International conference on construction engineering and project management (국제학술발표논문집)

  • 2024.07a
  • /
  • Pages.343-350
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  • 2024
  • /
  • 2508-9048(eISSN)
Korea Institute of Construction Engineering and Management (한국건설관리학회)
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Ear-EEG-based Stress Assessment for Construction Workers: A Comparison with High-Density Scalp-EEG

  • Juhyeon BAE (Department of Civil and Environmental Engineering, University of Michigan) ;
  • Gaang LEE (Department of Civil and Environmental Engineering, University of Alberta) ;
  • SangHyun LEE (Department of Civil and Environmental Engineering, University of Michigan)
  • Published : 2024.07.29
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Abstract

Mobile electroencephalography (EEG) can continuously and objectively monitor construction workers' psychological stress, thereby contributing to enhanced safety and health. Traditional EEG-based stress assessment techniques utilize headset-type devices that cover the scalp, including the frontal area, which is the most relevant brain part to stress. Yet, the invasiveness of such devices may pose a potential barrier to their field application. In response, ear-EEG technology presents a less intrusive alternative for continuous monitoring, potentially overcoming the limitations of scalp-EEG. The temporal regions monitored by ear-EEG hold anatomical and functional significance in the brain's response to stress, suggesting that ear-EEG could effectively detect stress. Despite its advantage, the effectiveness of ear-EEG in stress detection remains underexplored, largely due to the existing literature's focus on frontal brain regions. To address this gap, the authors aim to evaluate ear-EEG's effectiveness in measuring stress and compare it to high-density scalp-EEG. EEG signals were collected with ear- and scalp-EEGs from 10 subjects in a controlled laboratory while they performed the mental arithmetic tasks under time pressure and socio-evaluative threats to induce stress at different levels (high vs. low). Subsequently, the authors performed t-tests and point-biserial analysis to analyze differences between high and low-stress conditions in the most reliable stress biomarkers in literature: high-beta power in temporal regions for ear-EEG, and alpha asymmetry in frontal regions for scalp-EEG. The results indicate that both EEG techniques could effectively differentiate between stress levels, with statistical significance (p <0.001 for both) and moderate effect size. Furthermore, the results demonstrate ear-EEG's comparable effectiveness to scalp-EEG in detecting stress-induced brain activity given the comparable statistical metrics, such as p-value and effect size. This study provides a groundwork for further explorations into leveraging ear-EEG as a practical tool for the early detection of stress, aiming to enhance stress management strategies within the construction industry.

Keywords

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