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    此摘要是机器生成的。

    这项研究介绍了一个共享控制系统,它结合了精确的手动脑电脑接口 (BCI) 与基于知识的推理. 这种智能系统增强了残疾人的机器人抓取能力.

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    科学领域:

    • 神经科学和机器人技术
    • 人与计算机的交互

    背景情况:

    • 对残疾人智能辅助技术的需求日益增长.
    • 常规的大脑控制系统在现实世界的应用中的局限性.
    • 传统系统在对象选择任务中缺乏智能和多功能性.

    研究的目的:

    • 开发一个智能共享控制系统,将基于精确的手动 (PHM) 的脑计算机接口 (BCI) 与基于知识的推理集成在一起.
    • 为了提高机器人掌握的智能和多功能性,用于辅助目的.
    • 为了提高大脑控制系统的现实世界的适用性.

    主要方法:

    • 为BCI设计了一种新的运动执行范式,使用六种精确的手动.
    • 采用一个特征中间融合卷积神经网络用于电脑图解码.
    • 开发了一个共享的控制掌握技术,将PHM-BCI与机器人掌握的基于知识的推理结合起来.

    主要成果:

    • 在解码大脑信号方面获得了平均准确度为82.80±6.08%.
    • 在拟议的系统中达到94.27%的最高准确率.
    • 在对象选择中表现出增强的智能和多功能性,用于机器人抓取.

    结论:

    • 拟议的共享控制系统有效地整合了PHM-BCI和基于知识的推理.
    • 该系统显著提高了机器人掌握的智能和多功能性.
    • 实验结果验证了辅助技术新型共享控制方法的有效性.