TY - GEN
T1 - Classifying Stress Mental State by using Power Spectral Density of Electroencephalography (EEG)
AU - Wibawa, Adhi Dharma
AU - Astuti, Ulfi Widya
AU - Saputra, Nophaz Hanggara
AU - Mas, Arbintoro
AU - Pamungkas, Yuri
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - EEG analysis in frequency domain
KW - K-NN
KW - Keywords-Stress detection using EEG analysis
KW - Police stress mental state
KW - Power Spectral Density
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=85143640580&partnerID=8YFLogxK
U2 - 10.1109/ICITEE56407.2022.9954069
DO - 10.1109/ICITEE56407.2022.9954069
M3 - Conference contribution
AN - SCOPUS:85143640580
T3 - ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering
SP - 235
EP - 240
BT - ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference on Information Technology and Electrical Engineering, ICITEE 2022
Y2 - 18 October 2022 through 19 October 2022
ER -
