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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Neural signals are crucial for decision-making across various fields.
    • Low signal-to-noise ratio (SNR) and artifacts, like motion artifacts in electroencephalography (EEG), complicate signal interpretation.
    • Existing denoising tools are often limited to offline processing, lacking real-time capabilities.

    Purpose of the Study:

    • To develop high-performance, reliable, and real-time methods for neural signal artifact handling.
    • To address the challenge of denoising complex artifacts, including EEG motion artifacts.
    • To establish a unified framework for neural data artifact denoising compatible with real-time applications.

    Main Methods:

    • Development of novel sample-adaptive processing techniques.
    • Application of a core processing tool to diverse artifact types.
    • Focus on real-time processing capabilities for neural data.

    Main Results:

    • Proposed methods effectively handle challenging neural signal artifacts, including motion artifacts.
    • Demonstrated the potential for a unified artifact denoising framework.
    • Achieved real-time processing compatibility for neural data artifact removal.

    Conclusions:

    • Novel methods offer a promising solution for real-time neural signal artifact denoising.
    • The unified framework approach simplifies and enhances artifact management in neural data.
    • This work addresses a critical need for effective, real-time artifact removal in neuroscience and medicine.