eprintid: 17084
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/70/84
datestamp: 2023-12-19 03:23:32
lastmod: 2023-12-19 03:23:32
status_changed: 2023-12-19 03:07:26
type: article
metadata_visibility: show
creators_name: Al-Saggaf, U.M.
creators_name: Naqvi, S.F.
creators_name: Moinuddin, M.
creators_name: Alfakeh, S.A.
creators_name: Ali, S.S.A.
title: Performance Evaluation of EEG Based Mental Stress Assessment Approaches for Wearable Devices
ispublished: pub
keywords: Classification (of information); Computer aided diagnosis; Computer aided instruction; Convolutional neural networks; Deep learning; Extraction; Wearable technology, Computer-aided diagnose; Convolutional neural network; Features extraction; Learning approach; Machine learning approaches; Mental stress; Real- time; Sliding Window; Stress assessment; Wearable devices, Feature extraction, alpha rhythm; Article; benchmarking; beta rhythm; classification; computer assisted diagnosis; controlled study; convolutional neural network; deep learning; delta rhythm; diagnostic test accuracy study; electroencephalogram; feature extraction; gamma rhythm; human; information processing; machine learning; mental stress; sensitivity and specificity; theta rhythm
note: cited By 5
abstract: Mental stress has been identified as the root cause of various physical and psychological disorders. Therefore, it is crucial to conduct timely diagnosis and assessment considering the severe effects of mental stress. In contrast to other health-related wearable devices, wearable or portable devices for stress assessment have not been developed yet. A major requirement for the development of such a device is a time-efficient algorithm. This study investigates the performance of computer-aided approaches for mental stress assessment. Machine learning (ML) approaches are compared in terms of the time required for feature extraction and classification. After conducting tests on data for real-time experiments, it was observed that conventional ML approaches are time-consuming due to the computations required for feature extraction, whereas a deep learning (DL) approach results in a time-efficient classification due to automated unsupervised feature extraction. This study emphasizes that DL approaches can be used in wearable devices for real-time mental stress assessment. Copyright © 2022 Al-Saggaf, Naqvi, Moinuddin, Alfakeh and Ali.
date: 2022
publisher: Frontiers Media S.A.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124986925&doi=10.3389%2ffnbot.2021.819448&partnerID=40&md5=1e7e28f385df1c76bc9ec5cc0789f813
id_number: 10.3389/fnbot.2021.819448
full_text_status: none
publication: Frontiers in Neurorobotics
volume: 15
refereed: TRUE
issn: 16625218
citation:   Al-Saggaf, U.M. and Naqvi, S.F. and Moinuddin, M. and Alfakeh, S.A. and Ali, S.S.A.  (2022) Performance Evaluation of EEG Based Mental Stress Assessment Approaches for Wearable Devices.  Frontiers in Neurorobotics, 15.   ISSN 16625218