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A Novel Grasping Robot Control Method Using Motion Execution BCI Combining Knowledge Reasoning.

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    This study introduces a shared control system combining a precise hand movement brain-computer interface (BCI) with knowledge-driven reasoning. This intelligent system enhances robotic grasping capabilities for individuals with disabilities.

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    Area of Science:

    • Neuroscience and Robotics
    • Human-Computer Interaction

    Background:

    • Growing need for intelligent assistive technologies for disabled individuals.
    • Limitations of conventional brain-controlled systems in real-world applications.
    • Conventional systems lack intelligence and versatility in object selection tasks.

    Purpose of the Study:

    • To develop an intelligent shared control system integrating a precise hand movement (PHM)-based brain-computer interface (BCI) with knowledge-driven reasoning.
    • To enhance the intelligence and versatility of robotic grasping for assistive purposes.
    • To improve the real-world applicability of brain-controlled systems.

    Main Methods:

    • Designed a novel motion execution paradigm for BCI using six precise hand movements.
    • Employed a feature intermediate fusion convolutional neural network for electroencephalogram decoding.
    • Developed a shared control grasping technology combining PHM-BCI with knowledge-based reasoning for robotic grasping.

    Main Results:

    • Achieved an average accuracy of 82.80±6.08% in decoding brain signals.
    • Reached a highest accuracy of 94.27% in the proposed system.
    • Demonstrated enhanced intelligence and versatility in object selection for robotic grasping.

    Conclusions:

    • The proposed shared control system effectively integrates PHM-BCI and knowledge-driven reasoning.
    • The system significantly improves the intelligence and versatility of robotic grasping.
    • Experimental results validate the effectiveness of the novel shared control method for assistive technology.