Tutorials & E-Library

This program is designed to capture head position information from the CTF MEG system and display a scalp surface relative to the sensor helmet. The acquisition workstation runs the FieldTrip program acq2ftx and a second computer runs Brainstorm and the FieldTrip buffer program. The acquisition workstation and the second computer need to be connected to each other either across the same network or a direct computer-to-computer network connection.

Brainstorm connects with features from many third-party libraries of methods. The external software can be downloaded or updated automatically by Brainstorm when needed. This tutorial presents the API to register and manage plugins.

The tools presented here are part of nirstorm, which is a brainstorm plug-in dedicated to NIRS data analysis. This current tutorial only illustrates basic features on how to import and process NIRS recordings in Brainstorm. To go further, please visit the nirstorm wiki. There you can find the latest set of tutorials covering optimal montage and source reconstruction, that were given at the last PERFORM conference in Montreal (2018).

You can use the free BrainSuite and SVReg software package to extract segmented brain surfaces from a T1-weighted MRI image. Surface extraction and segmentation can either be automatic or manual--this tutorial will step you through the automated process (more information on manual tweaks can be found in BrainSuite's documentation).

For this tutorial, we will use the auditory_tutorial_T1.nii file: download. After extracting the zip file, you can rename the MRI image as T1.nii for convenience. We will use this filename in the tutorial for convenience.

Unlike Brainstorm, Freesurfer and BrainVisa, BrainSuite does not use a database structure for storing subject information and scans. Instead, by default it stores its output in the same folder as the input MRI scan. Thus, it is often best to have each subject's structural scan in its own folder.

The open-source software FastSurfer can be used to extract the cortical envelope from a T1 MRI and register it to an atlas. The process is fully automatic, the results are compatible with FreeSurfer.

The CIVET pipeline can be used to extract the cortical envelope from a T1 MRI. The process is fully automatic and the results can be imported in Brainstorm with just a few mouse clicks.

CAT is a SPM12 toolbox that is fully interfaced with Brainstorm. It can replace efficiently FreeSurfer for generating the cortical surface from any T1 MRI. It runs on any OS in about 1 hour, instead of the typical 24hr FreeSurfer recon-all processing. The surfaces are registered to the templates with the FreeSurfer spheres, and include many surface and volume parcellations. You can either install and run the CAT segmentation from Brainstorm, or run it separately and import its outputs as you would do with FreeSurfer.

The display of the time series figures can be configured using montages of sensors. The term montage in the Brainstorm interface can refer to a simple sub-selection of data channels, or a linear recombination of these channels (eg. average reference or other EEG re-referecing montage). The selection of channels was already introduced in the tutorials Continuous recordings and EEG and epilepsy. This page illustrates how to use the montage editor to create custom displays.

Brainstorm offers a flexible plug-in structure. All the operations available when using the Process1 and Process2 tabs, which means most of the Brainstorm features, are in fact written as plug-ins.

If you are interested in running your own code from the Brainstorm interface and benefit from the powerful database and visualization systems, the best option is probably for you to create your own process functions. It can take some time to get used to this logic but it is time well invested: you will be able to exchange code easily with your collaborators and the methods you develop could immediately reach thousands of users. Once your functions are stable, we can integrate them in the main Brainstorm distribution and maintain the code for you to ensure it stays compatible with the future releases of the software.

This tutorial looks long and complicated, but don't let it scare you. Putting your code in a process is not so difficult. Most of it is a reference manual that details all the possible options, you don't need to understand it completely. The last part explains how to copy an existing process and modify it to do what you want.

This tutorial explains how to download MEG recordings from the Human Connectome Project (HCP) ConnectomeDB database and process them into Brainstorm. The original processing pipeline was described in this article and this reference manual. Here we will focus only on reproducing the results on resting MEG recordings presented in the OMEGA tutorial.

This tutorial introduces how to download resting state recordings from the OMEGA database and process them into Brainstorm. The goal is to evaluate where in the brain is distributed the power in specific frequency bands at rest. This example includes only 5 healthy subjects, but the same pipeline can be applied over larger populations.

The aim of this tutorial is to reproduce in the Brainstorm environment the analysis described in the SPM tutorial "Multimodal, Multisubject data fusion". We use here a recent update of this dataset, reformatted to follow the Brain Imaging Data Structure (BIDS), a standard for neuroimaging data organization. It is part of a collective effort to document and standardize MEG/EEG group analysis, see Frontier's research topic: From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software.

The data processed here consists of simultaneous MEG/EEG recordings from 16 participants performing a simple visual recognition task from presentations of famous, unfamiliar and scrambled faces. The analysis is split in two tutorial pages: the present tutorial describes the detailed interactive analysis of one single subject; the second tutorial describes batch processing and group analysis of all 16 participants.