LIONirs: flexible Matlab toolbox for fNIRS data analysis

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.