Workplace Stress Management using EEG-fNIRS Data Fusion and Binaural Beat Stimulation

A Master of Science thesis in Biomedical Engineering by Ghenwa El Masri entitled, “Workplace Stress Management using EEG-fNIRS Data Fusion and Binaural Beat Stimulation”, submitted in November 2023. Thesis advisors are Dr. Hasan Al Nashash, Dr. Usman Tariq, and Dr. Fares Al Shargie. Soft copy is ava...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: El Masri, Ghinwa (author)
التنسيق: doctoralThesis
منشور في: 2023
الموضوعات:
الوصول للمادة أونلاين:http://hdl.handle.net/11073/25462
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author El Masri, Ghinwa
author_facet El Masri, Ghinwa
author_role author
dc.contributor.none.fl_str_mv Al Nashash, Hasan
Tariq, Usman
Yahya, Fares
dc.creator.none.fl_str_mv El Masri, Ghinwa
dc.date.none.fl_str_mv 2023-11
2024-02-26T08:25:57Z
2024-02-26T08:25:57Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv 35.232-2023.53
http://hdl.handle.net/11073/25462
dc.language.none.fl_str_mv en_US
dc.subject.none.fl_str_mv Occupational stress
Stress mitigation
Binaural beats
Functional connectivity
Cortisol level
Functional near-infrared spectroscopy (fNIRS)
Electroencephalography (EEG)
Data fusion
dc.title.none.fl_str_mv Workplace Stress Management using EEG-fNIRS Data Fusion and Binaural Beat Stimulation
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/doctoralThesis
description A Master of Science thesis in Biomedical Engineering by Ghenwa El Masri entitled, “Workplace Stress Management using EEG-fNIRS Data Fusion and Binaural Beat Stimulation”, submitted in November 2023. Thesis advisors are Dr. Hasan Al Nashash, Dr. Usman Tariq, and Dr. Fares Al Shargie. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).
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network_acronym_str aus
network_name_str aus
oai_identifier_str oai:repository.aus.edu:11073/25462
publishDate 2023
repository.mail.fl_str_mv
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spelling Workplace Stress Management using EEG-fNIRS Data Fusion and Binaural Beat StimulationEl Masri, GhinwaOccupational stressStress mitigationBinaural beatsFunctional connectivityCortisol levelFunctional near-infrared spectroscopy (fNIRS)Electroencephalography (EEG)Data fusionA Master of Science thesis in Biomedical Engineering by Ghenwa El Masri entitled, “Workplace Stress Management using EEG-fNIRS Data Fusion and Binaural Beat Stimulation”, submitted in November 2023. Thesis advisors are Dr. Hasan Al Nashash, Dr. Usman Tariq, and Dr. Fares Al Shargie. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Work-related stress is a common problem for employees in demanding jobs, often resulting in sleep disorders and reduced job performance. Early detection of mental stress levels in employees and implementing mitigation strategies is crucial to prevent such complications. This thesis introduces two neuroimaging modalities, Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS), which complement each other, offering insights into the shared characteristics between the brain's hemodynamic and electrical responses to stress. Alongside physiological analyses, this study incorporates behavioral, subjective, and biochemical markers. After signal processing to minimize interference and noise, Canonical Correlation Analysis (CCA) and Joint Sparse CCA (JSCCA) are employed to fuse signal features. The EEG and fNIRS features are derived from functional connectivity networks, estimated using partial directed coherence (PDC). This work's contributions include the fusion of directed connectivity between EEG and fNIRS and an exploration of the stress-mitigating potential of binaural beat stimulation (BBs) while simultaneously recording EEG and fNIRS. CCA analysis identifies the primary localization of stress in the Orbitofrontal Cortex and Dorsolateral Prefrontal Cortex (DLPFC) for the fusion between the control and stress phases of EEG and fNIRS, mostly DLPFC for the stress and mitigation phases, and frontopolar prefrontal cortex as well as DLPFC for mitigation and after mitigation phases. In terms of classification performance, CCA surpasses JSCCA in enhancing fNIRS metrics, with JSCCA being more suitable for larger datasets. CCA was reported to have an accuracy of 98.38% compared to JSCCA’s 61.91% for the Naïve Bayes Model. Overall, CCA improved Naïve Bayes classification results of control/stress classes significantly, with fNIRS classification at 78.12%, EEG classification at 77.7%. Similar improvement by CCA was observed for the classifications of stress/mitigation and mitigation/after mitigation classes as well.College of EngineeringMultidisciplinary ProgramsMaster of Science in Biomedical Engineering (MSBME)Al Nashash, HasanTariq, UsmanYahya, Fares2024-02-26T08:25:57Z2024-02-26T08:25:57Z2023-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2023.53http://hdl.handle.net/11073/25462en_USoai:repository.aus.edu:11073/254622025-06-26T12:37:59Z
spellingShingle Workplace Stress Management using EEG-fNIRS Data Fusion and Binaural Beat Stimulation
El Masri, Ghinwa
Occupational stress
Stress mitigation
Binaural beats
Functional connectivity
Cortisol level
Functional near-infrared spectroscopy (fNIRS)
Electroencephalography (EEG)
Data fusion
status_str publishedVersion
title Workplace Stress Management using EEG-fNIRS Data Fusion and Binaural Beat Stimulation
title_full Workplace Stress Management using EEG-fNIRS Data Fusion and Binaural Beat Stimulation
title_fullStr Workplace Stress Management using EEG-fNIRS Data Fusion and Binaural Beat Stimulation
title_full_unstemmed Workplace Stress Management using EEG-fNIRS Data Fusion and Binaural Beat Stimulation
title_short Workplace Stress Management using EEG-fNIRS Data Fusion and Binaural Beat Stimulation
title_sort Workplace Stress Management using EEG-fNIRS Data Fusion and Binaural Beat Stimulation
topic Occupational stress
Stress mitigation
Binaural beats
Functional connectivity
Cortisol level
Functional near-infrared spectroscopy (fNIRS)
Electroencephalography (EEG)
Data fusion
url http://hdl.handle.net/11073/25462