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在两个数据集上使用机器学习技术的视频引起的EEG反应解码情绪.

Embla C S Neverlien, Rose Lu, Mohit Kumar

    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
    PubMed
    概括

    这项研究引入了基于脑电图 (EEG) 的情绪识别的简单架构,在公共数据集上实现了高精度. 这种方法可以帮助开发自动化心理健康监测工具.

    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:

    • 目前基于EEG的情绪识别方法通常依赖于复杂的深度学习模型,需要大量的计算资源.
    • 需要使用EEG信号来识别人类情绪的更容易获得和更少的资源密集型方法.

    研究的目的:

    • 基于EEG数据,提出和评估一种更简单的人类情绪识别的架构方法.
    • 为了比较直接从时代提取特征的性能与分解的大脑节奏.
    • 在公共EEG数据集上调查不同分类器和特征组合的有效性.

    主要方法:

    • 使用了两个公共数据集:SEED和DEAP.
    • 将EEG信号细分为1秒的时段,并将其分解为大脑节奏.
    • 从时代和大脑节奏直接计算特征,检查各种组合.
    • 使用不同的分类器,包括支持矢量机 (SVM) 和多层感知器 (MLP).
    • 应用了DEAP数据集的基线特征校正.

    主要成果:

    • 支持矢量机 (SVM) 在结合基线特征校正和时代分解时,在DEAP数据集上表现出卓越的性能.
    • 在DEAP数据集上,高与低价值的平均准确率为96.50%,高与低激发类的平均准确率为96.71%.

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  • 在SEED数据集上使用具有两个隐藏层的多层感知器 (MLP) 实现了最佳平均准确率86.89%.

    • 结论:

    • 一个更简单的基于EEG的情感识别架构可以实现高精度,为复杂的深度学习模型提供一个可行的替代方案.
    • 拟议的方法,特别是DEAP数据集上的SVM,显示了实际应用的巨大潜力.
    • 这项研究可以为开发用于初步医疗查的自动心理健康监测器铺平道路.