MNE software for processing MEG and EEG data. 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.
Keywords for this software
References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Cole, S., Donoghue, T., Gao, R., Voytek, B.: NeuroDSP: A package for neural digital signal processing (2019) not zbMATH
- Hashemzadeh, Parham; Fokas, Athanassios S.: Helmholtz decomposition of the neuronal current for the ellipsoidal head model (2019)
- Pascarella, Annalisa; Pitolli, Francesca: An inversion method based on random sampling for real-time MEG neuroimaging (2019)
- Calvetti, D.; Pascarella, A.; Pitolli, F.; Somersalo, E.; Vantaggi, B.: A hierarchical Krylov-Bayes iterative inverse solver for MEG with physiological preconditioning (2015)