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在模拟飞行任务中认知负载识别:一项EEG研究

Yueying Zhou1,2, Xijia Xu3, Daoqiang Zhang2,4

  • 1School of Mathematics Science, Liaocheng University, Liaocheng, China.

Frontiers in human neuroscience
|March 20, 2025
PubMed
概括

使用模拟飞行中的EEG数据进行认知负载识别,表明浅CNN的表现最好. 准确性随着时间的推移而下降,突出了复杂任务中适应性系统的需要.

关键词:
大脑与计算机接口 (BCI)认知负载识别认知负载识别卷积神经网络是一种卷积神经网络.电脑电图 (EEG) 是一个电脑电图.模拟飞行飞行的模拟飞行

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科学领域:

  • 神经科学是一个神经科学.
  • 人与计算机的交互
  • 航空航天工程 航空航天工程

背景情况:

  • 使用脑电图 (EEG) 的认知负载识别 (CLR) 正在进步.
  • 现有的方法使用简单的任务,限制现实世界的适用性.
  • 模拟飞行任务为CLR研究提供了更具操作性的环境.

研究的目的:

  • 研究模拟飞行任务期间认知负载的时间动态.
  • 评估深层卷积神经网络 (CNN) 模型在复杂任务中对CLR的有效性.
  • 探索疲劳和任务进展对基于EEG的CLR的影响.

主要方法:

  • 36名参与者在低,中,高认知负载水平下执行模拟飞行任务.
  • 在三个会议中收集了EEG数据,主观评分和绩效指标.
  • 多个深度CNN模型应用于用于认知负载分类的原始EEG数据.

主要成果:

  • 在休息状态和疲劳后EEG数据之间观察到显著的差异.
  • 浅薄的CNN模型表现优于更复杂的CNN架构.
  • 随着模拟任务的进展,CLR性能下降.

结论:

  • 模拟飞行任务为研究运营环境中的认知负载提供了有价值的范式.
  • 浅层CNN在复杂,动态环境中显示出实时CLR的前景.
  • 时间动态对于理解长时间任务中的认知状态至关重要.