eprintid: 17419
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/74/19
datestamp: 2023-12-19 03:23:48
lastmod: 2023-12-19 03:23:48
status_changed: 2023-12-19 03:08:01
type: article
metadata_visibility: show
creators_name: Hasan, R.A.
creators_name: Ali, S.S.A.
creators_name: Tang, T.B.
creators_name: Yusoff, M.S.B.
title: Finding the EEG Footprint of Stress Resilience
ispublished: pub
keywords: Brain; Physiological models; Psychophysiology, Brain activity; EEG feature; Job performance; Key factors; Mental stress; Resilience; Self-assessment; Stress disorders; Wellbeing; Work stress, Neurophysiology
note: cited By 0; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319
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. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
date: 2022
publisher: Springer Science and Business Media Deutschland GmbH
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142671951&doi=10.1007%2f978-981-16-2183-3_76&partnerID=40&md5=db26d947a1b5b534a6b4d531188042c7
id_number: 10.1007/978-981-16-2183-3₇₆
full_text_status: none
publication: Lecture Notes in Electrical Engineering
volume: 758
pagerange: 807-816
refereed: TRUE
isbn: 9789811621826
issn: 18761100
citation:   Hasan, R.A. and Ali, S.S.A. and Tang, T.B. and Yusoff, M.S.B.  (2022) Finding the EEG Footprint of Stress Resilience.  Lecture Notes in Electrical Engineering, 758.  pp. 807-816.  ISSN 18761100