Impact of varying levels of mental stress on phase information of EEG Signals: A study on the Frontal, Central, and parietal regions

Farzad Saffari*, Kian Norouzi, Luis E. Bruni, Sahar Zarei, Thomas Z. Ramsøy

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Mental stress is a commonly occurring phenomenon that impacts people from diverse backgrounds and is associated with numerous physical and psychological illnesses. The brain plays a vital role in how individuals perceive and react to stress, including their physiological and behavioral responses. In this study, our objective was to investigate the impact of varying levels of induced stress, ranging from mild to severe, on brain activity. Our primary interest was to determine if mental stress would influence neural coordination, as assessed through intertrial phase clustering (ITPC). Furthermore, we hypothesized that an increase in perceived mental stress would result in reduced regional connectivity as measured via phase-lag index (PLI). EEG data from 41 participants (20 females, 21 males, age range 18 to 46; mean = 26.1; SD = 7.06) were collected while they were exposed to three levels of mental stress, using a parametric modulation study design. Following pre-processing, we extracted the two mentioned features and performed statistical analysis. As an additional analysis, we assessed the discriminatory power of these features using a Random Forest classifier. Statistical analysis revealed a significant decrease of ITPCs over frontal, central, and parietal regions accompanying increased levels of stress. The results obtained from the PLI analysis showed that the increase in levels of stress were associated with a decrease in the brain connectivity over the frontocentral, frontoparietal, and centroparietal regions. The classification result showed that the Random Forrest classifier predict three levels of stress with 83.78% accuracy. These findings indicate that phase-based EEG features could serve as a novel neurometric for quantifying in vivo stress levels. Furthermore, this study could contribute to developing more precise tools to measure mental stress objectively.

Original languageEnglish
Article number105236
JournalBiomedical Signal Processing and Control
Volume86
Issue numberPart B
ISSN1746-8094
DOIs
Publication statusPublished - Sept 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s)

Keywords

  • Electroencephalogram (EEG)
  • Intertrial Phase Clustering (ITPC)
  • Mental Stress
  • Phase-Lag Index (PLI)
  • Random Forrest

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