Finding the EEG Footprint of Stress Resilience

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

Full text not available from this repository.
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

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.

Item Type: Article
Additional Information: 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
Uncontrolled Keywords: Brain; Physiological models; Psychophysiology, Brain activity; EEG feature; Job performance; Key factors; Mental stress; Resilience; Self-assessment; Stress disorders; Wellbeing; Work stress, Neurophysiology
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 19 Dec 2023 03:23
Last Modified: 19 Dec 2023 03:23
URI: https://khub.utp.edu.my/scholars/id/eprint/17419

Actions (login required)

View Item
View Item