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Brain-Controlled 2D Navigation Robot Based on a Spatial Gradient Controller and Predictive Environmental Coordinator.

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    Summary
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    This study introduces a spatial gradient brain-computer interface (BCI) controller for 2D navigation. This new BCI system enhances control flexibility and efficiency by transferring quantified brain commands as 2D vectors.

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    Area of Science:

    • Neuroscience
    • Robotics
    • Human-Computer Interaction

    Background:

    • Current brain-computer interfaces (BCIs) for 2D robotic navigation, like wheelchairs and vehicles, primarily use binary selective control.
    • This binary control limits information transfer to directional commands (e.g., 'turn left/right'), preventing control over quantified values like turning radius.
    • This limitation reduces the flexibility, stability, and efficiency of existing brain-controlled navigation systems.

    Purpose of the Study:

    • To propose a novel spatial gradient BCI (SGC) controller and environment coordinator for 2D robotic navigation.
    • To enable the transfer of quantified brain commands as 2D vectors, overcoming the limitations of binary control.
    • To enhance the flexibility, stability, and efficiency of brain-computer interfaces in robotic navigation applications.

    Main Methods:

    • A horizontal array of steady-state visual stimulation was used to elicit electroencephalogram (EEG) signals from subjects.
    • Covariance between EEG signals and stimulation features was mapped into quantified 2D vectors.
    • These vectors were inputted into a predictive controller, fused with virtual forces from a predictive environment coordinator, and translated into robot driving commands with real-time speed feedback.

    Main Results:

    • The SGC controller demonstrated a faster response time (27.4s vs. 34.9s) in single-obstacle avoidance tasks compared to selective control.
    • In multi-obstacle tasks, the SGC-controlled robot achieved target reaching 39% faster than the selective controller.
    • The SGC approach significantly improved robustness in multi-obstacle avoidance, reducing average failures from 27% to 4%.

    Conclusions:

    • A new brain-machine shared control strategy was developed, quantifying brain commands as a 2D vector stream.
    • Integration with a predictive environment coordinator optimizes robot control, offering superior flexibility and performance.
    • The proposed controller is suitable for various brain-controlled 2D navigation devices, including wheelchairs and vehicles.