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Updated: Dec 29, 2025

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Enhancing fNIRS Analysis Using EEG Rhythmic Signatures: An EEG-Informed fNIRS Analysis Study.

Rihui Li, Chunli Zhao, Chushan Wang

    IEEE Transactions on Bio-Medical Engineering
    |February 8, 2020
    PubMed
    Summary

    This study enhances neuroimaging by integrating Electroencephalography (EEG) with functional near-infrared spectroscopy (fNIRS). EEG-informed fNIRS analysis improves the localization of brain activity, offering new insights into neurovascular coupling.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Cognitive Science

    Background:

    • Neurovascular coupling links neuronal activity and brain blood flow.
    • Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offer complementary brain imaging data.
    • Current methods for analyzing combined EEG and fNIRS data can be improved.

    Purpose of the Study:

    • To develop an EEG-informed analysis framework for fNIRS data.
    • To investigate the neuro-correlates between neuronal activity and cerebral hemodynamics.
    • To enhance the accuracy of fNIRS-based general linear model (GLM) analysis.

    Main Methods:

    • Developed an EEG-informed fNIRS analysis framework.
    • Utilized frequency-specific EEG rhythmic modulations as regressors in GLM analysis.

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  • Applied the framework to fNIRS data collected during a hand grasp task.

    • Main Results:

    • EEG-informed fNIRS GLM analysis showed higher sensitivity and specificity in localizing task-evoked regions compared to the standard boxcar model.
    • Alpha and beta EEG bands were particularly effective in improving fNIRS analysis.
    • Analysis using deoxygenated hemoglobin (HbR) signals showed slightly better performance than oxygenated hemoglobin (HbO).

    Conclusions:

    • The study validates the enhancement of fNIRS GLM analysis using simultaneous EEG recordings.
    • The findings provide a novel approach for studying neurovascular coupling.
    • This integrated neuroimaging technique offers a new perspective on brain activity analysis.