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Finding the EEG Footprint of Stress Resilience

  • Rumaisa Abu Hasan*
  • , Syed Saad Azhar Ali
  • , Tong Boon Tang
  • , Muhamad Saiful Bahri Yusoff
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Work stress faced by adults can lead to decreased job performance, reduced mental and physical wellbeing, and other detrimental health problems. Researchers are reporting resilience as a key factor in determining a person’s vulnerability towards mental stress disorders. Psychosocial measures of resilience conventionally use the self-assessment approach which is susceptible to potential biases caused by self-reporting and concerns of social stigma. With increasing emphasis of its role in mental health, researchers are using fMRI modality to identify the brain activity of stress resilience. But this approach is costly and lack practicality when evaluating stress resilience in daily tasks. The EEG modality provides a cost-efficient alternative with better practicality and high temporal resolution in studying the brain activity of stress resilience. However, EEG-based literatures on stress resilience are limited to brain activity during resting state. With reference to the cognitive affective conceptual stress model, we define stress resilience as an adaptation process, involving cognitive appraisal, physiological arousal and coping behaviour, that utilizes individual resources to cope with stress. This paper proposes an approach to identify the features of EEG-neural correlates of stress resilience through brain rhythms, hemispheric asymmetry and brain network.

Original languageEnglish
Title of host publicationInternational Conference on Artificial Intelligence for Smart Community - AISC 2020
EditorsRosdiazli Ibrahim, Ramani Kannan, Nursyarizal Mohd Nor, K. Porkumaran, S. Prabakar
PublisherSpringer Science and Business Media Deutschland GmbH
Pages807-816
Number of pages10
ISBN (Print)9789811621826
DOIs
StatePublished - 2022
Externally publishedYes
Event1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 - Virtual, Online
Duration: 17 Dec 202018 Dec 2020

Publication series

NameLecture Notes in Electrical Engineering
Volume758
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference1st International Conference on Artificial Intelligence for Smart Community, AISC 2020
CityVirtual, Online
Period17/12/2018/12/20

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

  • EEG feature
  • Resilience
  • Stress

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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