Biosignal-Based Attention Monitoring to Support Nuclear Operator Safety-Relevant Tasks.

Jung Hwan Kim,Chul Min Kim,Man-Sung Yim,Eun-Soo Jung

doi:10.3389/fncom.2020.596531

Abstract

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.

Highlights

Attention is an important cognitive resource for information processing directly affecting the quality of task performance (Wickens et al, 1998)
As main control room (MCR) operators perform cognitive activities by using information obtained through visual channels, we evaluated the use of both EEG signals and eye movements to establish a robust baseline to determine the plausibility of developing an attention monitoring system in this paper
The cases with incorrect responses to the first question of the checklist were labeled as the absence of attention (AoA) class by assuming that the wrong answer was due to a lack of attention

Summary

Introduction

Attention is an important cognitive resource for information processing directly affecting the quality of task performance (Wickens et al, 1998). According to the Nuclear Event Evaluation Database (NEED), a database developed by Korea Institute of Nuclear Safety (KINS), approximately 20% of the unplanned nuclear power plant (NPP) shutdowns between 2000 and 2011 in Korea were due to human errors (Lee et al, 2017). Biosignal-Based Attention Monitoring accounted for more than 26% of the total cause of human errors, which takes the biggest portion. The decreased attention of an NPP main control room (MCR) operator could lead to a decrease in their situational awareness, which could result in a poor reactor operating performance and cause critical human errors. Developed NPP designs include fully digitalized instrumentation and control (I&C). These upgrades are expected to enhance plant and operator performance. Advanced MCRs based on digital I&C technology create a completely different operating environment from the existing MCR configurations (Choi et al, 2019)

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Methods

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Discussion

Conclusion