Tutorials & E-Library

This tutorial introduces some concepts that are specific to the management of MEG/EEG files recorded with Yokogawa/KIT systems in the Brainstorm environment.

Brainstorm offers rudimental ways to interact with your connectivity matrices extracted using the different functional connectivity measures available

Brainstorm orients most of its database organization and processing stream for handling anatomical information together with the MEG/EEG recordings. The introduction tutorials start with the import of the T1 MRI of the subject, and this anatomy seems mandatory everywhere. These choices were made because the primary focus of Brainstorm was to estimate brain sources from MEG/EEG, which ideally requires an accurate spatial modelling of the head.

If you don't have access to anatomical images of your subjects or if you are not interested in source reconstruction, Brainstorm will still require that you explicitly define an anatomy; in those case you would use an anatomy template. In the case of group analysis at the source level, you would also use a template on which you would project of the individual results.

Several templates are available, with a large preference for using the MNI ICBM152 package distributed with Brainstorm because it provides the highest level of compatibility between different features within Brainstorm and with other software environments. You might be interested in using another one if you are working with different age ranges, or if you need to obtain results in a specific space. This tutorial provides references to the various templates available in Brainstorm.

The open-source software FreeSurfer can be used to extract the cortical envelope from a T1/T2 MRI and register it to an atlas. The process is fully automatic and the results can be imported in Brainstorm with just a few mouse clicks.

It is not the purpose of Brainstorm tutorials to teach you how to use BrainVISA. But many Brainstorm users are lost when it gets to the segmentation of the MRI. So here is a short introduction to the BrainVISA T1 MRI processing pipeline. To extract head and cortex meshes from a T1 MRI, you can also try to use: BrainSuite, CAT12 or FreeSurfer.

This tutorial was written for BrainVISA 4.6. To get started, or for additional information, you can also read the BrainVISA tutorial and the Morphologist tutorial.

All efforts should be made to avoid any movements and in particular head movements during a MEG recording, as it can cause various issues such as blurring of signals and loss of amplitude, mis-localization of source activity, and possibly motion artefacts. Yet it is important to evaluate and account for any head motion at the time of analysis. This is possible because the positions of the head tracking coils are saved in channels along the MEG data. It is important to note however that most analysis software, including Brainstorm, assume a single fixed position for the head for most computations. This "reference" position is the one that is measured just before the recording starts and that is saved separately from the continuous head localization channels.

This tutorial will explore different options on how to deal with head motion, using the example dataset from the tutorial: MEG resting state & OMEGA database (CTF). See there for details on how to download the dataset and load it in Brainstorm. Note that importing all the subjects can take a while; for this tutorial, you can only import sub-0007.

This tutorial introduces how to transfer events from one data set to a second data set of a different type, which was recorded at the same time but with a different sampling rate. We will explain this on the example of synchronizing ocular data recorded with an EyeLink eye tracker (SR Research, Canada) with a second raw recording, e.g. EEG.

Removing artifacts from MEG recordings using Signal Space Projections can sometimes be very efficient. Here are a few tricks to help you remove artifacts from your data that are ongoing or repeating.

Spike sorting is an essential step in electrophysiology that provides information on the selectivity of individual neurons. The common practice that most spike sorters use, is to apply a threshold on a band-passed version of the raw signals, collect a few samples of the data around that threshold crossing, and then cluster those waveforms based on their shape. Multiple clustered shapes picked up from the same electrode would signify the presence of multiple neurons. This automatic clustering (unsupervised), although powerful, can often be inaccurate. Therefore, a manual inspection of the clusters is required. After the unsupervised step, users normally manually approve or modify the clusters created (supervised step). For more general information, users can consult this non-technical article on Spike Sorting.

Our goal was to allow users to perform both steps (unsupervised-supervised) within Brainstorm. There are currently several solutions for spike sorting and since we cannot accommodate all of them, we have embedded 3 widely used algorithms within Brainstorm – Waveclus, UltraMegaSort2000 and Kilosort.

We understand that some users might not feel comfortable learning how to use a new spike sorter from scratch, therefore we created this page, so they can still use their spike sorter of preference (outside of Brainstorm), and later import the spiking events in Brainstorm using a compatible format.

The next section shows how the users can perform the Unsupervised and Supervised steps of spike sorting, with the algorithms embedded within Brainstorm.

This tutorial introduces the features developed in Brainstorm to compute and view SPRiNT models, or time-resolved parameterized spectral density maps.

This tutorial addresses everything that can be called "simulation" in Brainstorm: simulation of simple signals, simulation of full source maps from a few scouts, simulation of MEG/EEG recordings from source maps (real or synthetic). Its goal is to group all the information that was previously spread across multiple tutorials and forum posts, and difficult to access. The examples are based on the introduction tutorials, but can easily be transposed to any study.

At the end of the page, two video tutorials explain how to use the software SimMEEG for advanced signal simulations. This interface enables the simulation of multiple trials with many controlled properties: event-related power, PLV, PLI, PAC and various noise configurations.

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.