Classification of Mental Stress using Dry EEG Electrodes and Machine Learning

Yara Badr, Fares Al-Shargie, Usman Tariq, Fabio Babiloni, Fadwa Al Mughairbi, Hasan Al-Nashash

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Stress is a major health issue that affects people worldwide and results in several diseases and negative psychological consequences. Therefore, early detection of stress has become crucial for maintaining a healthy society. In this study, five different machine learning classifiers were studied for their accuracy in assessing mental stress among university students. Mental stress was obtained from EEG signals using a dry Electroencephalography (EEG) electrode. To induce stress and calm mental states, a Stroop Color Word Task (SCWT) was utilized. EEG data were analyzed by extracting the mean power of 4 frequency bands using the Fast Fourier Transform (FFT). The different machine learning classifiers: K-Nearest Neighbors (KNN), Discriminant Analysis (DA), Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM), were then compared for accuracy, sensitivity, and specificity. The behavioral result showed that the accuracy of detecting the SCWT under stress was reduced by 50%. The SVM outperformed other classifiers, achieving the highest classification performance in subject dependent with 99.98 ± 0.09, 99.96 ± 0.122, 99.85 ± 0.30, and 99.27 ± 0.57 accuracies in alpha, beta, theta, and delta band, respectively. In conclusion, alpha and beta bands showed a slightly higher accuracy than other frequency bands. Meanwhile, SVM outperformed other classifiers, achieving the highest classification accuracy of 99.98 % with the mean power of the alpha band.

Original languageEnglish
Title of host publication2023 Advances in Science and Engineering Technology International Conferences, ASET 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665454742
DOIs
Publication statusPublished - 2023
Event2023 Advances in Science and Engineering Technology International Conferences, ASET 2023 - Dubai, United Arab Emirates
Duration: Feb 20 2023Feb 23 2023

Publication series

Name2023 Advances in Science and Engineering Technology International Conferences, ASET 2023

Conference

Conference2023 Advances in Science and Engineering Technology International Conferences, ASET 2023
Country/TerritoryUnited Arab Emirates
CityDubai
Period2/20/232/23/23

Keywords

  • EEG
  • Machine learning
  • PSD
  • mental stress

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Biomedical Engineering
  • Control and Optimization
  • Artificial Intelligence
  • Computer Science Applications
  • Decision Sciences (miscellaneous)
  • Fuel Technology

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