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ArmBCIsys:机器人手臂BCI系统与时间频率网络用于多对象抓取.

Feng Yu, Zhongrui Rao, Neng Chen

    IEEE transactions on neural networks and learning systems
    |June 23, 2025
    PubMed
    概括

    这项研究介绍了ArmBCIsys,一个使用新型网络 (DBFENet) 解码噪音EEG信号的脑电脑接口系统,用于机器人手臂控制. 该系统使残疾人能够执行复杂的掌握任务,增强辅助技术应用.

    科学领域:

    • * 神经科学是一门神经科学.
    • * 机器人技术 机器人技术
    • * * 信号处理 信号处理

    背景情况:

    • *脑电脑接口 (BCI) 为身体残疾的人提供了沟通道.
    • *解码脑电图 (EEG) 信号,特别是低信号噪声比 (SNR) 下,对于复杂的任务,如多物体抓取,具有挑战性.
    • * 现有的系统缺乏强大的解码算法和精确的视觉跟踪,以适用于现实世界的应用.

    研究的目的:

    • * 开发一个集成的机器人手臂系统 (ArmBCIsys) 用于使用噪声的EEG信号抓取多物体.
    • * 在低SNR条件下增强EEG信号解码稳定性.
    • * 改进视觉跟踪和细分,以可靠地抓住对象.

    主要方法:

    • * 提出了一种新型的双分支频率增强网络 (DBFENet),具有缩放时间卷积块 (STCB) 和滴度投影变压器 (DSPT) 用于EEG特征提取.
    • * 集成了一个视觉引导的抓取模块 (VisGraspSeg),使用微调的Mask2Former和多中枢交叉与联合 (IoU) 追踪.
    • *在自建和两个公共代码调制的视觉唤起潜力 (c-VEP) 数据集上验证了系统.

    主要成果:

    • *DBFENet在c-VEP数据集上实现了最先进的识别性能.

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  • * 集成的ArmBCIsys在动态环境中展示了稳定的多对象选择和自动抓取.
  • * 该系统有效地解码低SNREEG信号,以精确控制机器人手臂.

    • 结论:

    • *ArmBCIsys提供了一个强大的解决方案,可以通过BCI控制机器人手臂,即使有噪音的EEG数据.
    • *开发的DBFENet和VisGraspSeg模块显著提升了辅助机器人的BCI能力.
    • * 这项技术对医疗保健机器人,辅助设备和工业自动化具有重大前景.