Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Early Sepsis Detection Using Heterogeneous Structured ICU Data with Explainable Deep Learning.

Sensors (Basel, Switzerland)·2026
Same author

Addition of CAD polygenic risk score to coronary artery calcium score enhances prediction of MACE.

Frontiers in cardiovascular medicine·2026
Same author

Quantifying cerebellar signal detectability in MEG and EEG in epilepsy using anatomically informed source modeling.

NeuroImage·2026
Same author

Efficacy of Optically Pumped Magnetometers in Detecting Activity From the Cerebellar Cortex.

Human brain mapping·2026
Same author

Frequency-Resolved Cortical Functional Connectivity Across the Adult Lifespan.

Human brain mapping·2026
Same author

Quantifying Cerebellar Signal Detectability in MEG and EEG in Epilepsy Using Anatomically Informed Source Modeling.

bioRxiv : the preprint server for biology·2026
Same journal

CEST MRI reveals nicotine-induced alterations in glutamate-associated molecular connectivity in the mouse brain.

Frontiers in neuroscience·2026
Same journal

Brain protein burden is related to intravoxel incoherent motion: PET-MR imaging study.

Frontiers in neuroscience·2026
Same journal

Screening the optimal rTSMS frequency to orchestrate immune-fibrotic remodeling for adult spinal cord repair.

Frontiers in neuroscience·2026
Same journal

Assessment of tenecteplase target-associated pathogenic mechanisms underlying depression in acute ischemic stroke patients: insights from artificial intelligence-driven multi-omics analysis and <i>in vitro</i> validation.

Frontiers in neuroscience·2026
Same journal

Sex-divergent intrinsic brain function in Parkinson's disease: elevated nigral fluctuations and premotor-visuospatial coupling in female patients.

Frontiers in neuroscience·2026
Same journal

Spatial transcriptomics on an expanded dataset at the brain-electrode interface: exploration of variability and identification of novel biomarkers.

Frontiers in neuroscience·2026
See all related articles

Related Experiment Video

Updated: May 4, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

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

Published on: October 24, 2012

14.3K

MEG and EEG data analysis with MNE-Python.

Alexandre Gramfort1, Martin Luessi2, Eric Larson3

  • 1Institut Mines-Telecom, Telecom ParisTech, CNRS LTCI Paris, France ; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and Harvard Medical School Charlestown MA, USA ; NeuroSpin, CEA Saclay Gif-sur-Yvette, France.

Frontiers in Neuroscience
|January 17, 2014
PubMed
Summary
This summary is machine-generated.

MNE-Python offers advanced algorithms for analyzing magnetoencephalography and electroencephalography (M/EEG) brain signals. This open-source package simplifies complex neuroimaging analysis, promoting reproducible research.

Keywords:
electroencephalography (EEG)magnetoencephalography (MEG)neuroimagingopen-sourcepythonsoftware

More Related Videos

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.9K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.6K

Related Experiment Videos

Last Updated: May 4, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

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

Published on: October 24, 2012

14.3K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.9K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.6K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Magnetoencephalography and electroencephalography (M/EEG) measure subtle brain electromagnetic signals.
  • Analyzing M/EEG data for neural activation requires interdisciplinary expertise.
  • Characterizing and localizing brain activity from M/EEG is a significant computational challenge.

Purpose of the Study:

  • To introduce MNE-Python, an open-source software package for M/EEG data analysis.
  • To provide state-of-the-art algorithms for preprocessing, source localization, and connectivity analysis.
  • To facilitate the creation of reproducible M/EEG analysis pipelines.

Main Methods:

  • MNE-Python implements advanced algorithms in Python for M/EEG data processing.
  • It offers tools for source localization, statistical analysis, and functional connectivity estimation.
  • The package integrates seamlessly with scientific Python libraries like NumPy, SciPy, and Nibabel.

Main Results:

  • MNE-Python provides a comprehensive suite of tools for M/EEG analysis.
  • The software enables users to build custom analysis pipelines through Python scripting.
  • It supports reproducible research by offering access to preprocessed datasets and detailed documentation.

Conclusions:

  • MNE-Python is a powerful, open-source tool for advancing M/EEG research.
  • Its collaborative development and integration with the scientific Python ecosystem foster best practices.
  • The package democratizes complex neuroimaging analysis, making it accessible to a wider research community.