NIRSTORM

Current features include:

• classical within-subject analysis comprising motion-correction, MBLL and window-averaging.
• statistical analysis using GLM
• optimal montages that optimize the sensitivity to a given cortical region of interest
• source reconstruction (cMEM, MNE)
• precomputed fluence template based on Colin27

Authors

• Thomas Vincent, EPIC center, Montreal Heart Institute, Montreal, Canada
• Zhengchen Cai, PERFORM Centre and physics dpt., Concordia University, Montreal, Canada
• Alexis Machado, Multimodal Functional Imaging Lab., Biomedical Engineering Dpt, McGill University, Montreal, Canada
• Edouard Delaire, PERFORM Centre and physics dpt., Concordia University, Montreal, Canada
• Robert Stojan, Sportpsychology, Chemnitz University of Technology, Germany
• Louis Bherer, Centre de recherche EPIC, Institut de Cardiologie de Montréal, Montréal, Canada
• Jean-Marc Lina, Electrical Engineering Dpt, Ecole de Technologie Supérieure, Montréal, Canada
• Christophe Grova, PERFORM Centre and physics dpt., Concordia University, Montreal, Canada

Usage

The main documentation is in the form tutorials available on the nirstorm github project wiki.

Installation

Nirstorm is available in two forms: an open-source Matlab plugin for Brainstorm (Matlab license required) and is included in the Brainstorm standalone version (Java executable)

Matlab version can be installed using the Brainstorm plugin system :

This required the Brainstorm version 3.210414 (14 April 2021) or higher. To use nirstorm with a previous version of Brainstorm, refers to this tutorial https://github.com/Nirstorm/nirstorm/wiki/Installation (Note: We recomand you to always use the latest version of Brainstorm)

Standalone version is directly included in Brainstorm standalone version. Follow Brainstorm tutorial for more information: https://neuroimage.usc.edu/brainstorm/Installation