Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

Frontiers in Human Neuroscience
Alexander von LühmannMeryem A Yücel

Abstract

Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to bett...Continue Reading

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Citations

Dec 19, 2020·Translational Neuroscience·Patil AshleshKochhar Kanwal Preet
Feb 6, 2021·NeuroImage. Clinical·Surjo R SoekadarAlexander von Lühmann
Nov 3, 2020·Frontiers in Neuroscience·Alexander von LühmannMeryem A Yücel
Mar 13, 2021·Current Opinion in Biomedical Engineering·Alexander von LühmannMeryem A Yücel

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Methods Mentioned

BETA
imaging technique
feature extraction
GAM

Software Mentioned

HOMER2 fNIRS toolbox
HOMER2
HOMER
AFNI
MATLAB
SMAR

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