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. 2011:2011:879716.
doi: 10.1155/2011/879716. Epub 2011 Apr 13.

Brainstorm: a user-friendly application for MEG/EEG analysis

François Tadel  1 , Sylvain Baillet, John C Mosher, Dimitrios Pantazis, Richard M Leahy
Affiliations

Brainstorm: a user-friendly application for MEG/EEG analysis

François Tadel et al. Comput Intell Neurosci. 2011.

Abstract

Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).

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Figures

Figure 1
Figure 1
General overview of the Brainstorm interface. Considerable effort was made to make the design intuitive and easy to use. The interface includes: (a) a file database that provides direct access to all data (recordings, surfaces, etc.), (b) contextual menus that are available throughout the interface with a right-button click, (c) a batch tool that launches processes (filtering, averaging, statistical tests, etc.) for all files that were drag-and-dropped from the database; (right) multiple displays of information from the database, organized as individual figures and automatically positioned on the screen, and (d) properties of the currently active display.
Figure 2
Figure 2
Brainstorm features multiple solutions for the visualization of MEG/EEG recordings.
Figure 3
Figure 3
Interface for reviewing raw recordings and marking events.
Figure 4
Figure 4
MRI and surface visualization.
Figure 5
Figure 5
Registration of MRI data volumes with corresponding surface meshes.
Figure 6
Figure 6
Brainstorm tool for editing of EEG electrode montages.
Figure 7
Figure 7
Warping of the MRI volume and corresponding tissue surface envelopes of the Colin27 template brain to fit a set a digitized head points (white dots in upper right corner): initial Colin27 anatomy (left) and warped to the scalp control points of another subject (right). Note how surfaces and MRI volumes are adjusted to the individual data.
Figure 8
Figure 8
Interactive selection of the best-fitting sphere model parameter for MEG and EEG forward modeling.
Figure 9
Figure 9
A variety of options for the visualization of estimated sources. (a) 3D rendering of the cortical surface, with control of surface smoothing; (c) 3D orthogonal planes of the MRI volumes; (b) conventional orthogonal views of the MRI volume with overlay of the MEG/EEG source density.
Figure 10
Figure 10
Selection of cortical regions of interest in Brainstorm and extraction of a representative time course of the elementary sources within.
Figure 11
Figure 11
Temporal evolution of elementary dipole sources estimated with the external Xfit software. Data from a right-temporal epileptic spike. This component was implemented in collaboration with Elizabeth Bock, MEG Program, Medical College of Wisconsin.
Figure 12
Figure 12
A variety of display options to visualize time-frequency decompositions using Brainstorm (see text for details).
Figure 13
Figure 13
Graphical interface of the batching tool. (a) selection of the input files by drag-and-drop. (b) creation of an analysis pipeline. (c) example of Matlab script generated automatically.
Figure 14
Figure 14
Example of Brainstorm script.
Figure 15
Figure 15
Student t-test between two conditions. (a) selection of the files. (b) selection of the test. (c) options tab for the visualization of statistical maps, including the selection of the thresholding method.
Figure 16
Figure 16
Cortical activations 46 ms after the electric stimulation of the left median nerve on the subject's brain (a) and their projection in the MNI brain (b).
See this image and copyright information in PMC

References

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