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在线隐私保护EEG分类由源代码免费转移学习

Hanrui Wu, Zhengyan Ma, Zhenpeng Guo

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
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    概括
    此摘要是机器生成的。

    本研究介绍了私人EEG分类的在线无源传输学习 (OSFTL). 通过OSFTL,可以在没有新的受试者数据的情况下快速使用脑计算机接口 (BCI),从而增强隐私和可转移性.

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    科学领域:

    • 神经科学和机器学习
    • 大脑与计算机接口 (BCI) 技术

    背景情况:

    • 电脑电图 (EEG) 信号对于BCI应用至关重要,转移学习提高了新用户的适应能力.
    • 电脑脑脑电图数据的隐私是一个重大问题,目前的方法往往需要新的受试者数据进行调整.
    • 实际的BCI部署受到广泛数据收集和对新用户进行校准的需要的阻碍.

    研究的目的:

    • 提出使用转移学习进行EEG分类的隐私保护方法.
    • 为了应对在实际的BCI场景中有限或没有新的受试者数据可用性的挑战.
    • 为了使BCI能够快速使用,同时保护敏感的EEG数据.

    主要方法:

    • 引入了在线源代码免费转移学习 (OSFTL),具有不同的线下和在线阶段.
    • 离线阶段:从多个主体中提取源模型参数,保持源数据的隐私.
    • 在线阶段:通过连续的EEG数据训练目标分类器,并结合源/目标分类器,动态调整权重以实现最佳可转移性.

    主要成果:

    • 在保持隐私的同时,OSFTL有效地对EEG信号进行分类.
    • 该方法在模拟和真实世界BCI应用中表现出强的性能.
    • 动态权重调整改善了知识从源到目标领域的可转移性.

    结论:

    • 在BCI中,OSFTL提供了一种有效的解决方案,用于在BCI中维护隐私的EEG分类.
    • 该方法通过消除对新受试者数据收集的需求,促进了BCI的快速部署.
    • OSFTL显示出在控制实验室环境之外推进BCI应用的巨大潜力.