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Brain-Controlled Wheeled Mobile Robots: A Framework Combining Probabilistic Brain-Computer Interface and Model

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    This study introduces a novel brain-controlled framework for mobile robots, enhancing control precision and efficiency. The system combines a probabilistic brain-computer interface (BCI) with a model predictive controller (MPC) for improved performance.

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    Area of Science:

    • Robotics
    • Neuroscience
    • Control Systems

    Background:

    • Brain-controlled systems, particularly those using electroencephalography (EEG), show promise but struggle with control precision and efficiency.
    • Existing brain-computer interface (BCI) applications often lack robust decision-making capabilities for complex tasks.

    Purpose of the Study:

    • To develop and evaluate a novel brain-controlled framework for a wheeled mobile robot (WMR).
    • To enhance control accuracy and efficiency in BCI applications by integrating a probabilistic BCI with a model predictive controller (MPC).

    Main Methods:

    • Developed a probabilistic BCI using the sigmoid fitting-filter bank canonical correlation analysis (SF-FBCCA) algorithm for EEG signal decoding.
    • Integrated an auxiliary MPC with adaptive cost function weights, determined by command probabilities, to assist BCI decision-making.
    • Conducted simulation-based evaluations using a WMR in a path-keeping scenario.

    Main Results:

    • The proposed framework significantly improved control accuracy and efficiency compared to direct brain control.
    • Demonstrated a 58.02% reduction in average lateral error and a 60.06% reduction in average yaw angle error.
    • Showcased further performance enhancements through the MPC's adaptive weight adjustments.

    Conclusions:

    • The combined probabilistic BCI and MPC framework offers a significant advancement for brain-controlled systems.
    • Provides a robust solution for improving precision and efficiency in BCI-based mobile robot control.
    • Offers valuable theoretical insights and technical references for future BCI control research.