maris and Oostenveld, 2007), employed with MNE-Python (Gramfort et al., 2014).”
Spatiotemporal patterns of theta-band activity during rapid-eye movement sleep: a magnetoencephalography analysis (2024, methodology): “We computed the MNE source distribution with unconstrained sources and default Brainstorm parameters for each epoch (Gramfort et al., 2014).”
Task‐specific topology of brain networks supporting working memory and inhibition (2024, methodology): “We used MNE-python (Gramfort et al., 2014) for EEG preprocessing, the Frites software package (Combrisson et al., 2022) for estimation of functional connectivity, networkX (Hagberg et al., 2008) for graph measures calculation and lme4 (Bates et al., 2015) for modeling.”
Strengthening of alpha synchronization is a neural correlate of cognitive transfer (2024, methodology): “The signal was projected to the parcel space using minimum norm estimation (MNE) with the dSPM method using MNE-Python (Gramfort et al. 2014).”