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Tracing Pilots’ Situation Assessment by Neuroadaptive Cognitive Modeling
Frontiers in Neuroscience ( IF 4.3 ) Pub Date : 2020-08-11 , DOI: 10.3389/fnins.2020.00795
Oliver W Klaproth 1, 2 , Christoph Vernaleken 3 , Laurens R Krol 4 , Marc Halbruegge 2 , Thorsten O Zander 4, 5 , Nele Russwinkel 2
Affiliation  

This study presents the integration of a passive brain-computer interface (pBCI) and cognitive modeling as a method to trace pilots’ perception and processing of auditory alerts and messages during operations. Missing alerts on the flight deck can result in out-of-the-loop problems that can lead to accidents. By tracing pilots’ perception and responses to alerts, cognitive assistance can be provided based on individual needs to ensure they maintain adequate situation awareness. Data from 24 participating aircrew in a simulated flight study that included multiple alerts and air traffic control messages in single pilot setup are presented. A classifier was trained to identify pilots’ neurophysiological reactions to alerts and messages from participants’ electroencephalogram (EEG). A neuroadaptive ACT-R model using EEG data was compared to a conventional normative model regarding accuracy in representing individual pilots. Results show that passive BCI can distinguish between alerts that are processed by the pilot as task-relevant or irrelevant in the cockpit based on the recorded EEG. The neuroadaptive model’s integration of this data resulted in significantly higher performance of 87% overall accuracy in representing individual pilots’ responses to alerts and messages compared to 72% accuracy of a normative model that did not consider EEG data. We conclude that neuroadaptive technology allows for implicit measurement and tracing of pilots’ perception and processing of alerts on the flight deck. Careful handling of uncertainties inherent to passive BCI and cognitive modeling shows how the representation of pilot cognitive states can be improved iteratively for providing assistance.

中文翻译:

通过神经适应性认知建模追踪飞行员的情况评估

这项研究提出了被动脑机接口 (pBCI) 和认知建模的集成,作为一种跟踪飞行员在操作过程中对听觉警报和消息的感知和处理的方法。驾驶舱上缺少警报可能会导致失环问题,从而导致事故。通过追踪飞行员对警报的感知和反应,可以根据个人需求提供认知帮助,以确保他们保持足够的情境意识。介绍了模拟飞行研究中 24 名参与机组人员的数据,该研究包括单个飞行员设置中的多个警报和空中交通管制消息。训练分类器来识别飞行员对来自参与者脑电图 (EEG) 的警报和消息的神经生理反应。将使用 EEG 数据的神经适应性 ACT-R 模型与关于代表单个飞行员的准确性的传统规范模型进行了比较。结果表明,被动 BCI 可以根据记录的 EEG 将飞行员处理的警报区分为与任务相关或与驾驶舱无关。与未考虑 EEG 数据的规范模型的 72% 准确率相比,神经适应模型对这些数据的整合在表示单个飞行员对警报和消息的响应方面的总体准确率显着提高了 87%。我们得出结论,神经自适应技术允许隐式测量和跟踪飞行员对驾驶舱警报的感知和处理。
更新日期:2020-08-11
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