The NIRS Brain AnalyzIR Toolbox

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Abstract Submission 


Hendrik Santosa1, Xuetong Zhai1, Theodore Huppert1


1University of Pittsburgh, Pittsburgh, PA

First Author:

Hendrik Santosa  
University of Pittsburgh
Pittsburgh, PA


Xuetong Zhai  
University of Pittsburgh
Pittsburgh, PA
Theodore Huppert  
University of Pittsburgh
Pittsburgh, PA


Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low-levels of light (650–900 nm) to measure changes in cerebral blood volume and oxygenation. The lower operation cost, portability, and versatility of this method make it an alternative to methods such as functional magnetic resonance imaging for studies in pediatric and special populations and for studies without the confining limitations of a supine and motionless acquisition setup. However, the analysis of fNIRS data poses several challenges stemming from the unique physics of the technique, the unique statistical properties of data [1], and the growing diversity of non-traditional experimental designs being utilized in studies due to the flexibility of this technology. For these reasons, specific analysis methods for this technology must be developed. In this paper, we introduce the NIRS Brain AnalyzIR toolbox [2] 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.


The AnalyzIR toolbox is an open-source analysis package. This toolbox utilizes both custom namespace and class definitions written in MATLAB (MathWorks, Natick MA USA) language to provide an object-oriented programming interface to performing fNIRS analysis. The toolbox is maintained on a public project (Atlassian Corp. Sydney Australia. as well as the NIH's NeuroImaging Tools & Resources Collaboratory (NITRC) ( In addition, several demos or examples (e.g., fNIRS analysis, connectivity, group analysis, image reconstruction, registration, etc.) with explanations are provided in the toolbox download.


Object classes define context-specific methods, such as the drawing commands, which allow the same command (e.g.,) "draw" to act differently depending on the type of object (e.g., time-course, statistics variable, or reconstructed image) that is called upon (see Fig. 1).

Fig. 1. Example plot for data objects: (a) Example time series from 3 fNIRS channels with stimulus information shown along the bottom. An example of the same probe object shown in (b) 2D probe geometry; (c) 10–20 International System, and (d) registered 3D probe geometry is also demonstrated.
Supporting Image: Fig1Toolbox.png
   ·Fig. 1. Example plot for data objects: (a) Example time series (b) 2D probe geometry; (c) 10–20 International System, and (d) registered 3D probe geometry.


FNIRS technology will continue to evolve alongside other modalities to improve our understanding of human brain function. This open-source toolbox allows other researchers to freely use, modify, or share, while respecting the original toolbox authorship. We welcome more interaction with other researchers from various modalities (e.g., fNIRS, fMRI, MEG, EEG) on this AnalyzIR toolbox with a particular focus on developing multimodal methods within this common framework. The toolbox already supports all four of these modalities in some form. It is our hope that this toolbox will continue to grow and advance with (but not restricted to) the NIRS field. In addition, we already implemented the modules of the short-separation measurements in the toolbox as a popular technique to reduce the systemic physiological noises in the fNIRS signal. We also encourage researchers to use ROC analysis when they are proposing new methods. Doing so will allow proper comparisons of the performance of the proposed method with existing method.

Modeling and Analysis Methods:

EEG/MEG Modeling and Analysis
Image Registration and Computational Anatomy
Motion Correction and Preprocessing 2

Novel Imaging Acquisition Methods:

Multi-Modal Imaging


Data analysis
Design and Analysis
Near Infra-Red Spectroscopy (NIRS)
Statistical Methods

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My abstract is being submitted as a Software Demonstration.


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Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.


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Functional MRI
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[1] Huppert T. J. (2016), “Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy,” Neurophotonics, 3(1), 010401.
[2] Santosa, H. et al. (2018), “The NIRS brain AnalyzIR toolbox,” Algorithms, 11(5), 73.