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Deep learning in fNIRS: a review.

Condell Eastmond¹, Aseem Subedi¹, Suvranu De¹

¹Center for Modeling, Simulation and Imaging for Medicine, Rensselaer Polytechnic, Department of Biomedical Engineering, Troy, New York, United States.

|July 25, 2022

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Deep learning (DL) significantly enhances functional near-infrared spectroscopy (fNIRS) brain imaging by improving data processing and classification accuracy. DL methods overcome challenges like small sample sizes and lengthy preprocessing, offering comparable or better results than traditional techniques.

Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Optical neuroimaging, particularly functional near-infrared spectroscopy (fNIRS), is crucial for monitoring brain activity.
fNIRS study outcomes are highly sensitive to data processing and classification models.
Deep learning (DL) shows promise for efficient and accurate biomedical data analysis.

Purpose of the Study:

To review the application of emerging DL techniques in fNIRS studies.
To summarize current DL research in key fNIRS application areas.
To highlight DL's potential to address fNIRS data challenges.

Main Methods:

Introduction to common DL techniques.
Systematic review of 63 relevant research papers on DL in fNIRS.

Keywords:

biophotonics brain–machine interface data processing functional near-infrared spectroscopy real-time imaging

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Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

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Last Updated: Sep 3, 2025

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

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Published on: July 21, 2021

fMRI Validation of fNIRS Measurements During a Naturalistic Task

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Published on: June 15, 2015

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

Categorization of DL applications into brain-computer interface, neuro-impairment diagnosis, and neuroscience discovery.

Main Results:

DL techniques outperformed traditional machine learning in 26 out of 32 comparative studies.
DL was used to reduce data preprocessing time and augment data in eight studies.
DL demonstrated improved classification accuracy in fNIRS data analysis.

Conclusions:

DL effectively addresses common fNIRS study limitations, including extensive preprocessing and small datasets.
DL application in fNIRS research leads to comparable or enhanced classification accuracy.
DL represents a significant advancement for fNIRS data analysis and application.