Classifying Stress Mental State by using Power Spectral Density of Electroencephalography (EEG)

Abstract

Police are one of the jobs that have a heavy workload. Police are more susceptible to stress as a result. Currently, the Indonesian National Police evaluates the mental health of police officers using a questionnaire. However, this questionnaire is very prone to subjectivity bias. Electroencephalography (EEG) was studied as another method for detecting stress in humans. Participants were selected through questionnaire results, labeled, and categorized into stressed and normal. Eighteen participants were involved in this experiment. They are nine normal subjects and nine stressed subjects. The EEG data was recorded on two channels, F3 and F4. Those channels are located in the prefrontal cortex and have been recognized as channels for exploring the stress mental state. Python was used to perform EEG preprocessing, including bandstop filtering, artifact and noise removal, and ICA filtering. The cleaned EEG signal is then decomposed into Alpha, Beta, and Gamma sub-bands. Power Spectral Density (PSD) is then calculated as the feature for classifying between the two classes, the normal and stress mental state. K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) were applied to obtain accuracy. K-NN and SVM produce an accuracy of 90.8% and 74.5% consecutively.