Minicourse-16

Introduction to the NIRS Brain AnalyzIR Toolbox

Theodore Huppert, Hendrik Santosa, Xuetong Zhai, University of Pittsburgh

Duration: 180 min

Capacity: 40

Level: Introductory, Some coding experience

Data Analysis Toolbox

Requirements: Laptop; Matlab, Brain AnalyzIR pre-installed

Synopsis: The analysis of fNIRS data poses several challenges stemming from the unique physics of the technique, the unique statistical properties of data, and the growing diversity of non-traditional experimental designs being utilized in studies due to the flexibility of this technology. For these reasons, fNIRS-specific analysis methods for this technology must be developed. In this mini-course, we will introduce the NIRS Brain AnalyzIR toolbox as an open-source Matlab-based analysis package for fNIRS data management, pre-processing, and first- and second-level (i.e., single subject and group level) statistical analysis. We also will describe the basic architectural format of this toolbox, which is based on the object-oriented programming paradigm.

Learning objectives: Introduce the NIRS Brain AnalyzIR toolbox as an open-source Matlab-based analysis package for fNIRS data management, pre-processing, and first- and second-level (i.e., single subject and group level) statistical analysis. We also will describe the basic architectural format of this toolbox, which is based on the object-oriented programming paradigm.

Requirements: You are expected to bring a laptop with Matlab and Brain AnalyzIR installed. You can download Brain AnalyzIR toolbox from our website https://bitbucket.org/huppertt/nirs-toolbox/wiki/Home for free. Also please run the toolbox demo files “code_testing_demo.m” which checks that the toolbox is
installed properly and “fnirs_analysis_demo.m” which will download a dataset from the web and runs basic analysis steps.

We suggest that you read the following papers before the course:

  • Santosa et al., “The NIRS Brain AnalyzIR Toolbox”, Algorithms 2018, 11(5), 73.
  • Huppert, “Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy”, Neurophotonics 2016, 3(1), 010401.