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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 animal invasive neurophysiology.

Our objective is to share a comprehensive set of user-friendly tools with the scientific community using MEG/EEG as an experimental technique. For physicians and researchers, the main advantage of Brainstorm is its rich 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 24,000 accounts. See our reference page for a list of published studies featuring Brainstorm at work.

The best way to learn how to use Brainstorm, like any other academic software, is to benefit from local experts. However, you may be the first one in your institution to consider using Brainstorm for your research. We are happy to provide comprehensive online tutorials and support through our forum but there is nothing better than a course to make your learning curve steeper. Consult our training pages for upcoming opportunities to learn better and faster.

Finally, have a look regularly at our 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

• Generate surfaces from MRI volume | head, skull, cortex

• 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)

• sEEG/ECoG: Boundary Element Models (with OpenMEEG)
• 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