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A Real-Time Artifact Removal System for Closed-Loop Deep-Brain Stimulation.

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    This study introduces a dynamic template subtraction method for real-time removal of local field potential (LFP) artifacts during deep-brain stimulation (DBS). This innovation enables artifact-free LFPs crucial for closed-loop DBS systems.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Deep-brain stimulation (DBS) systems require artifact-free local field potentials (LFPs) for effective closed-loop operation.
    • Existing methods for artifact removal in LFPs during DBS face challenges in real-time precision and adaptability.

    Purpose of the Study:

    • To develop and validate a novel real-time signal processing method for removing local field potential (LFP) artifacts during deep-brain stimulation (DBS).
    • To enable artifact-free LFPs essential for the feedback mechanisms in closed-loop DBS systems.

    Main Methods:

    • A dynamic template subtraction method leveraging stimulation-sampling synchronization for real-time artifact removal.
    • Dynamic updating of artifact templates to adapt to changing stimulation artifacts.
    • Evaluation through simulations, in vitro experiments, and in vivo recordings.

    Main Results:

    • Simulations confirmed the theoretical feasibility of the method.
    • In vitro experiments demonstrated high accuracy in LFP recovery with minimal power spectral density errors (0.31-0.73%) across various stimulation frequencies (20-130 Hz).
    • In vivo evaluations successfully recorded artifact-free LFPs in real time, supporting beta-triggered closed-loop DBS and demonstrating efficacy with a commercial device at a sampling rate of 260 Hz.

    Conclusions:

    • The proposed dynamic template subtraction method effectively removes DBS-induced artifacts from LFPs in real time.
    • This technique supports the development of lightweight and robust closed-loop DBS systems by providing reliable, artifact-free neural signals.
    • The method's adaptability and efficiency at low sampling rates make it a valuable tool for advancing neuromodulation therapies.