Development of an Information Security-Enforced EEG-Based Nuclear Operators’ Fitness for Duty Classification System

Abstract

In a nuclear power plant (NPP), operator performance is a critical to ensure safe operation of the plant. The fitness for duty (FFD) of the operators should be systematically assessed before they engage in duties related to reactor operations. This study proposes the use of an electroencephalography (EEG)-based deep learning algorithm to classify an operator's FFD. To determine the suitability of this approach, EEG data were collected during simple cognitive exercises designed to examine the mental readiness of nuclear operators. The EEG-based FFD classification system designed could successfully determine an operator's sobriety, stress, and fatigue in a timely and cost-effective manner. As protecting personal information of the operators while using their EEG data is important and necessary, this study also investigated schemes for providing information security to the EEGbased FFD status classification system by following the International Organization for Standardization/International Electrotechnical Commission standard. Data confidentiality, integrity, and unlinkability were considered in the resulting schemes of information security for the EEG data. The resulting system provides the necessary protection of personal information and the FFD databases without significantly affecting the overhead of FFD classification through near real-time analysis.INDEX TERMS Information security, brain computer interface, electroencephalography (EEG), deep learning, nuclear safety, fitness for duty (FFD).

Brain-computer interfaces in safety and security fields: Risks and applications

“…Table 5 shows the results obtained within the field "safety" or "security" or "security and safety" applying the search strings. When these results are compared with those of Table 4, it can be observed: Search string 1: the 48 results are the same as those shown in Table 4; Search string 2: of the 26 results, 23 are included within the safety field, and 3 are included within security (Belkacem & IEEE, 2020;Bernal et al, 2021;Bernal, Sergio López et al, 2022); Search string 3: the 22 results are within the 25 of the security field; Search string 4: 3 results that are within security and safety (Karim et al, 2019;Kim et al, 2021;Sciaraffa et al, 2020).…”

“…Regarding the evaluation and training of specific cognitive and physiological conditions, Kim et al (2021) present the development of an information security-enforced EEG-based classification system for evaluating nuclear power plant operators and determining their fitness for duty for safe nuclear reactor operations. Huang et al (2021) propose a virtual reality system for construction safety training, based on BCI and physiology data, which facilitates understanding workers' physical condition, enhancing safety awareness, and reducing accidents.…”

Behavioural Curves Analysis Using Near-Infrared-Iris Image Sequences

“…The results showed that subject-independent sleepiness classification based on HRV performs poorly in realistic driving conditions. Kim et al [32] studied operator performance in a nuclear power plant using an FFD system using Electroencephalogram (EEG) with a deep learning algorithm to classify an operator's condition. To determine the suitability of this approach, EEG data were collected during simple cognitive exercises designed to examine the mental readiness of nuclear operators.…”

A Detailed Analysis & Parametric Comparison Of Eeg Processing Models

“…The experimental results demonstrate a greater degree of classification accuracy for the fuzzy-based CIT. Also looked at by [38]. The efficiency of any nuclear power plant depends on how efficiently it is managed.…”