The Psychological State Evaluation Method of a Main Control Room Operator Based on Physiological Signals

Mental state refers to the comprehensive state of mental activities and mental characteristics such as emotion, perception, thinking, will, fatigue, etc., which has a great impact on human operation performance. The main control room of nuclear power plant is the key link to the safety and efficiency of nuclear power system, and the operator is the main part of it. Therefore, the analysis and evaluation of the operator's psychological state has become the main technical means to improve the operation safety of the main control room. Brain fatigue is a psychological phenomenon that operators in the main control room of nuclear power plants often face. Workload is the most important cause of mental fatigue. Therefore, this paper takes workload and mental fatigue as research objects, and studies the evaluation methods of operators' mental states under accident conditions based on EEG and ECG physiological signals. Fatigue induced experiment and workload evaluation experiment were designed respectively, and EEG and ECG signals were collected before and after the experimental task. Fifteen operators were selected as volunteers to participate in the fatigue induction experiment, and the independent variable was task duration. The operators were required to continuously handle one Steam Generator Tube Ruptures (SGTR) accident with a duration of approximately 40 minutes and one SGTR accident with an additional minor accident inserted with a duration of approximately 60 minutes. Another 14 operators were selected as volunteers to participate in the workload evaluation experiment. The independent variable was tasks with different workloads, that is, different workloads in the same time were used to represent different levels of workload. In order to avoid the interference of mental fatigue on workload evaluation caused by too long task duration, the operator should not handle tasks for too long. In the workload assessment experiment, the operators were required to deal with a small Loss of Coolant Accident (LOCA) task that lasted about 20 minutes, and then to deal with a large LOCA task that also lasted about 20 minutes after adequate rest. The workload of LOCA big break task was much higher than that of LOCA small break task. The results showed that RMSSD, SDSD, pNN50, pNN20 and HF power of heart rate variability were significantly different after pre-measured rest and 100-minute long task. There were significant differences of EEG characteristics in (α+θ)/(α+β), Beta average power, Beta total power, θ/β, Delta average power, Delta total power, etc. These characteristics could indicate mental fatigue. After different workload tasks, 31 EEG features showed significant differences, including total Theta power, average Theta power, average Delta power, total Delta power, (α+θ)/(α+β), θ/(α+ θ)/β, θ/(α+ θ)/β, θ/(α+ θ)/β, and θ/(α+β) in Fp1 channel. (α+θ)/(α+β), total Theta power of Fp2 channel, etc. Mental fatigue (mental state) was divided into two levels, wakefulness and fatigue, and a mental fatigue classification model was established by Gauss naive Bayes algorithm with an accuracy of 80.0%. The workload was divided into two levels: low workload and high workload. The precision tree algorithm was used to establish the workload classification model, and the classification accuracy is 89.3%.