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以EEG为基础的交叉数据集驱动器嗜睡识别与度优化网络

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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:

    • 使用脑电图 (EEG) 识别驾驶员的昏昏欲睡,对于开发先进的驾驶辅助系统至关重要.
    • 无校准系统是非常理想的,但由于分布在数据集上的漂移而面临挑战.
    • 当前的方法在应用于新的,未见的数据分布时,难以保持准确性.

    研究的目的:

    • 提出一种新的模型,即优化网络 (EON),用于跨数据集的驾驶员昏昏欲睡的识别.
    • 为了应对基于EEG的嗜睡检测中分布偏移的挑战.
    • 推进无校准驾驶员昏昏欲睡识别系统的开发.

    主要方法:

    • 采用两步策略,有效地分离未标记的目标域数据.
    • 一个修改后的损失函数被用于集群源域对齐的未标记样本.
    • 一个自我训练框架通过利用固有的目标域模式,逐渐完善样本分离.

    主要成果:

    • 拟议的EON模型使用两个公共数据集在域调整任务上实现了高二类识别准确性.
    • 该方法在跨数据集驾驶员昏昏欲睡的识别方面显著优于现有的基线方法.
    • 在减轻分布偏移对识别性能影响方面已证明有效.

    结论:

    • 优化网络 (EON) 提供了一个有前途的方法,用于强大的,无校准的驾驶员昏昏欲睡的识别.
    • 拟议的方法有效地处理EEG数据中的分布转移,提高跨数据集的适用性.
    • 这项研究揭示了一条通向开发无需个别校准的普遍适用的驾驶员监控系统的可行途径.