EEG-based Statistical Analysis on Determining the Stress Mental State on Police Personnel

Nophaz Hanggara Saputra, A. Wibawa, M. Purnomo, Yuri Pamungkas
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Abstract

Suicide is a global phenomenon that occurs worldwide, including in Indonesia. This is due to complications from high and severe stress because of economic factors, family and environmental problems. High and severe stresses are not only experienced by the individual in a society, but also by government staff such as members of the police. The heavy workload, such as handling demonstrations with high escalation, is one of the factors that cause police members to experience high stress. Psychological assistance and healing such as in-depth interviews and psychological assessment tests have been carried out to map the psychological barriers and stressful conditions of police officers. The use of an Electroencephalogram (EEG) is one of the physiological signals that can be used to measure and recognize stress based on data on human brain activity. This research is exploring stress state by using eeg signal analysis in time domain. The analysis is done based on EEG time-domain features in the theta (4-8Hz), alpha (8-13Hz), and beta (13-30Hz) frequency bands from two different channels, namely F3 and F4 in the 10/20 EEG system. Twenty members of the state police (10 under stress conditions and 10 in normal conditions) are involved in this study. Statistical features such as Mean, Standard Deviation, and Zero Crossing are used to distinguish between stress and normal conditions. The experimental results showed that the Standard Deviation feature on the Alpha subband provided the highest difference in comparing between normal and stress conditions. In the classification of stress and normal conditions using several algorithms, SVM indicates the highest classification accuracy (88.90%), compared to other algorithms such as Random Forest (86.10%), K-NN (77.80%) and Decision Tree (77,80%).
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基于脑电图的公安人员应激心理状态的统计分析
自杀是一种全球现象,在世界各地都有发生,包括在印度尼西亚。这是由于经济因素、家庭和环境问题造成的高度和严重压力引起的并发症。高度和严重的压力不仅是社会中的个人所经历的,而且是警察等政府工作人员所经历的。处理高度升级的示威等繁重的工作是警察们感到压力很大的原因之一。开展了心理援助和治疗,如深入访谈和心理评估测试,以了解警察的心理障碍和压力状况。脑电图(EEG)的使用是一种生理信号,可用于测量和识别基于人脑活动数据的压力。本研究是利用脑电信号的时域分析来探索应力状态。基于10/20 EEG系统中F3和F4两个不同通道的theta (4-8Hz)、alpha (8-13Hz)和beta (13-30Hz)频段的EEG时域特征进行分析。本研究涉及20名州警察(10名处于压力状态,10名处于正常状态)。统计特征,如平均值,标准差,和过零点被用来区分应力和正常条件。实验结果表明,在正常和应力条件下,α子带上的标准差特征提供了最大的差异。在几种算法对应力和正常状态的分类中,SVM的分类准确率最高(88.90%),而其他算法如Random Forest(86.10%)、K-NN(77.80%)和Decision Tree(78,80%)。
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