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Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach

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  • Ding, Song
  • Hu, Lunhu
  • Pan, Xing
  • Zuo, Dujun
  • Sun, Liuwang

Abstract

Situation awareness (SA) assessment is the process of acquiring and maintaining SA, which serves as a crucial indicator of operator task performance and behavioral safety in human-machine interaction. SA reliability is the evaluation of how well SA is established, and it is also the goal of SA assessment. Nonetheless, current SA assessment models rarely consider the influence of human physiological states, such as fatigue and mood, and rely heavily on subjective data. To address these deficiencies, this paper proposes a SA assessment model based on a Bayesian Neural Network (BNN) and Bayesian Network (BN), with a focus on examining the impact of fatigue and mood on the SA reliability. Firstly, fatigue and mood state classification models are developed using EEG data based on a BNN, and the uncertainty is assessed. Secondly, a BN model for SA reliability evaluation is proposed, where the uncertainty of BNN outputs is used as the prior probability, and conditional probability tables are established based on experimental statistics. Finally, a SA experiment is conducted using a civil aviation scenario based on the SAGAT platform to validate the proposed model. This model overcomes the limitations of previous approaches by leveraging objective physiological data and experimental statistics to infer the influence of physiological states on the SA reliability.

Suggested Citation

  • Ding, Song & Hu, Lunhu & Pan, Xing & Zuo, Dujun & Sun, Liuwang, 2025. "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:reensy:v:260:y:2025:i:c:s0951832025001656
    DOI: 10.1016/j.ress.2025.110962
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    1. Baraldi, Piero & Mangili, Francesca & Zio, Enrico, 2013. "Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 94-108.
    2. Sui, Zhongyi & Wang, Shuaian, 2025. "Traffic advisory for ship encounter situation based on linear dynamic system," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    3. Li, Jue & Li, Heng & Wang, Fan & Cheng, Andy S.K. & Yang, Xincong & Wang, Hongwei, 2021. "Proactive analysis of construction equipment operators’ hazard perception error based on cognitive modeling and a dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    4. Pan, Xing & Zuo, Dujun & Zhang, Wenjin & Hu, Lunhu & Wang, Huixiong & Jiang, Jing, 2021. "Research on Human Error Risk Evaluation Using Extended Bayesian Networks with Hybrid Data," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    5. Panakkal, Pranavesh & Padgett, Jamie Ellen, 2024. "More eyes on the road: Sensing flooded roads by fusing real-time observations from public data sources," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    6. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2015. "Bayesian belief networks for human reliability analysis: A review of applications and gaps," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 1-16.
    7. Domeh, Vindex & Obeng, Francis & Khan, Faisal & Bose, Neil & Sanli, Elizabeth, 2023. "An operational risk awareness tool for small fishing vessels operating in harsh environment," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. You, Qidong & Guo, Jianbin & Zeng, Shengkui & Che, Haiyang, 2024. "A dynamic Bayesian network based reliability assessment method for short-term multi-round situation awareness considering round dependencies," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    9. Kwon, Yongchan & Won, Joong-Ho & Kim, Beom Joon & Paik, Myunghee Cho, 2020. "Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation," Computational Statistics & Data Analysis, Elsevier, vol. 142(C).
    10. Li, Qinbiao & Ng, Kam K.H. & Yiu, Cho Yin & Yuan, Xin & So, Chun Kiu & Ho, Chun Chung, 2023. "Securing air transportation safety through identifying pilot's risky VFR flying behaviours: An EEG-based neurophysiological modelling using machine learning algorithms," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    11. Naderpour, Mohsen & Lu, Jie & Zhang, Guangquan, 2016. "A safety-critical decision support system evaluation using situation awareness and workload measures," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 147-159.
    12. Naderpour, Mohsen & Lu, Jie & Zhang, Guangquan, 2015. "An abnormal situation modeling method to assist operators in safety-critical systems," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 33-47.
    13. Fan, Shiqi & Yang, Zaili, 2024. "Accident data-driven human fatigue analysis in maritime transport using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    14. Kim, Man Cheol & Seong, Poong Hyun, 2006. "An analytic model for situation assessment of nuclear power plant operators based on Bayesian inference," Reliability Engineering and System Safety, Elsevier, vol. 91(3), pages 270-282.
    15. Sonal, & Ghosh, Debomita, 2022. "Impact of situational awareness attributes for resilience assessment of active distribution networks using hybrid dynamic Bayesian multi criteria decision-making approach," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    16. Xin, Xuri & Liu, Kezhong & Loughney, Sean & Wang, Jin & Yang, Zaili, 2023. "Maritime traffic clustering to capture high-risk multi-ship encounters in complex waters," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    17. Lee, Hyun-Chul & Seong, Poong-Hyun, 2009. "A computational model for evaluating the effects of attention, memory, and mental models on situation assessment of nuclear power plant operators," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1796-1805.
    18. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Che, Haiyang, 2021. "A Bayesian network for reliability assessment of man-machine phased-mission system considering the phase dependencies of human cognitive error," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    19. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
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