relation: https://khub.utp.edu.my/scholars/17084/
title: Performance Evaluation of EEG Based Mental Stress Assessment Approaches for Wearable Devices
creator: Al-Saggaf, U.M.
creator: Naqvi, S.F.
creator: Moinuddin, M.
creator: Alfakeh, S.A.
creator: Ali, S.S.A.
description: 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.
publisher: Frontiers Media S.A.
date: 2022
type: Article
type: PeerReviewed
identifier:   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     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124986925&doi=10.3389%2ffnbot.2021.819448&partnerID=40&md5=1e7e28f385df1c76bc9ec5cc0789f813
relation: 10.3389/fnbot.2021.819448
identifier: 10.3389/fnbot.2021.819448