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预测一致性和基于信任的代理域建设,用于在跨主体EEG分类中保护隐私.

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    本研究引入了用于EEG分类的保护隐私的域调整方法. 这种新的方法使用代理域来传输知识,而无需访问敏感的源数据,改进跨主题分析.

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    科学领域:

    • 神经科学是一个神经科学.
    • 机器学习 机器学习
    • 生物医学工程 生物医学工程

    背景情况:

    • 域调整对于跨主体脑电图 (EEG) 分类至关重要,解决主体间的变异性.
    • 现有的方法通常需要直接访问源域数据,这引发了隐私问题.
    • 没有标记的目标域数据在现实世界EEG应用中很常见.

    研究的目的:

    • 为跨学科的EEG分类提出一个保护隐私的域调整框架.
    • 开发一种方法,将知识从来源转移到目标领域,而无需访问原始源数据.
    • 为了减轻EEG分析中的跨主体变异性问题.

    主要方法:

    • 提出了一个新的框架,预测一致性和信心 (PDCC),以构建一个代理域.
    • 由源模型对目标数据的预测得出的代理域取代了直接的源数据访问.
    • 雇员对源模型的分散培训和数据增强/调整,以提高通用性.

    主要成果:

    • PDCC有效地将知识从源域转移到目标域,同时保持源数据的隐私.
    • 在四个基准EEG数据集上的实验结果显示PDCC的性能优于11种现有方法.
    • 代理域建设的有效性得到了广泛的验证.

    结论:

    • 在跨主题EEG分类中,PDCC为域调整提供了一个强大且保护隐私的解决方案.
    • 拟议的代理域有效地封装了源知识,而不影响数据隐私.
    • 这种方法显著提高了基于EEG的BCI系统在现实场景中的应用性.