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Related Experiment Video

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LIONirs: flexible Matlab toolbox for fNIRS data analysis.

Julie Tremblay1, Eduardo Martínez-Montes2, Alejandra Hüsser3

  • 1LIONLAB, Research Center of the Sainte-Justine University Hospital, Université de Montréal, Montreal, Canada.

Journal of Neuroscience Methods
|January 29, 2022
PubMed
Summary

LIONirs is a new MATLAB toolbox for analyzing functional near-infrared spectroscopy (fNIRS) data. It offers flexibility and simplicity for processing complex brain imaging data, aiding researchers in pediatric and challenging populations.

Keywords:
Functional connectivityFunctional near infrared spectroscopy (fNIRS)Matlab toolboxMultimodalSemi-automated artifact correctionTask-based analysis

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Data Science

Background:

  • Functional near-infrared spectroscopy (fNIRS) is valuable for brain imaging in pediatric and challenging populations.
  • Standardized fNIRS data analysis methods are under continuous development and debate within the scientific community.

Purpose of the Study:

  • Introduce LIONirs, a novel neuroinformatics toolbox for fNIRS data analysis.
  • Provide a flexible and simple platform for exploring various analysis methods and processing large datasets.

Main Methods:

  • LIONirs features a graphical user interface (DisplayGUI) for artifact correction and 3D visualization of fNIRS data.
  • The toolbox supports multimodal data integration (EEG, physiology, audio-video) and advanced decomposition methods.
  • It is compatible with existing tools and supports common data formats, facilitating integration into current research workflows.

Main Results:

  • Users can visualize topographical projections of fNIRS data onto 3D head models.
  • Identification of relevant signatures, including brain responses and artifacts, is enabled through data decomposition.
  • The toolbox facilitates preprocessing and analysis of fNIRS data, including task-based and functional connectivity measures.

Conclusions:

  • LIONirs provides a versatile platform for both basic and advanced fNIRS data analysis.
  • Demonstrated utility through real experimental examples, highlighting its practical application in research.