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基准测试基于EEG的交叉数据集驾驶员昏昏欲睡的识别与深度转移学习

Jian Cui, Liqiang Yuan, Ruilin Li

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
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    收集用于驾驶员监控的脑电图 (EEG) 数据是耗时的. 本研究引入了一个深度转移学习模型,用于使用现有的EEG数据集准确,无校准的驾驶员昏昏欲睡的识别.

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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
    • 运输安全运输安全

    背景情况:

    • 电脑电图 (EEG) 对于驾驶员昏昏欲睡的监测至关重要.
    • 收集EEG校准数据是耗时的.
    • 跨数据集的识别是必要的,以减少校准时间,但面临着分布偏移的挑战.

    研究的目的:

    • 开发一个深度转移学习模型,以有效地跨数据集识别驾驶员的昏昏欲睡.
    • 解决来自不同环境的EEG数据集中的分布偏移问题.
    • 为了实现无校准的驾驶员昏昏欲睡的检测.

    主要方法:

    • 提出了以率驱动的联合适应网络 (EDJAN),这是一个深度转移学习模型.
    • 利用驱动的损失函数用于表示集群.
    • 实施个人级域调整技术,以减轻分布差异.

    主要成果:

    • 以SADT作为来源和SEED-VIG作为目标域实现了83.3%的准确性.
    • 获得了76.7%的准确性,以SEED-VIG作为来源和SADT作为目标域.
    • 在交叉数据集识别任务中超越了最先进的 (SOTA) 方法.

    结论:

    • 在交叉数据集EEG分析中,EDJAN模型有效地处理分布偏移.
    • 拟议的方法允许准确,无校准的驾驶员昏昏欲睡的识别.
    • 这项研究为驾驶员安全的实际EEG应用开辟了道路.