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Updated: May 6, 2026

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

MNE software for processing MEG and EEG data.

Alexandre Gramfort¹, Martin Luessi², Eric Larson³

¹Institut Mines-Telecom, Telecom ParisTech, CNRS LTCI, 37-39 Rue Dareau, 75014 Paris, France; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Charlestown, MA, USA; Institut Mines-Telecom, Telecom ParisTech, CNRS LTCI, Paris, France; NeuroSpin, CEA Saclay, Bat. 145, 91191 Gif-sur-Yvette Cedex, France.

|October 29, 2013

Summary

This summary is machine-generated.

The MNE software package offers tools for analyzing magnetoencephalography and electroencephalography (M/EEG) brain activity. It provides comprehensive workflows for preprocessing, source estimation, and connectivity analysis to enhance research reproducibility.

Keywords:

Connectivity Electroencephalography (EEG)Inverse problem Magnetoencephalography (MEG)Non-parametric statistics Software Time–frequency analysis

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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Area of Science:

Neuroscience
Computational Neuroscience
Biomedical Engineering

Background:

Magnetoencephalography and electroencephalography (M/EEG) measure neural activity via electromagnetic signals.
Characterizing and localizing brain activity from M/EEG data presents significant methodological challenges.
Decades of research have focused on developing advanced analysis techniques for M/EEG.

Purpose of the Study:

To provide a detailed overview of the MNE software package.
To describe typical use cases and potential analytical caveats associated with MNE.
To promote reproducible research through shared best practices and analysis pipelines.

Main Methods:

MNE software package for M/EEG data analysis.
Cortically-constrained minimum-norm current estimation.
Comprehensive workflows: preprocessing, source estimation, time-frequency analysis, statistical analysis, functional connectivity.
Collaborative development across multiple research institutes.

Main Results:

MNE facilitates detailed analysis of M/EEG data.
The package supports advanced source localization and functional connectivity estimation.
Identified potential pitfalls in M/EEG data analysis using MNE.

Conclusions:

MNE is a valuable, collaborative software package for M/EEG research.
It enhances the reproducibility of neuroscience research by standardizing analysis pipelines.
Users are encouraged to consult detailed documentation for optimal application and to avoid common caveats.