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基于EEG信号的空中交通管制员工作负载检测

Quan Shao1, Hui Li1, Zhe Sun1

  • 1College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
概括
此摘要是机器生成的。

新的脑电图 (EEG) 马波指示器可以准确地检测空中交通管制员的认知工作量. 这些新的马波指标显示出比传统的工作量评估方法更高的准确性.

关键词:
电脑电图 (EEG) 是一个电脑电图.空中交通管制员是空中交通管制员机器学习是机器学习.工作负载的工作负载.

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科学领域:

  • 神经科学是一个神经科学.
  • 人类因素工程 人类因素工程
  • 认知心理学 认知心理学

背景情况:

  • 评估空中交通管制员的认知工作量对于安全和效率至关重要.
  • 现有的工作量评估方法在准确性和实时应用方面存在局限性.

研究的目的:

  • 引入新的电脑电图 (EEG) 马波相关指标,用于检测空中交通管制员的认知工作负载.
  • 将这些新指标的准确性与传统的工作量评估方法进行比较.
  • 探索最准确的指标的实际应用.

主要方法:

  • 在各种场景下收集了来自空中交通管制员的EEG和NASA-Task Load Index (NASA-TLX) 数据.
  • 使用统计测试 (沙皮罗-威尔克,ANOVA) 来分析工作负载数据差异.
  • 使用支持矢量机 (SVM) 分类来评估指示器检测准确度.
  • 应用了最小冗余最大相关性 (mRMR) 算法用于敏感通道选择.

主要成果:

  • 证实了玛波与空中交通管制员的工作量之间存在强烈的相关性.
  • 与传统指标相比,EEG马波指标显示出更高的检测精度.
  • 使用组合指示器 (β + θ + α + γ) 的单一敏感通道,实现了超过95%的识别率,符合实际应用要求.

结论:

  • 与EEG马波相关的指标为分析空中交通管制员工作量提供了一种有价值和准确的方法.
  • 开发的指标为现实世界工作负载监控提供了方便和精确的方法.
  • 这项研究促进了对高风险运营环境中的认知工作负载的理解和评估.