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Related Experiment Video

Updated: Sep 1, 2025

fMRI Validation of fNIRS Measurements During a Naturalistic Task
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A Machine Learning Perspective on fNIRS Signal Quality Control Approaches.

Andrea Bizzego, Michelle Neoh, Giulio Gabrieli

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |August 11, 2022
    PubMed
    Summary

    Functional Near Infra-Red Spectroscopy (fNIRS) signal processing lacks standardization. This study shows machine learning, particularly Convolutional Neural Networks, offers a more objective and automated approach to signal quality control for fNIRS data.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Functional Near Infra-Red Spectroscopy (fNIRS) is increasingly used to study neural systems.
    • Current fNIRS signal processing lacks standardization, relying on empirical and manual procedures.
    • Signal Quality Control (SQC) is crucial for reliable fNIRS analysis, but current methods using empirical thresholds on Signal Quality Indicators (SQIs) have limitations.

    Purpose of the Study:

    • To investigate the limitations of current Signal Quality Control (SQC) practices in fNIRS analysis.
    • To explore the potential of Deep Learning approaches for improving fNIRS SQC.
    • To develop a more objective, automated, and standardized SQC for fNIRS data.

    Main Methods:

    • Utilized a dataset of 1,340 fNIRS signals from 67 subjects.
    • Manually labeled the signal quality of 548 signal segments.
    • Compared empirical thresholding of SQIs with conventional machine learning models and Convolutional Neural Networks (CNNs).

    Main Results:

    • While SQIs statistically differentiate poor-quality signals, empirical thresholding shows limited sensitivity.
    • Conventional machine learning models based on SQIs demonstrate improved accuracy over manual thresholding.
    • End-to-end deep learning approaches, specifically CNNs, further enhance performance in fNIRS SQC.

    Conclusions:

    • Current empirical methods for fNIRS SQC are suboptimal.
    • Machine learning, especially deep learning with CNNs, offers a more objective and accurate approach to fNIRS SQC.
    • The proposed machine learning-based approach paves the way for standardized and automated fNIRS data processing.