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使用EEG和眼睛追踪的工作负载估计器.

Ivan Tashev, Christine Beauchene, R Michael Winters

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    概括
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    本研究介绍了一种使用脑电图和眼睛追踪用于虚拟现实飞行模拟器的新型工作负载估计器. 该系统实现了与客观飞行数据的高相关性,使适应性训练成为可能.

    科学领域:

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

    背景情况:

    • 工作负载估计对于复杂环境中的自适应训练系统至关重要,例如飞行模拟器.
    • 现有的方法往往缺乏个人和会话独立性,限制了它们在现实世界中的适用性.
    • 对工作负载的客观评估具有挑战性,需要先进的估计技术.

    研究的目的:

    • 为虚拟现实飞行模拟器开发和评估一个独立于人和会话的工作负载估计器.
    • 将生物信号 (脑电图,眼睛追踪) 整合到适应性训练系统中.
    • 使用客观的飞行日志数据进行培训和验证工作负载估计器.

    主要方法:

    • 使用脑电图 (EEG) 和眼睛跟踪数据作为生物信号.
    • 开发了一个回归神经网络,通过从飞行日志中获得的客观工作负载标签进行训练.
    • 实施了一个独立于个人和会话的估计方法.
    • 使用与客观标签的相关性评估估计器性能.

    主要成果:

    • 性能最好的工作负载估计器与客观飞行日志标签的相关性为0.84.
    • 在VR飞行模拟器中证明了使用生物信号用于工作负载估计的有效性.

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  • 开发的估计器旨在独立于个人和会话.

    • 结论:

    • 基于生物信号和客观标签的拟议工作负载估计器显示了适应性培训系统的重大前景.
    • 这种方法为评估虚拟现实环境中的飞行员工作量提供了一种可靠和可通用的方法.
    • 这些发现支持将此类估计器纳入未来的飞行训练范式.