The objective of this study is to investigate the feasibility of developing a worker psychological distress monitoring system using Electroencephalogram (EEG). Psychological impairment has emerged as a key security (insider threat) and safety (human error) issue at Nuclear Power Plants (NPPs) as well as other industries. Although the U.S. Nuclear Regulatory Commission (NRC) highlighted the importance of NPP workers' Fitness-ForDuty (FFD) to ensure personnel reliability, current FFD programs only consider drug and alcohol testing and fatigue management. However, today's bio-signals technology makes it possible to monitor the physical and mental state of workers. Thus, this study examines the feasibility of using EEG indicators to identify potentially-at-risk workers, especially those with acute psychological distress. We reviewed historical cases of insider threat and human error at nuclear facilities, and analyzed these cases from the perspective of a suspect's mental health. Based on bio-signal literature, a variety of EEG indicators identified at risk workers with a psychological impairment. As such, we selected the following: (1) Frontal EEG asymmetry; (2) EEG coherence; and (3) the variations of frequency domain EEG indicators (Theta, Alpha, Beta and Gamma) at certain brain area. To verify the appropriateness of these EEG indicators in realistic situations, this study performed a pilot experiment. The resting states of EEG (Eye Closed and Eye Open) were recorded on 56 student subjects (36 healthy and 20 with a high score for depression and anxiety symptoms). The resting states of EEG results showed a statistically significant difference between at-risk students and healthy students. This means specific EEG indicators can be used to classify the mental status of workers. These results can be applied directly to the mental health monitoring system of nuclear power plants as well as the industries requiring high reliability (aerospace, military and transportation).