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Introduction
Brainstorm is a collaborative, open-source application dedicated to the analysis of brain recordings:
MEG, EEG, fNIRS, ECoG, depth electrodes and multiunit electrophysiology.
Our objective is to share a comprehensive set of user-friendly tools with the scientific community using MEG/EEG as an experimental technique. For clinician-scientists and researchers, the main advantage of Brainstorm is its easy and intuitive graphic interface, which does not require any programming knowledge. We are also putting the emphasis on practical aspects of data analysis (e.g., with scripting for batch analysis and intuitive design of analysis pipelines) to promote reproducibility and productivity in MEG/EEG research. Finally, although Brainstorm is developed with Matlab (and Java), it does not require users to own a Matlab license: an executable, platform-independent (Windows, MacOS, Linux) version is made available in the downloadable package. To get an overview of the interface, you can watch this introduction video.
Since the project started by the end of the 1990's, our server has registered more than 38,000 users, contributing thousands of scientific publications.
We are happy to provide comprehensive documentation via online tutorials and data, and support through a user forum to help you learn how to use Brainstorm. We also deliver in-person and online courses: consult our training pages for upcoming opportunities to learn to use Brainstorm better and faster.
Finally, browse the What's new page for staying on top of Brainstorm news and updates and 
Like us on Facebook to stay in touch. We hope you enjoy using Brainstorm as much as we enjoy developing and sharing these tools with the community!
Support
This software was generated primarily with support from the National Institutes of Health under grants R01-EB026299, 2R01-EB009048, R01-EB009048, R01-EB002010 and R01-EB000473.
Primary support was provided by the Centre National de la Recherche Scientifique (CNRS, France) for the Cognitive Neuroscience & Brain Imaging Laboratory (La Salpetriere Hospital and Pierre & Marie Curie University, Paris, France), and by the Montreal Neurological Institute to the MEG Program at McGill University.
Additional support was also from two grants from the French National Research Agency (ANR) to the Cognitive Neuroscience Unit (PI: Ghislaine Dehaene; Inserm/CEA, Neurospin, France) and to the ViMAGINE project (PI: Sylvain Baillet; ANR-08-BLAN-0250), and by the Epilepsy Center in the Cleveland Clinic Neurological Institute.
How to cite Brainstorm
Please cite the following reference in your publications if you have used our software for your data analyses: How to cite Brainstorm. It is also good offline reading to get an overview of the main features of the application.
Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM (2011)
Brainstorm: A User-Friendly Application for MEG/EEG Analysis
Computational Intelligence and Neuroscience, vol. 2011, ID 879716
What you can do with Brainstorm
MEG/EEG recordings
Digitize the position of the EEG electrodes and the subject's head shape | link
Support for multiple modalities | MEG, EEG, sEEG, ECoG, NIRS, electrophysiology
Read data from the most popular file formats | link
Interactive access to data files in native formats | link
Import data in Matlab | link
Import and order data in a well-organized database | link
Review, edit and import event markers in continuous recordings | link
Automatic detection of well-defined artifacts: eye blinks, heartbeats | link
Artifact correction: Signal Space Projections (SSP)
Independent Component Analysis (ICA)
Detection of bad trials / bad channels
Baseline correction | link
Power spectrum density | link
Frequency filtering, resampling | link
Epoching | link
Averaging | link
Powerful and versatile visualization
Various time series displays | link
Data mapping on 2D or 3D surfaces | link
Generate slides and animations (export as contact sheets, snapshots, movies, ...)
Flexible montage editor | link
Channel selection and sensor clustering | link
MRI visualization and coregistration
Import individual MRI volumes and surfaces | link, FreeSurfer, BrainSuite, BrainVISA, CAT, CIVET
Deface MRI images | link
Normalize MRI to MNI space | link
Use anatomy templates | link
Warp templates to individual head surface | link
Automatic or interactive co-registration with the MEG/EEG coordinate system | link
Volume rendering (multiple display modes) | link
Anatomical atlases: surface parcelations and sub-cortical regions | link
Database: Keep your data organized
- Ordering of data by protocol, subject and condition/event
- Quick access to all the data in a study for efficient, batch processing
- Quick access to comparisons between subjects or conditions
Graphical batching tools
Apply the same process to many files in a few clicks | link
Automatic generation of scripts to perform full analysis | link
Flexible plug-in structure that makes the software easy to extend | link
Head modeling
MEG: Single sphere, overlapping spheres | link
EEG: Berg's three-layer sphere, Boundary Element Models (with OpenMEEG) and the Finite Element Models (with DUNEuro)
sEEG/ECoG: Boundary Element Models (with OpenMEEG) and the Finite Element Models (with DUNEuro)
- Interactive interface to define the best-fitting sphere
Source modeling
Estimation of noise statistics for improved source modeling | link
L2 Minimum-norm current estimates | link
Normalizations: dSPM, sLORETA, Z-score | link
- All models can be cortically-constrained or not, and with/without constrained orientations
Source estimation on cortical surface, MRI volume or sub-cortical atlases
Dipole scanning | link
Dipole fitting with FieldTrip | link
Import and display of Neuromag's Xfit and CTF's DipoleFit dipole models | link
Simulation of MEG/EEG recordings from source activity | link
Source display and analysis
Multiple options for surface and volume rendering of the source maps | link
- Re-projection of the sources in the MRI volume (from surface points to voxels)
Definition of regions of interest | link
Project the sources on a surface with higher or lower resolution | link
Project the sources on a group template | link
Surface or volume spatial smoothing | link
Time-frequency decompositions
Time-frequency analyses of sensor data and sources time series using Morlet wavelet, Fast Fourier Transform and Hilbert transform | link
- Define time and frequency scales of interest
- Multiple display modes available
Functional connectivity
Correlation, coherence, Granger causality, phase-locking value | link
Phase-amplitude coupling estimation | link
- Both at sensor and source levels
- Dynamic circle plots for representing dense and high-dimensional connectivity graphs
Representation of functional connectivity on anatomical fibers | link
Machine learning
Decoding / Multivariate pattern analysis with SVM or LDA | link
Group analysis
Registration of individual brains to a template | link
Parametric and non-parametric statistics | link
Standard group analysis pipeline | single subject, group, roadmaps
Guidelines for scripting the analysis of large datasets | link
Documentation and support
- Easy and automatic updates of the software
Detailed step-by-step tutorials for most common features
Active user forum supported by a large user community
Organization of training courses on demand
Supported file formats
| EEG / Electrophysiology
Data structures of similar applications
Dipole models
Surface atlases
| MEG
fNIRS
Other recordings
Sensors locations
MRI volumes
Surface meshes
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