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

Adhi Dharma Wibawa; Ulfi Widya Astuti; Nophaz Hanggara Saputra; Arbintoro Mas; Yuri Pamungkas

doi:10.1109/ICITEE56407.2022.9954069

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

Adhi Dharma Wibawa
, Ulfi Widya Astuti
, Nophaz Hanggara Saputra
, Arbintoro Mas
, Yuri Pamungkas

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

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.

Original language	English
Title of host publication	ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	235-240
Number of pages	6
ISBN (Electronic)	9781665460774
DOIs	https://doi.org/10.1109/ICITEE56407.2022.9954069
Publication status	Published - 2022
Event	14th International Conference on Information Technology and Electrical Engineering, ICITEE 2022 - Yogyakarta, Indonesia Duration: 18 Oct 2022 → 19 Oct 2022

Publication series

Name	ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering

Conference

Conference	14th International Conference on Information Technology and Electrical Engineering, ICITEE 2022
Country/Territory	Indonesia
City	Yogyakarta
Period	18/10/22 → 19/10/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

EEG analysis in frequency domain
K-NN
Keywords-Stress detection using EEG analysis
Police stress mental state
Power Spectral Density
SVM

Access to Document

10.1109/ICITEE56407.2022.9954069

Cite this

Wibawa, A. D., Astuti, U. W., Saputra, N. H., Mas, A., & Pamungkas, Y. (2022). Classifying Stress Mental State by using Power Spectral Density of Electroencephalography (EEG). In ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering (pp. 235-240). (ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICITEE56407.2022.9954069

Wibawa, Adhi Dharma ; Astuti, Ulfi Widya ; Saputra, Nophaz Hanggara et al. / Classifying Stress Mental State by using Power Spectral Density of Electroencephalography (EEG). ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 235-240 (ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering).

@inproceedings{bce295069e8642fb819f1f73fdd6d5eb,

title = "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.",

keywords = "EEG analysis in frequency domain, K-NN, Keywords-Stress detection using EEG analysis, Police stress mental state, Power Spectral Density, SVM",

author = "Wibawa, \{Adhi Dharma\} and Astuti, \{Ulfi Widya\} and Saputra, \{Nophaz Hanggara\} and Arbintoro Mas and Yuri Pamungkas",

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year = "2022",

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series = "ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Wibawa, AD, Astuti, UW, Saputra, NH, Mas, A & Pamungkas, Y 2022, Classifying Stress Mental State by using Power Spectral Density of Electroencephalography (EEG). in ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering. ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering, Institute of Electrical and Electronics Engineers Inc., pp. 235-240, 14th International Conference on Information Technology and Electrical Engineering, ICITEE 2022, Yogyakarta, Indonesia, 18/10/22. https://doi.org/10.1109/ICITEE56407.2022.9954069

Classifying Stress Mental State by using Power Spectral Density of Electroencephalography (EEG). / Wibawa, Adhi Dharma; Astuti, Ulfi Widya; Saputra, Nophaz Hanggara et al.
ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering. Institute of Electrical and Electronics Engineers Inc., 2022. p. 235-240 (ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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.

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KW - K-NN

KW - Keywords-Stress detection using EEG analysis

KW - Police stress mental state

KW - Power Spectral Density

KW - SVM

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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

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ER -

Wibawa AD, Astuti UW, Saputra NH, Mas A, Pamungkas Y. Classifying Stress Mental State by using Power Spectral Density of Electroencephalography (EEG). In ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering. Institute of Electrical and Electronics Engineers Inc. 2022. p. 235-240. (ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering). doi: 10.1109/ICITEE56407.2022.9954069

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

Abstract

Publication series

Conference

UN SDGs

Keywords

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