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Updated: May 1, 2026

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Brain-Controlled Wheeled Mobile Robots: A Shared Control Framework Integrating Event-Triggered Mechanism and Deep

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    Summary
    This summary is machine-generated.

    This study introduces a novel brain-computer shared control system using Event-Triggered Control (ETC) and Deep Reinforcement Learning (DRL) for improved wheeled mobile robot (WMR) navigation. The DRL-based adaptive triggering significantly enhances path-tracking performance and user control authority.

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    Area of Science:

    • Robotics
    • Neuroscience
    • Control Systems

    Background:

    • Brain-computer interfaces (BCIs) offer intuitive control but face challenges in shared control systems.
    • Quantifying user control authority is crucial for effective human-robot collaboration.
    • Existing event-triggered control (ETC) mechanisms often lack adaptability.

    Purpose of the Study:

    • To develop and evaluate an adaptive ETC framework for BCI-based shared control of wheeled mobile robots (WMRs).
    • To quantify user control authority within the shared control system.
    • To improve path-tracking performance and reduce system intrusion compared to traditional methods.

    Main Methods:

    • Integration of Steady-State Visual Evoked Potential (SSVEP) BCI with an ETC framework for WMR control.
    • Development of a Deep Reinforcement Learning (DRL) based adaptive triggering strategy to replace fixed thresholds.
    • Implementation of a grouped training strategy to address inter-subject variability in SSVEP-BCI decoding.
    • Comparison with Fixed Threshold (FT) and Time-Triggered Shared Control (TTSC) baselines.

    Main Results:

    • The DRL-based ETC framework demonstrated improved path-tracking performance in brain-controlled WMRs.
    • Heading error was reduced by 32.34% and intrusion rate by 57.85% compared to the FT strategy.
    • Cumulative execution time was reduced by 82.38% compared to the TTSC baseline.
    • The system enabled explicit quantification of user control authority.

    Conclusions:

    • The proposed DRL-enhanced ETC framework effectively balances tracking performance, computational cost, and user control preservation.
    • Adaptive triggering via DRL offers superior adaptability and performance over fixed threshold methods in BCI shared control.
    • This approach represents a significant advancement in developing intuitive and efficient brain-controlled robotic systems.