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使用卷积神经网络进行EEG源分析和有限元分析.

Thanos Delatolas, Marios Antonakakis, Carsten H Wolters

    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

    这项研究引入了一种新的卷积神经网络 (CNN),用于脑电图 (EEG) 脑活动重建. 在现实的头部模型上训练的CNN准确地定位了大脑活动,并显示了现实世界应用的潜力.

    科学领域:

    • 神经科学是一个神经科学.
    • 生物医学工程 生物医学工程
    • 计算神经科学是一种神经科学.

    背景情况:

    • 对电生理学大脑活动的源分析涉及解决错误的反向问题.
    • 现有的算法通常依赖于次优向前建模来训练神经网络.
    • 需要改进电脑图 (EEG) 源分析的方法.

    研究的目的:

    • 提出和评估一种新的卷积神经网络 (CNN),用于重建EEG大脑活动.
    • 为了解决目前用于EEG源分析的神经网络培训的局限性.
    • 为了证明CNN训练有现实的头部模型的有效性.

    主要方法:

    • 开发了一个用于EEG源分析的CNN架构.
    • 采用了对头骨导电性进行校准的,白质异性质头部模型来生成模拟的EEG数据.
    • 使用生成的模拟EEG数据训练了CNN.
    • 在模拟数据和真实世界体感官唤起的潜在实验上评估了CNN的表现.

    主要成果:

    • 美国有线电视新闻网成功重建了EEG脑活动.
    • 在Brodmann区域3b实现了P20/N20组件的精确定位.
    • 美国有线电视新闻网 (CNN) 展示了本地化更深层次的大脑来源的潜力.

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  • 定位性能与已建立的方法 (如单双极扫描和sLORETA) 相当.

    • 结论:

    • 拟议的CNN为EEG源分析提供了一个有前途的方法.
    • 现实的头部建模对于在这个领域有效训练神经网络至关重要.
    • 美国有线电视新闻网 (CNN) 显示了大脑活动重建中的现实世界临床和研究应用的潜力.