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fMRI Validation of fNIRS Measurements During a Naturalistic Task
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Rethinking Delayed Hemodynamic Responses for fNIRS Classification.

Zenghui Wang, Jihong Fang, Jun Zhang

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |November 7, 2023
    PubMed
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    A new model, fNIRSNet, enhances brain-computer interface (BCI) performance by incorporating domain knowledge of hemodynamic delays into functional near-infrared spectroscopy (fNIRS) signal classification. This efficient model outperforms existing deep learning methods, reducing computational costs for practical BCI applications.

    Area of Science:

    • Neuroimaging
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique.
    • Improving fNIRS signal classification is crucial for advancing brain-computer interfaces (BCIs).
    • Current deep neural networks (DNNs) often overlook the inherent delays in fNIRS hemodynamic responses, leading to performance limitations.

    Purpose of the Study:

    • To introduce a novel, efficient deep learning model, fNIRSNet, for fNIRS signal classification.
    • To integrate domain knowledge of delayed hemodynamic responses into the model architecture.
    • To enhance the performance and practicality of fNIRS-based BCIs.

    Main Methods:

    • Developed fNIRSNet, a concise deep learning model that incorporates delayed hemodynamic responses as domain knowledge.

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  • Utilized kernel size and receptive field considerations within convolutional layers.
  • Established three empirical design guidelines for fNIRSNet.
  • Evaluated the model on open-access datasets using subject-specific and subject-independent experimental designs.

    • Main Results:

    • fNIRSNet demonstrated superior classification performance compared to other DNNs on benchmark fNIRS datasets.
    • On mental arithmetic tasks, fNIRSNet achieved a 6.58% higher accuracy than a CNN with millions of parameters, despite having only 498 parameters.
    • fNIRSNet exhibited significantly lower floating-point operations (FLOPs) than traditional CNNs.

    Conclusions:

    • fNIRSNet offers an efficient and effective approach for fNIRS signal classification, outperforming existing DNNs.
    • The model's reduced parameter count and computational load make it suitable for practical, low-cost BCI applications.
    • This knowledge-driven approach has the potential to inspire further research in developing advanced fNIRS BCIs.