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Related Concept Videos

Brain Imaging01:14

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
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A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and

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    A new real-time processing system for functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) enhances brain-computer interfaces (BCI) and neurofeedback (NFB). The system effectively corrects motion artifacts, enabling advanced brain monitoring for rehabilitation and assistive technologies.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) leverage real-time brain activity monitoring for diverse applications.
    • Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising non-invasive neuroimaging techniques for BCI/NFB.
    • Real-time processing of fNIRS/DOT data faces challenges in baseline calibration, motion artifact (MA) correction, and image reconstruction.

    Purpose of the Study:

    • To develop and validate a real-time processing system for fNIRS/DOT data.
    • To integrate advanced techniques for baseline calibration, MA correction, and 3D brain hemodynamics reconstruction.
    • To address the critical need for low-latency, high-channel processing in BCI and NFB applications.

    Main Methods:

    • A real-time fNIRS/DOT processing system integrating baseline calibration, a denoising autoencoder (DAE) for MA correction using a sliding window, and a pre-calculated inverse Jacobian matrix.
    • The DAE model was trained on an extensive high-density DOT (HD-DOT) dataset and validated on a motor imagery dataset with artificial MA.
    • The system was evaluated for its real-time processing capability across approximately 750 channels.

    Main Results:

    • The DAE-based MA correction demonstrated superior performance over traditional methods, evidenced by lower mean squared error and higher correlation with MA-free data.
    • The system achieved low latency processing, crucial for effective real-time BCI and NFB applications.
    • The system successfully processed approximately 750 channels in real-time, providing channel-wise oxygenation and 3D functional imaging.

    Conclusions:

    • The proposed real-time processing system effectively overcomes key challenges in fNIRS/DOT data analysis for BCI and NFB.
    • The DAE-based MA correction offers a robust and efficient solution for motion artifact removal in neuroimaging.
    • The system's high-channel, real-time capabilities support advanced applications in motor rehabilitation and assistive technology, particularly in movement-intensive scenarios.