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监禁240天后大脑功能的评估,使用功能近红外光谱学.

Fares Al-Shargie1, Usman Tariq2, Saleh Al-Ameri3

  • 1Rutgers University New Brunswick NJ 07102 USA.

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|November 20, 2024
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概括
此摘要是机器生成的。

在长时间的太空任务中检测宇航员的心理压力至关重要. 功能近红外光谱 (fNIRS) 与机器学习相结合,准确量化压力水平,为宇航员福祉提供早期检测.

关键词:
精神上的压力是心理上的压力.阿尔法氨基酶是什么?大脑的连接性大脑的连接性在FNIRS中使用.隔离隔离隔离隔离隔离机器学习是机器学习.

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科学领域:

  • 神经科学是一个神经科学.
  • 太空医学 太空医学
  • 机器学习 机器学习

背景情况:

  • 宇航员在长期太空任务中面临着严重的压力.
  • 早期发现心理压力对于任务成功和机组人员健康至关重要.
  • 目前监测宇航员精神压力的方法有限.

研究的目的:

  • 为了识别和量化宇航员在240天的囚禁场景中的心理压力水平.
  • 为了评估功能近红外光谱学 (fNIRS) 与机器学习相结合的应力评估的有效性.
  • 探索压力的各种生理和绩效指标.

主要方法:

  • 利用功能近红外光谱 (fNIRS) 来测量大脑活动.
  • 收集的压力指标:唾液α氨酶 (sAA),反应时间 (RT),目标检测精度,功率光谱密度 (PSD) 和功能连接网络 (FCN).
  • 用于PSD估计的快速福里埃转换 (FFT) 和用于FCN分析的部分定向连贯性.
  • 应用了五个机器学习分类器 (KNN,LDA,NB,DT,SVM) 来区分压力水平.

主要成果:

  • 在为期240天的任务中,唾液α氨基酶 (sAA) 水平在整个任务期间都增加了.
  • 反应时间 (RT) 和目标检测准确性显示出显著的波动.
  • 功率光谱密度 (PSD) 增加,而功能连接网络 (FCN) 在特定的大脑区域下降.
  • 机器学习模型实现了高准确率,支持矢量机 (SVM) 和k-近邻 (KNN) 超过96%.

结论:

  • fNIRS与机器学习相结合,提供了一种有效的方法来评估宇航员的心理压力.
  • 这项研究表明,在长时间的太空任务中,早期应激检测是一种有前途的方法.
  • 这项技术可以为宇航员健康监测和任务安全做出贡献.