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深度源半监督转移学习 (DS3TL) 用于跨主题的EEG分类.

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    概括
    此摘要是机器生成的。

    深源半监督转移学习 (DS3TL) 减少了大脑计算机接口 (BCI) 中标记电脑图 (EEG) 数据的需求. 这种方法有效地使用未标记的源数据训练目标分类器,以更少的用户特定培训提高性能.

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    科学领域:

    • 神经科学是一个神经科学.
    • 计算机科学 计算机科学
    • 机器学习 机器学习

    背景情况:

    • 大脑-计算机接口 (BCI) 将脑电图 (EEG) 信号转化为设备命令.
    • 训练可靠的EEG识别模型通常需要大量的标记数据,这耗时且不友好.
    • 半监督学习 (SSL) 和转移学习提供了通过使用未标记或辅助数据来减少对标记数据的依赖的策略.

    研究的目的:

    • 为基于EEG的BCI提出深度源半监督转移学习 (DS3TL).
    • 为了减少训练新受试者的可靠EEG识别模型所需的标记数据的数量.
    • 为了提高分类员培训,利用来自源主体的未标记数据和来自目标主体的未标记数据.

    主要方法:

    • DS3TL集成了一个混合SSL模块 (伪标签和一致性规范化),一个弱监督的对比模块 (使用true和伪标签),以及一个域调整模块 (减少不确定性).
    • 源主体有少量标记的和大量未标记的EEG试验.
    • 所有目标受试者的EEG试验都没有标记.

    主要成果:

    • DS3TL的表现优于使用标记训练数据的监督学习基线,该基线使用了更多标记训练数据.
    • 当使用相同数量的标记数据时,DS3TL与最先进的SSL方法相比表现出更高的性能.
    • 实验对来自不同任务的三个不同的EEG数据集进行了实验.

    结论:

    • DS3TL是基于EEG的BCI中的第一个方法,有效地利用未标记的源数据进行增强的目标分类器培训.
    • 拟议的方法大大降低了个人用户对数据标签的负担.
    • DS3TL为开发更高效,更易于使用的BCI提供了一个有前途的方向.