Tutorials & E-Library

Brainstorm can export raw EEG and MEG files following the standard data organization of the Brain Imaging Data Structure (BIDS).

For an example of how to import an existing BIDS dataset, refer to the following tutorial.

Note: Only EEG and MEG data files are supported for now. We will support electrophysiology BIDS exports when the specifications for those data types are finalized by the BIDS community.

Epileptogenic Zone Fingerprint (EZF) is an open-source software that allows you to process stereotactic EEG (SEEG) recordings, visualize time-frequency (TF) plots and potentially help you localize the epileptogenic zones (EZs) for subjects with focal epilepsies.

This tutorial explains how to import and process Elekta-Neuromag current phantom recordings. We decided to release this example for testing and cross-validation purposes. With these datasets, we can evaluate the equivalence of various forward models and dipole fitting methods in the case of simple recordings with single dipoles. The recordings are available in two file formats (native and FIF) to cross-validate the file readers available in Brainstorm and MNE. A similar page exists for the CTF phantom.

This tutorial describes how to process continuous Elekta-Neuromag MEG recordings. It is based on median nerve stimulation acquired at the Massachusetts General Hospital in 2005 with a Neuromag Vectorview 306 system. The sample dataset contains the results for one subject for both arms: 18 minutes, 300 stimulations per arm.

This tutorial introduces the most advanced FEM modeling options available in the Brainstorm environment, applied to MEG+EEG recordings of a median nerve stimulation. The pipeline presented here includes: FEM mesh reconstruction with SimNIBS/CAT12, FEM head model including DTI tensors for modeling anisotropic conductivities (using BrainSuite), FEM forward model estimation with DUNEuro. This pipeline requires many third-party programs and very long computation times. For a simpler FEM pipeline, please refer to the tutorial: Realistic head model: FEM with DUNEuro.

Note that the operations used here are not detailed, the goal of this tutorial is not to introduce Brainstorm to new users. For in-depth explanations of the interface and theoretical foundations, please refer to the introduction tutorials.

This tutorial introduces some concepts that are specific to the management of ECoG/sEEG recordings in the Brainstorm environment. It is based on a clinical case from the Epilepsy Center at the University Hospital of Freiburg, Germany.

The Brainstorm Digitize application allows for a connection to a Polhemus device and collection of digitized head points and/or EEG sensor locations.

The application is designed for two receivers (i.e. the stylus and reference) and one transmitter.

The connection to the device is made via a serial port connection. This can be accomplished using a direct serial connection or with a USB-to-Serial cable connection to the host computer.

The toolbox features a convenient signal converter that produces local field potential (LFP) time-series from raw signals. The converter jointly processes the recorded raw data and the events generated by the spike sorter. The fast-sampled raw signals are downsampled to a new user selected sampling rate (e.g. 1000 Hz), with an anti-aliasing bandpass filter (e.g., [0.5, 150] Hz) applied. LFP time series and spiking/acquisition events are automatically featured in the Brainstorm database as new data files.

Corticomuscular coherence measures the degree of similarity between electrophysiological signals (MEG, EEG, ECoG sensor traces or source time series, especially over the contralateral motor cortex) and the EMG signals recorded from muscle activity during voluntary movements. Cortical-muscular signals similarities are conceived as due mainly to the descending communication along corticospinal pathways between primary motor cortex (M1) and the muscles attached to the moving limb(s). For consistency purposes, the present tutorial replicates, with Brainstorm tools, the processing pipeline "Analysis of corticomuscular coherence" of the FieldTrip toolbox.

This page describes the various coordinate systems used in Brainstorm, and explains how to convert beetween referentials.

Brain functions (e.g., in cognition, behavior and perception) stem from the coordinated activity of multiple regions. Brain connectivity measures are designed to probe how brain regions (or nodes) interact as a network. A distinction is made between structural (fiber pathways), functional (non-directed statistical associations) and effective (causal interactions, or "directed functional connectivity") connectivity between regions. Here we explain how to compute various connectivity metrics for non-directed and directed functional connectivity analyses with Brainstorm, both with simulated (ground-truth) and empirical data.

This tutorial will guide the user through the usage of all the features available in the connectivity visualization tool. This tool displays functional connectivity data in the form of a 'graph' suited for human understanding. For a theoretical background on functional connectivity and different connectivity measures, please refer to the tutorial Functional connectivity.