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P3DL:一个保护隐私的个性化分布式学习框架,用于基于EEG的认知状态识别.

Yu Ouyang, Wenjie Cheng, Lizhi Wang

    IEEE journal of biomedical and health informatics
    |October 9, 2025
    PubMed
    概括

    这项研究引入了一个保护隐私的框架,用于使用电脑电图 (EEG) 识别老年人的认知状态. 新方法提高了准确性,同时保护了敏感的大脑数据.

    科学领域:

    • 神经科学是一个神经科学.
    • 人工智能的人工智能
    • 医疗保健技术 技术 医疗保健 技术

    背景情况:

    • 电脑电图 (EEG) 对于识别老年人认知能力下降至关重要.
    • EEG数据包含敏感的个人信息,构成隐私风险.
    • 目前的方法优先考虑精确性,而不是EEG数据隐私.

    研究的目的:

    • 为基于EEG的认知状态识别开发一个保护隐私的个性化分布式学习框架 (P3DL).
    • 提高老年人认知评估的准确性和隐私性.

    主要方法:

    • 提出了一个保护隐私的个性化分布式学习框架 (P3DL),其中包含客户端和中央服务器.
    • 实施了一个联合动态更新策略 (FedDBS) 用于模型优化.
    • 引入了一个新的损失函数,极端错误损失 (E2Loss),以改善识别和误诊评估.

    主要成果:

    • 在临床和公共数据集上,P3DL显示F2Score的平均增加分别为5.58%和3.31%.
    • 在各自的数据集上,准确度提高了1.78%和2.46%.
    • 在情绪识别任务中证实了框架可扩展性.

    结论:

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    • P3DL框架有效地提高了认知状态识别的准确性.
    • P3DL确保了敏感的EEG数据的隐私保护.
    • 这项工作为使用EEG的安全可靠的医疗保健应用开辟了新的途径.