In the main control room (MCR) of a nuclear power plant (NPP), the quality of an operator's performance can depend on their level of attention to the task. Insufficient operator attention accounted for more than 26% of the total causes of human errors and is the highest category for errors. It is therefore necessary to check whether operators are sufficiently attentive either as supervisors or peers during reactor operation. Recently, digital control technologies have been introduced to the operating environment of an NPP MCR. These upgrades are expected to enhance plant and operator performance. At the same time, because personal computers are used in the advanced MCR, the operators perform more cognitive works than physical work. However, operators may not consciously check fellow operators' attention in this environment indicating potentially higher importance of the role of operator attention. Therefore, remote measurement of an operator's attention in real time would be a useful tool, providing feedback to supervisors. The objective of this study is to investigate the development of quantitative indicators that can identify an operator's attention, to diagnose or detect a lack of operator attention thus preventing potential human errors in advanced MCRs. To establish a robust baseline of operator attention, this study used two of the widely used biosignals: electroencephalography (EEG) and eye movement. We designed an experiment to collect EEG and eye movements of the subjects who were monitoring and diagnosing nuclear operator safety-relevant tasks. There was a statistically significant difference between biosignals with and without appropriate attention. Furthermore, an average classification accuracy of about 90% was obtained by the k-nearest neighbors and support vector machine classifiers with a few EEG and eye movements features. Potential applications of EEG and eye movement measures in monitoring and diagnosis tasks in an NPP MCR are also discussed.

中文翻译：

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基于生物信号的注意力监测支持核运营商安全相关任务


在核电厂 (NPP) 的主控制室 (MCR) 中，操作员的表现质量取决于他们对任务的关注程度。操作员注意力不足占人为错误总原因的 26% 以上，是错误的最高类别。因此，有必要检查操作员在反应堆运行期间作为监督者或同行是否足够专心。最近，数字控制技术已被引入核电厂MCR的操作环境中。这些升级预计将提高工厂和操作员的绩效。同时，由于先进的MCR中使用了个人计算机，操作人员进行的认知工作多于体力工作。然而，操作员可能不会有意识地检查其他操作员在这种环境中的注意力，这表明操作员注意力的作用可能更重要。因此，实时远程测量操作员的注意力将是一个有用的工具，可以向主管人员提供反馈。本研究的目的是调查定量指标的开发，这些指标可以识别操作员的注意力，诊断或检测操作员注意力的缺乏，从而防止高级 MCR 中潜在的人为错误。为了建立操作员注意力的可靠基线，本研究使用了两种广泛使用的生物信号：脑电图 (EEG) 和眼球运动。我们设计了一项实验，收集监测和诊断核运营商安全相关任务的受试者的脑电图和眼球运动。有和没有适当关注的生物信号之间存在统计学上的显着差异。 此外，通过具有一些脑电图和眼动特征的 k 最近邻和支持向量机分类器获得了约 90% 的平均分类精度。还讨论了脑电图和眼动测量在 NPP MCR 监测和诊断任务中的潜在应用。