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Minicourse EDDA-CG16

NIRSTORM: a Brainstorm plugin dedicated to fNIRS statistical analysis, 3D reconstructions and optimal probe design

Christophe Grova

with Edouard Delaire

About this course

NIRSTORM is a plugin dedicated to fNIRS data analysis, built upon Brainstorm, an internationally recognized software for EEG/MEG processing, featuring advanced databasing, visualization, signal processing, source localization and statistical analysis methods. The purpose of this mini-course is to introduce NIRSTORM as a user-friendly and fully complete environment dedicated to fNIRS statistical analysis. The first section will be dedicated to beginners, introducing the NIRSTORM database, data importation and classical channel-space fNIRS processing (bandpass filtering, Modified Beer-Lambert Law, motion correction and window averaging). Most recent updates will then be presented: General Linear Model-based statistical analyses (auto-regressive/precoloring model, mixed-effect group-level analysis) to provide statistics of the hemodynamic response either in the channel space or along the cortical surface after 3D reconstruction. Finally, we will present the most advanced NIRSTORM features, such as the integration of MCXLab software [Fang and Boas Opt. Express 2009] to estimate light sensitivity profiles within anatomical head models, our method allows personalized optimal montage design targeting a predefined brain region [Machado et al JNS-Methods 2018], advanced 3D reconstructions using Maximum Entropy on the Mean [Cai et al HBM 2021], and finally time-frequency analysis using Morse wavelet, removing the 1/f aperiodic signals.

Learning Outcomes

  • Use Brainstorm and NIRSTORM’s GUI efficiently to perform standard fNIRS processing and statistical analysis through GLM approaches.
  • Explore more advanced features such as tomographic reconstructions and optimal montage design in the plugin.

Course Plan

Level: Introductory

Pre-Requisites: None
Course Duration: 3 hours

Delivery Plan

The course will consist of hands-on sessions, and fNIRS data sets dedicated to the training will be made available to the participants.

Part 1: Database organization in Brainstorm and fNIRS data importation; standard fNIRS preprocessing and quality check; statistical analysis of the hemodynamic response; Time-Frequency analysis in the channel space.

Part 2: fNIRS forward model through MCXLab; personalized optimal montage design targeting a predefined brain region; advanced 3D reconstruction methods; wavelet-based MEM dedicated to localizing ongoing resting-state fNIRS signals in specific frequency bands.

Why enrol on this course?

Despite measuring physiological signals of different origins, EEG and fNIRS share several similarities: (i) they consist of scalp measurements, (ii) they offer an excellent temporal resolution and access to long-duration recordings, (iii) their spatial resolution is limited and 3D reconstruction of the generators of these scalp recordings requires solving an ill-posed inverse problem. This was the main reason for us to choose the Brainstorm software environment [Tadel et al. Comp Intell Neurosci 2011] to develop an fNIRS data analysis platform inspired by electrophysiology. NIRSTORM allows ideal 3D visualizations and interaction features involving multi-channel signals in the time domain, co-registration of fNIRS sensors along an anatomical model and eventual 3D reconstructions of hemodynamic responses within the brain along the cortical surface. In addition to fNIRS specific features we implemented, NIRSTORM also benefits from Brainstorm complete library of signal processing methods that can be directly applied to fNIRS data (filtering, time-frequency based analysis, non-parametric statistics, estimation of functional connectivity patterns), while offering the possibility to implement new specific scripts and pipelines. NIRSTORM is an open-source initiative developed in Matlab, and it welcomes any contribution. It is currently hosted on GitHub (, where the wiki pages of our previous training sessions and tutorials are available.