{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:17:21Z","timestamp":1782843441931,"version":"3.54.5"},"reference-count":59,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T00:00:00Z","timestamp":1724112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"American University of Sharjah","award":["FRG-CE05"],"award-info":[{"award-number":["FRG-CE05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Assessments of stress can be performed using physiological signals, such as electroencephalograms (EEGs) and galvanic skin response (GSR). Commercialized systems that are used to detect stress with EEGs require a controlled environment with many channels, which prohibits their daily use. Fortunately, there is a rise in the utilization of wearable devices for stress monitoring, offering more flexibility. In this paper, we developed a wearable monitoring system that integrates both EEGs and GSR. The novelty of our proposed device is that it only requires one channel to acquire both physiological signals. Through sensor fusion, we achieved an improved accuracy, lower cost, and improved ease of use. We tested the proposed system experimentally on twenty human subjects. We estimated the power spectrum of the EEG signals and utilized five machine learning classifiers to differentiate between two levels of mental stress. Furthermore, we investigated the optimum electrode location on the scalp when using only one channel. Our results demonstrate the system\u2019s capability to classify two levels of mental stress with a maximum accuracy of 70.3% when using EEGs alone and 84.6% when using fused EEG and GSR data. This paper shows that stress detection is reliable using only one channel on the prefrontal and ventrolateral prefrontal regions of the brain.<\/jats:p>","DOI":"10.3390\/s24165373","type":"journal-article","created":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T09:13:48Z","timestamp":1724145228000},"page":"5373","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["One-Channel Wearable Mental Stress State Monitoring System"],"prefix":"10.3390","volume":"24","author":[{"given":"Lamis","family":"Abdul Kader","sequence":"first","affiliation":[{"name":"Biomedical Engineering Graduate Program, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5792-8032","authenticated-orcid":false,"given":"Fares","family":"Al-Shargie","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation and Movement Sciences, Rutgers University, Newark, NJ 07107, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8244-2165","authenticated-orcid":false,"given":"Usman","family":"Tariq","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9685-4937","authenticated-orcid":false,"given":"Hasan","family":"Al-Nashash","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1038\/nrn2639","article-title":"Effects of stress throughout the lifespan on the brain, behaviour and cognition","volume":"10","author":"Lupien","year":"2009","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1152\/physrev.00041.2006","article-title":"Physiology and neurobiology of stress and adaptation: Central role of the brain","volume":"87","author":"McEwen","year":"2007","journal-title":"Physiol. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Masri, G., Al-Shargie, F., Tariq, U., Almughairbi, F., Babiloni, F., and Al-Nashash, H. (2023). Mental Stress Assessment in the Workplace: A Review. IEEE Trans. Affect. Comput., 1\u201320.","DOI":"10.1109\/TAFFC.2023.3312762"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1136\/bmj.1.4667.1383","article-title":"Stress and the general adaptation syndrome","volume":"1","author":"Selye","year":"1950","journal-title":"Br. Med. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1109\/TAFFC.2019.2927337","article-title":"Review on psychological stress detection using biosignals","volume":"13","author":"Giannakakis","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1109\/TAFFC.2017.2699633","article-title":"New methods for stress assessment and monitoring at the workplace","volume":"10","author":"Carneiro","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.neuroimage.2010.11.068","article-title":"Stress-induced reduction in reward-related prefrontal cortex function","volume":"55","author":"Ossewaarde","year":"2011","journal-title":"Neuroimage"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Vanhollebeke, G., Kappen, M., De Raedt, R., Baeken, C., van Mierlo, P., and Vanderhasselt, M.-A. (2023). Effects of acute psychosocial stress on source level EEG power and functional connectivity measures. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-35808-y"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vanhollebeke, G., De Smet, S., De Raedt, R., Baeken, C., van Mierlo, P., and Vanderhasselt, M.-A. (2022). The neural correlates of psychosocial stress: A systematic review and meta-analysis of spectral analysis EEG studies. Neurobiol. Stress, 18.","DOI":"10.1016\/j.ynstr.2022.100452"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Katmah, R., Al-Shargie, F., Tariq, U., Babiloni, F., Al-Mughairbi, F., and Al-Nashash, H. (2021). A review on mental stress assessment methods using EEG signals. Sensors, 21.","DOI":"10.20944\/preprints202107.0255.v1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Al-Shargie, F.M., Tang, T.B., Badruddin, N., and Kiguchi, M. (2015, January 6\u20138). Mental stress quantification using EEG signals. Proceedings of the International Conference for Innovation in Biomedical Engineering and Life Sciences: ICIBEL2015, Putrajaya, Malaysia.","DOI":"10.1007\/978-981-10-0266-3_4"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Attallah, O. (2020). An Effective Mental Stress State Detection and Evaluation System Using Minimum Number of Frontal Brain Electrodes. Diagnostics, 10.","DOI":"10.3390\/diagnostics10050292"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hou, X., Liu, Y., Sourina, O., Tan, Y.R.E., Wang, L., and M\u00fcller-Wittig, W. (2015, January 9\u201312). EEG Based Stress Monitoring. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Kowloon Tong, Hong Kong.","DOI":"10.1109\/SMC.2015.540"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Karthikeyan, P., Murugappan, M., and Yaacob, S. (2011, January 4\u20136). A review on stress inducement stimuli for assessing human stress using physiological signals. Proceedings of the 2011 IEEE 7th International Colloquium on Signal Processing and Its Applications, Penang, Malaysia.","DOI":"10.1109\/CSPA.2011.5759914"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jun, G., and Smitha, K.G. (2016, January 9\u201312). EEG based stress level identification. Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary.","DOI":"10.1109\/SMC.2016.7844738"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shanmugasundaram, G., Yazhini, S., Hemapratha, E., and Nithya, S. (2019, January 29\u201330). A comprehensive review on stress detection techniques. Proceedings of the 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India.","DOI":"10.1109\/ICSCAN.2019.8878795"},{"key":"ref_17","first-page":"28","article-title":"Stress Sensor Prototype: Determining the Stress Level in using a Computer through Validated Self-Made Heart Rate (HR) and Galvanic Skin Response (GSR) Sensors and Fuzzy Logic Algorithm","volume":"5","author":"Cantara","year":"2016","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1111\/j.1600-0846.2010.00459.x","article-title":"Electrodermal activity by DC potential and AC conductance measured simultaneously at the same skin site","volume":"17","author":"Grimnes","year":"2011","journal-title":"Skin Res. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1088\/0967-3334\/35\/6\/1011","article-title":"Estimation of skin conductance at low frequencies using measurements at higher frequencies for EDA applications","volume":"35","author":"Nordbotten","year":"2014","journal-title":"Physiol. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Das, P., Das, A., Tibarewala, D., and Khasnobish, A. (2016, January 21\u201323). Design and development of portable galvanic skin response acquisition and analysis system. Proceedings of the 2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI), Kolkata, India.","DOI":"10.1109\/ICICPI.2016.7859688"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Posada-Quintero, H.F., and Chon, K.H. (2020). Innovations in electrodermal activity data collection and signal processing: A systematic review. Sensors, 20.","DOI":"10.3390\/s20020479"},{"key":"ref_22","unstructured":"Kim, J., Kwon, S., Seo, S., and Park, K. (2014, January 26\u201330). Highly wearable galvanic skin response sensor using flexible and conductive polymer foam. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8639","DOI":"10.1109\/JSEN.2021.3050875","article-title":"Design and Evaluation of an Electrodermal Activity Sensor (EDA) With Adaptive Gain","volume":"21","author":"Banganho","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sequeira, H., and Roy, J.-C. (1993). Cortical and hypothalamo-limbic control of electrodermal responses. Progress in Electrodermal Research, Springer.","DOI":"10.1007\/978-1-4615-2864-7_8"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Boucsein, W. (2012). Principles of electrodermal phenomena. Electrodermal Activity, Springer.","DOI":"10.1007\/978-1-4614-1126-0"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Secerbegovic, A., Ibric, S., Nisic, J., Suljanovic, N., and Mujcic, A. (2017). Mental workload vs. stress differentiation using single-channel EEG. CMBEBIH 2017, Springer.","DOI":"10.1007\/978-981-10-4166-2_78"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITS.2005.848368","article-title":"Detecting stress during real-world driving tasks using physiological sensors","volume":"6","author":"Healey","year":"2005","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.bbe.2019.01.004","article-title":"A survey of machine learning techniques in physiology based mental stress detection systems","volume":"39","author":"Panicker","year":"2019","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fowles, D.C. (1993). Electrodermal activity and antisocial behavior: Empirical findings and theoretical issues. Progress in Electrodermal Research, Springer.","DOI":"10.1007\/978-1-4615-2864-7_15"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Anusha, A., Jose, J., Preejith, S., Jayaraj, J., and Mohanasankar, S. (2018). Physiological signal based work stress detection using unobtrusive sensors. Biomed. Phys. Eng. Express, 4.","DOI":"10.1088\/2057-1976\/aadbd4"},{"key":"ref_31","first-page":"13","article-title":"A review of EEG sensors used for data acquisition","volume":"12","author":"Tyagi","year":"2012","journal-title":"J. Comput. Appl. (IJCA)"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e210","DOI":"10.2196\/jmir.9410","article-title":"Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study","volume":"20","author":"Sano","year":"2018","journal-title":"J. Med. Internet Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sani, M., Norhazman, H., Omar, H., Zaini, N., and Ghani, S. (2014, January 12\u201314). Support vector machine for classification of stress subjects using EEG signals. Proceedings of the 2014 IEEE Conference on Systems, Process and Control (ICSPC 2014), Kuala Lumpur, Malaysia.","DOI":"10.1109\/SPC.2014.7086243"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4409","DOI":"10.1007\/s12652-019-01571-0","article-title":"Multilevel assessment of mental stress via network physiology paradigm using consumer wearable devices","volume":"12","author":"Zanetti","year":"2019","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ahn, J.W., Ku, Y., and Kim, H.C. (2019). A Novel Wearable EEG and ECG Recording System for Stress Assessment. Sensors, 19.","DOI":"10.3390\/s19091991"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.jbi.2015.11.007","article-title":"Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review","volume":"59","author":"Alberdi","year":"2016","journal-title":"J. Biomed. Inform."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7120","DOI":"10.3390\/s140407120","article-title":"Wearable biomedical measurement systems for assessment of mental stress of combatants in real time","volume":"14","author":"Seoane","year":"2014","journal-title":"Sensors"},{"key":"ref_38","unstructured":"Baumgartl, H., Fezer, E., and Buettner, R. (2020, January 10\u201314). Two-Level Classification of Chronic Stress in EEG Recordings Two-Level Classification of Chronic Stress Using Machine Learning on Resting-State EEG Recordings. Proceedings of the AMCIS 2020 Proceedings: 25th Americas Conference on Information Systems, Salt Lake City, UT, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Scarpina, F., and Tagini, S. (2017). The Stroop Color and Word Test. Front. Psychol., 8.","DOI":"10.3389\/fpsyg.2017.00557"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Bousefsaf, F., Maaoui, C., and Pruski, A. (2013, January 5\u20138). Remote assessment of the Heart Rate Variability to detect mental stress. Proceedings of the 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops (PervasiveHealth), Venice, Italy.","DOI":"10.4108\/pervasivehealth.2013.252181"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1002\/pits.10047","article-title":"Patterns of performance on the Stroop Color and Word Test in children with learning, attentional, and psychiatric disabilities","volume":"39","author":"Golden","year":"2002","journal-title":"Psychol. Sch."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1002\/hbm.20127","article-title":"Involvement of the inferior frontal junction in cognitive control: Meta-analyses of switching and Stroop studies","volume":"25","author":"Derrfuss","year":"2005","journal-title":"Hum. Brain Mapp."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1037\/0033-2909.120.1.3","article-title":"The emotional Stroop task and psychopathology","volume":"120","author":"Williams","year":"1996","journal-title":"Psychol. Bull."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1310\/sci18-00038","article-title":"Dual-Task Obstacle Crossing Training Could Immediately Improve Ability to Control a Complex Motor Task and Cognitive Activity in Chronic Ambulatory Individuals With Spinal Cord Injury","volume":"25","author":"Amatachaya","year":"2019","journal-title":"Top. Spinal Cord Inj. Rehabil."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1109\/TNSRE.2023.3239913","article-title":"Mental Stress Management Using fNIRS Directed Connectivity and Audio Stimulation","volume":"31","author":"Katmah","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3552","DOI":"10.1364\/BOE.455097","article-title":"Stress management using fNIRS and binaural beats stimulation","volume":"13","author":"Katmah","year":"2022","journal-title":"Biomed. Opt. Express"},{"key":"ref_47","unstructured":"Yoo, J., Park, J.W., and Kim, S.J. (2016, January 27\u201329). Development of User-friendly Bio-signal Acquisition System Based on LabVIEW. Proceedings of the 2016 IEEE International Conference on Consumer Electronics (ICCE), Nantou County, Taiwan."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chi, Y.M., Deiss, S.R., and Cauwenberghs, G. (2009, January 3\u20135). Non-contact low power EEG\/ECG electrode for high density wearable biopotential sensor networks. Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks, Berkeley, CA, USA.","DOI":"10.1109\/BSN.2009.52"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4857","DOI":"10.1109\/TIE.2010.2103538","article-title":"A Stress-Detection System Based on Physiological Signals and Fuzzy Logic","volume":"58","author":"Sierra","year":"2011","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_50","unstructured":"Cohen, S., Kamarck, T., and Mermelstein, R. (1994). Perceived stress scale. Measuring Stress: A Guide for Health and Social Scientists, Oxford University Press."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s13246-015-0333-x","article-title":"Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques","volume":"38","author":"Amin","year":"2015","journal-title":"Australas. Phys. Eng. Sci. Med."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hou, X., Liu, Y., Sourina, O., and Mueller-Wittig, W. (2015, January 7\u20139). CogniMeter: EEG-based emotion, mental workload and stress visual monitoring. Proceedings of the 2015 International Conference on Cyberworlds (CW), Visby, Sweden.","DOI":"10.1109\/CW.2015.58"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"58690U","DOI":"10.1117\/12.618478","article-title":"An overview of power spectral density (PSD) calculations","volume":"5869","author":"Richard","year":"2005","journal-title":"Proc. SPIE"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"115941","DOI":"10.1109\/ACCESS.2020.3004504","article-title":"EEG-based semantic vigilance level classification using directed connectivity patterns and graph theory analysis","volume":"8","author":"Hassanin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bobade, P., and Vani, M. (2020, January 15\u201317). Stress Detection with Machine Learning and Deep Learning using Multimodal Physiological Data. Proceedings of the 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India.","DOI":"10.1109\/ICIRCA48905.2020.9183244"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Badr, Y., Al-Shargie, F., Tariq, U., Babiloni, F., Al-Mughairbi, F., and Al-Nashash, H. (2023, January 24\u201327). Mental Stress Detection and Mitigation using Machine Learning and Binaural Beat Stimulation. Proceedings of the 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia.","DOI":"10.1109\/EMBC40787.2023.10340673"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Blanco, J.A., Vanleer, A.C., Calibo, T.K., and Firebaugh, S.L. (2019). Single-Trial Cognitive Stress Classification Using Portable Wireless Electroencephalography. Sensors, 19.","DOI":"10.3390\/s19030499"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Tsai, Y.-H., Wu, S.-K., Yu, S.-S., and Tsai, M.-H. (2022). Analyzing Brain Waves of Table Tennis Players with Machine Learning for Stress Classification. Appl. Sci., 12.","DOI":"10.3390\/app12168052"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Calibo, T.K., Blanco, J.A., and Firebaugh, S.L. (2013, January 6\u20139). Cognitive stress recognition. Proceedings of the 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Minneapolis, MN, USA.","DOI":"10.1109\/I2MTC.2013.6555658"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5373\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:39:41Z","timestamp":1760110781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5373"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,20]]},"references-count":59,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24165373"],"URL":"https:\/\/doi.org\/10.3390\/s24165373","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,20]]}}}