一个新的掌握机器人控制方法使用动作执行BCI结合知识推理
IEEE journal of biomedical and health informatics
|October 16, 2025
概括
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.
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.
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