Industrial insider threat detection has consistently been a popular field of research. To help detect potential insider threats, the emotional states of humans are identified through a wide range of physiological signals including the galvanic skin response, electrocardiogram, and electroencephalogram (EEG). This paper presents an insider risk assessment system as a fitness for duty security evaluation using EEG brainwave signals with explainable deep learning and machine learning algorithms to classify abnormal EEG signals indicating a potential insider threat and evaluating fitness for duty. The system is designed to be cost-effective by using an Emotiv Insight EEG device with five electrodes. In this study, the data from 17 people in different emotional states were collected. The different levels of emotions were mapped and classified into four risk levels, namely low, normal, medium, and high. The data were collected while the subjects were presented with different images from the scientific international affective picture system. The collected EEG signals were preprocessed to eliminate noise from physical movements and blinking. The data were then used to train self-feature learning of two-and one-dimensional convolutional neural networks, Adaptive Boosting, random forest, and K-nearest neighbors models; the proposed method yielded classification accuracies of 96, 75, 97, 94 and 81%, respectively.