An Investigation of Insider Threat Mitigation Based on EEG Signal Classification

Effects of tDCS on emotion recognition and brain oscillations

INTRODUCTION

Emotion recognition, the ability to interpret the emotional state of individuals by looking at their facial expressions, is essential for healthy social interactions and communication. There is limited research on the effects of tDCS on emotion recognition in the literature. This study aimed to investigate the effects of anodal stimulation of the ventromedial prefrontal cortex (vmPFC), a key region for emotion recognition from facial expressions, on emotion recognition and brain oscillations.

METHOD

A single-blind randomized-controlled study was conducted with 54 healthy participants. Before and after brain stimulation emotion recognition tasks were administered and resting-state EEG were recorded. The changes in task performances and brain oscillations were analyzed using repeated-measures two-way ANOVA analysis.

RESULTS

There was no significant difference in the emotion recognition tasks between groups in pre-post measurements. The changes in delta, theta, alpha, beta and gamma frequency bands in the frontal, temporal, and posterio-occipital regions, which were determined as regions of interest in resting state EEG data before and after tDCS, were compared between groups. The results showed that there was a significant difference between groups only in delta frequency before and after tDCS in the frontal and temporal regions. While an increase in delta activity was observed in the experimental group in the frontal and temporal regions, a decrease was observed in the control group.

CONCLUSIONS

The tDCS may not have improved emotion recognition because it may not have had the desired effect on the vmPFC, which is in the lower part of the prefrontal lobe. The changes in EEG frequencies observed section tDCS may be similar to those seen in some pathological processes, which could explain the lack of improvement in emotion recognition. Future studies to be carried out for better understand this effect are important.

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

Emotion artificial intelligence (AI) is being increasingly adopted in several industries such as healthcare and education. Facial expressions and tone of speech have been previously considered for emotion recognition, yet they have the drawback of being easily manipulated by subjects to mask their true emotions. Electroencephalography (EEG) has emerged as a reliable and cost-effective method to detect true human emotions. Recently, huge research effort has been put to develop efficient wearable EEG devices to be used by consumers in out of the lab scenarios. In this work, a subject-dependent emotional valence recognition method is implemented that is intended for utilization in emotion AI applications. Time and frequency features were computed from a single time series derived from the Fp1 and Fp2 channels. Several analyses were performed on the strongest valence emotions to determine the most relevant features, frequency bands, and EEG timeslots using the benchmark DEAP dataset. Binary classification experiments resulted in an accuracy of 97.42% using the alpha band, by that outperforming several approaches from literature by ~3-22%. Multiclass classification gave an accuracy of 95.0%. Feature computation and classification required less than 0.1 s. The proposed method thus has the advantage of reduced computational complexity as, unlike most methods in the literature, only two EEG channels were considered. In addition, minimal features concluded from the thorough analyses conducted in this study were used to achieve state-of-the-art performance. The implemented EEG emotion recognition method thus has the merits of being reliable and easily reproducible, making it well-suited for wearable EEG devices.

Evolutionary Game Model of Internal Threats to Nuclear Security in Spent Fuel Reprocessing Plants Based on RDEU Theory

The internal threat to nuclear security is one of the most serious problems in the physical protection supervision of spent fuel reprocessing plants. Both insiders and nuclear security departments have obvious characteristics of situational decision making and even irrational decision making. Combined with Game theory and RDEU theory, the RDEU Game model of insiders and nuclear security departments was constructed to analyze the existence of equilibrium solutions of two-way strategies under different emotional states. From a dynamic point of view, the influence and change process of emotion on participants’ decision-making behavior were analyzed. Then, the model was numerically simulated to verify its accuracy and effectiveness, which showed that different emotional states and intensities would not only affect the final result of evolutionary equilibrium, but also change the evolution speed of the strategies. In addition, compared with insiders, the intensity of pessimism in the nuclear security department had a greater impact on the game equilibrium. Finally, we present some reasonable recommendations to prevent and protect nuclear security events at spent fuel reprocessing plants by strengthening the emotional supervision and guidance of insiders and the nuclear security department.

Biosignal-Based Attention Monitoring to Support Nuclear Operator Safety-Relevant Tasks

In the main control room (MCR) of a nuclear power plant (NPP), the quality of an operator's performance can depend on their level of attention to the task. Insufficient operator attention accounted for more than 26% of the total causes of human errors and is the highest category for errors. It is therefore necessary to check whether operators are sufficiently attentive either as supervisors or peers during reactor operation. Recently, digital control technologies have been introduced to the operating environment of an NPP MCR. These upgrades are expected to enhance plant and operator performance. At the same time, because personal computers are used in the advanced MCR, the operators perform more cognitive works than physical work. However, operators may not consciously check fellow operators' attention in this environment indicating potentially higher importance of the role of operator attention. Therefore, remote measurement of an operator's attention in real time would be a useful tool, providing feedback to supervisors. The objective of this study is to investigate the development of quantitative indicators that can identify an operator's attention, to diagnose or detect a lack of operator attention thus preventing potential human errors in advanced MCRs. To establish a robust baseline of operator attention, this study used two of the widely used biosignals: electroencephalography (EEG) and eye movement. We designed an experiment to collect EEG and eye movements of the subjects who were monitoring and diagnosing nuclear operator safety-relevant tasks. There was a statistically significant difference between biosignals with and without appropriate attention. Furthermore, an average classification accuracy of about 90% was obtained by the k-nearest neighbors and support vector machine classifiers with a few EEG and eye movements features. Potential applications of EEG and eye movement measures in monitoring and diagnosis tasks in an NPP MCR are also discussed.