{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T04:31:12Z","timestamp":1790051472179,"version":"4.0.1"},"reference-count":83,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Digital Wellness Platform, National Electronics and Computer Technology Center, Thailand","award":["P2150802"],"award-info":[{"award-number":["P2150802"]}]},{"DOI":"10.13039\/501100022536","name":"Faculty of Science, Prince of Songkla University","doi-asserted-by":"publisher","award":["SCI6404003S"],"award-info":[{"award-number":["SCI6404003S"]}],"id":[{"id":"10.13039\/501100022536","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Mental health, especially stress, plays a crucial role in the quality of life. During different phases (luteal and follicular phases) of the menstrual cycle, women may exhibit different responses to stress from men. This, therefore, may have an impact on the stress detection and classification accuracy of machine learning models if genders are not taken into account. However, this has never been investigated before. In addition, only a handful of stress detection devices are scientifically validated. To this end, this work proposes stress detection and multilevel stress classification models for unspecified and specified genders through ECG and EEG signals. Models for stress detection are achieved through developing and evaluating multiple individual classifiers. On the other hand, the stacking technique is employed to obtain models for multilevel stress classification. ECG and EEG features extracted from 40 subjects (21 females and 19 males) were used to train and validate the models. In the low&amp;high combined stress conditions, RBF-SVM and kNN yielded the highest average classification accuracy for females (79.81%) and males (73.77%), respectively. Combining ECG and EEG, the average classification accuracy increased to at least 87.58% (male, high stress) and up to 92.70% (female, high stress). For multilevel stress classification from ECG and EEG, the accuracy for females was 62.60% and for males was 71.57%. This study shows that the difference in genders influences the classification performance for both the detection and multilevel classification of stress. The developed models can be used for both personal (through ECG) and clinical (through ECG and EEG) stress monitoring, with and without taking genders into account.<\/jats:p>","DOI":"10.1371\/journal.pone.0291070","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T13:35:16Z","timestamp":1693575316000},"page":"e0291070","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":44,"title":["ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0621-5240","authenticated-orcid":true,"given":"Apit","family":"Hemakom","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danita","family":"Atiwiwat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pasin","family":"Israsena","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,9,1]]},"reference":[{"key":"pone.0291070.ref001","first-page":"840","article-title":"Emotion and blood pressure","volume":"105","author":"G Innes","year":"1959","journal-title":"Br. J. Psychiatry"},{"issue":"6","key":"pone.0291070.ref002","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.1016\/S0735-1097(00)00595-7","article-title":"Effects of controlled breathing, mental activity and mental stress with or without verbalization on heart rate variability","volume":"35","author":"L Bernardi","year":"2000","journal-title":"J. Am. College Cardiol"},{"key":"pone.0291070.ref003","first-page":"109","article-title":"Functional analysis of mental stress based on physiological data of GSR sensor","volume":"37","author":"R Sahoo","year":"2015","journal-title":"Adv. Intel. Syst. Comput"},{"issue":"9","key":"pone.0291070.ref004","doi-asserted-by":"crossref","first-page":"11305","DOI":"10.1007\/s11042-016-3444-9","article-title":"Multi-level assessment model for wellness service based on human mental stress level. Multimed","volume":"76","author":"JY Yoon","year":"2017","journal-title":"Tools Appl"},{"key":"pone.0291070.ref005","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1146\/annurev.clinpsy.1.102803.144141","article-title":"Stress and health: psychological, behavioral, and biological determinants","volume":"1","author":"N Schneiderman","year":"2005","journal-title":"Annu. Rev. Clin. Psychol"},{"issue":"9","key":"pone.0291070.ref006","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1053\/euhj.1998.1084","article-title":"Heart rate variability and heart rate in healthy volunteers. Is the female autonomic nervous system cardioprotective?","volume":"19","author":"D Ramaekers","year":"1995","journal-title":"Eur. Heart. J"},{"key":"pone.0291070.ref007","first-page":"1","article-title":"The Female Heart: Sex Differences in the Dynamics of ECG in Response to Stress","volume":"9","author":"T Adjei","journal-title":"Front. Physiol. 2018"},{"issue":"1","key":"pone.0291070.ref008","doi-asserted-by":"crossref","first-page":"4","DOI":"10.4103\/0972-6748.98407","article-title":"Gender differences in stress response: Role of developmental and biological determinants","volume":"20","author":"R Verma","year":"2011","journal-title":"Ind. Psychiatry. J"},{"issue":"3","key":"pone.0291070.ref009","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1037\/0033-295X.107.3.411","article-title":"Biobehavioral responses to stress in females: tend-and-befriend, not fight-or-flight","volume":"107","author":"SE Taylor","year":"2000","journal-title":"Psychol. Rev"},{"issue":"3","key":"pone.0291070.ref010","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1016\/S0008-6363(01)00508-9","article-title":"Gender, sex hormones and autonomic nervous control of the cardiovascular system","volume":"53","author":"AM Dart","year":"2002","journal-title":"Cardiovasc. Res"},{"issue":"2","key":"pone.0291070.ref011","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1023\/A:1014950618756","article-title":"The brief stress and coping inventory: a useful stress management instrument","volume":"9","author":"RH Rahe","year":"2002","journal-title":"Int. J. Stress Manage"},{"key":"pone.0291070.ref012","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/978-3-658-11994-2_14","volume-title":"Methodological Issues of Longitudinal Surveys","author":"C Gross","year":"2016"},{"issue":"4","key":"pone.0291070.ref013","doi-asserted-by":"crossref","first-page":"385","DOI":"10.2307\/2136404","article-title":"A global measure of perceived stress","volume":"24","author":"S Cohen","year":"1983","journal-title":"J. Health Soc. Behav"},{"issue":"1","key":"pone.0291070.ref014","first-page":"26","article-title":"The LF\/HF ratio does not accurately measure cardiac sympathovagal balance","volume":"26","author":"G. Billman","year":"2013","journal-title":"Front. Physiol"},{"issue":"3","key":"pone.0291070.ref015","first-page":"1835","article-title":"Analysis of Stroop Color Word Test-Based Human Stress Detection using Electrocardiography and Heart Rate Variability Signals","volume":"39","author":"P Karthikeyan","year":"2013","journal-title":"AJSE"},{"key":"pone.0291070.ref016","first-page":"19","article-title":"An Electrocardiogram Acquisition and Analysis System for Detection of Human Stress","author":"J Fan","year":"2019","journal-title":"Proceedings of the 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Suzhou, China"},{"key":"pone.0291070.ref017","first-page":"3","article-title":"A stress recognition system using HRV parameters and machine learning techniques","author":"G Giannakakis","year":"2019","journal-title":"Proceedings of the 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), Cambridge, UK"},{"key":"pone.0291070.ref018","first-page":"13","volume-title":"Pervasive Computing Paradigms for Mental Health","author":"Springer","year":"2016"},{"key":"pone.0291070.ref019","first-page":"25","article-title":"Modeling Perceived Stress via HRV and Accelerometer Sensor Streams. In: Proceedings of the 2015 37th Annu. Int. Conf","author":"M Wu","year":"2015","journal-title":"IEEE Eng. Med. Biol. Soc. (EMBC), Milan, Italy"},{"issue":"1","key":"pone.0291070.ref020","article-title":"The ten-twenty electrode system of the international federation","volume":"10","author":"HH Jasper","year":"1958","journal-title":"Electroenceph. Clin. Neurophysiol"},{"key":"pone.0291070.ref021","doi-asserted-by":"crossref","first-page":"113472","DOI":"10.1016\/j.chaos.2023.113472","article-title":"QLBP: Dynamic patterns-based feature extraction functions for automatic detection of mental health and cognitive conditions using EEG signals","volume":"172","author":"G Tasci","year":"2023","journal-title":"Chaos Solit. Fractals"},{"key":"pone.0291070.ref022","first-page":"1","article-title":"Mental performance classification using fused multilevel feature generation with EEG signals","author":"E Aydemir","year":"2022","journal-title":"Int. J. Healthc. Manag"},{"key":"pone.0291070.ref023","doi-asserted-by":"crossref","first-page":"120306","DOI":"10.1016\/j.eswa.2023.120306","article-title":"Automated mental arithmetic performance detection using quantum pattern- and triangle pooling techniques with EEG signals","volume":"227","author":"N Baygin","year":"2023","journal-title":"Expert Syst. Appl"},{"key":"pone.0291070.ref024","volume-title":"Fisch and Spehlmann\u2019s EEG Primer: Basic Principles of Digital and Analog EEG","author":"BJ Fisch","year":"1999"},{"key":"pone.0291070.ref025","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.biopsycho.2004.03.008","article-title":"What does the prefrontal cortex do\u201d in affect: Perspectives on frontal EEG asymmetry research","volume":"67","author":"RJ Davidson","year":"2004","journal-title":"Biol. Psychiatry"},{"key":"pone.0291070.ref026","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/0013-4694(90)90015-C","article-title":"Changes in EEG mean frequency and spectral purity during spontaneous alpha blocking. Electroencephalogr","volume":"76","author":"II Goncharova","year":"1990","journal-title":"Clin. Neurophysiol"},{"key":"pone.0291070.ref027","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.jad.2015.05.015","article-title":"EEG correlates of the severity of posttraumatic stress symptoms: A systematic review of the dimensional PTSD literature","volume":"183","author":"I Lobo","year":"2015","journal-title":"J. Affect. Disord"},{"key":"pone.0291070.ref028","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s11517-017-1733-8","article-title":"Towards multilevel mental stress assessment using SVM with ECOC: An EEG approach","volume":"56","author":"F Al-shargie","year":"2018","journal-title":"Med. Biol. Eng. Comput"},{"key":"pone.0291070.ref029","first-page":", 14","volume-title":"Proceedings of the 24th International Conference","author":"A Baghdadi","year":"2017"},{"issue":"1","key":"pone.0291070.ref030","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"DEAP: A Database for Emotion Analysis; using physiological signals","volume":"3","author":"S Koelstra","year":"2012","journal-title":"IEEE Trans. Affect"},{"issue":"7","key":"pone.0291070.ref031","doi-asserted-by":"crossref","first-page":"1886","DOI":"10.3390\/s20071886","article-title":"EEG based Classification of Long-term Stress Using Psychological Labeling","volume":"20","author":"SMU Saeed","year":"2020","journal-title":"Sensors"},{"key":"pone.0291070.ref032","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1587\/transinf.2016EDL8248","article-title":"Quantification of human stress using commercially available single channel EEG Headset","volume":"100","author":"SMU Saeed","year":"2017","journal-title":"IEICE Trans. Inf. Syst"},{"key":"pone.0291070.ref033","first-page":"66","article-title":"On identification of driving-induced stress using electroencephalogram signals: A framework based on wearable safety-critical scheme and machine learning","volume":"53","author":"Z Halim","journal-title":"Inf. Fusion. 2020"},{"key":"pone.0291070.ref034","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1007\/s12652-020-02381-5","article-title":"Gauging human visual interest using multiscale entropy analysis of EEG signals","volume":"12","author":"M Fraiwan","year":"2021","journal-title":"J. Ambient. Intell. Humaniz. Comput"},{"key":"pone.0291070.ref035","first-page":"22","article-title":"The development of intelligent models for detection towards real-world applications","author":"A Hemakom","year":"2022","journal-title":"Proceeding of 2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE). Bangkok, Thailand"},{"key":"pone.0291070.ref036","article-title":"Physiological-based smart stress detector using machine learning algorithms","author":"M Rosales","year":"2019","journal-title":"Proceedings of the 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Laoag, Philippines"},{"issue":"5","key":"pone.0291070.ref037","doi-asserted-by":"crossref","first-page":"2155","DOI":"10.1109\/JBHI.2023.3239305","article-title":"Stress detection through wrist-based electrodermal activity monitoring and machine learning","volume":"27","author":"L Zhu","year":"2023","journal-title":"IEEE J. Biomed. Health Inform"},{"issue":"4","key":"pone.0291070.ref038","first-page":"285","article-title":"Development of a stress classification model using deep belief networks for stress monitoring","volume":"23","author":"S-H Song","year":"2017"},{"issue":"2","key":"pone.0291070.ref039","doi-asserted-by":"crossref","first-page":"172","DOI":"10.3390\/brainsci12020172","article-title":"Associations between heart rate variability and brain activity during a working memory task: A preliminary electroencephalogram study on depression and anxiety disorder","volume":"12","author":"D Lee","year":"2022","journal-title":"Brain Sci"},{"key":"pone.0291070.ref040","first-page":"A","article-title":"Subgenual anterior cingulate (BA25) activity covaries with changes in cardiac vagal tone during affective set shifting in healthy adults","volume":"70","author":"RD Lane","year":"2008","journal-title":"Psychosom. Med"},{"key":"pone.0291070.ref041","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.neuroimage.2008.07.056","article-title":"Neural correlates of heart rate variability during emotion","volume":"44","author":"RD Lane","year":"2009","journal-title":"Neuroimage"},{"key":"pone.0291070.ref042","first-page":"A","article-title":"Anatomical correlates of autonomic control during a motor task","volume":"69","author":"AC Nugent","year":"2007","journal-title":"Psychosom. Med"},{"key":"pone.0291070.ref043","first-page":"A","article-title":"Alterations in neural correlates of autonomic control in females with major depressive disorder","volume":"70","author":"AC Nugent","year":"2008","journal-title":"Psychosom Med"},{"key":"pone.0291070.ref044","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1111\/1469-8986.2004.00179.x","article-title":"Regional cerebral blood flow correlates with heart period and high-frequency heart period variability during working memory tasks: Implications for the cortical and subcortical regulation of cardiac autonomic activity","volume":"41","author":"PJ Gianaros","year":"2004","journal-title":"Psychophysiology"},{"issue":"743","key":"pone.0291070.ref045","first-page":"1","article-title":"A critical review of consumer wearables, mobile applications, and equipment for providing biofeedback, monitoring stress, and sleep in physically active populations","volume":"9","author":"JM Peake","year":"2018","journal-title":"Front. Physiol"},{"issue":"1","key":"pone.0291070.ref046","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s40537-020-00289-7","article-title":"A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals","volume":"7","author":"V Doma","year":"2020","journal-title":"J. Big Data"},{"key":"pone.0291070.ref047","doi-asserted-by":"crossref","first-page":"13545","DOI":"10.1109\/ACCESS.2017.2723622","article-title":"Machine Learning Framework for the Detection of Mental Stress at Multiple Levels","volume":"5","author":"AR Subhani","year":"2017","journal-title":"IEEE Access"},{"key":"pone.0291070.ref048","first-page":"15","article-title":"Classification of mental arithmetic and resting-state based on Ear-EEG","author":"SI Choi","year":"2018","journal-title":"Proceedings of the 2018 6th International Conference on Brain-Computer Interface (BCI), Jeongseon, South Korea"},{"key":"pone.0291070.ref049","first-page":"26","article-title":"Recognition of Real-Scene Stress in Examination with Heart Rate Features","author":"J Zhang","year":"2017","journal-title":"Proceedings of the 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China"},{"key":"pone.0291070.ref050","first-page":"1","article-title":"Classifying Multi-Level Stress Responses from Brain Cortical EEG in Nurses and Non-Health Professionals Using Machine Learning Auto Encoder","volume":"9","author":"A Akella","year":"2021","journal-title":"IEEE J. Transl. Eng"},{"issue":"1","key":"pone.0291070.ref051","first-page":"295","article-title":"Performance comparison of adaboost based weak classifiers in network intrusion detection","volume":"3","author":"P Natesan","year":"2012","journal-title":"J. Inf. Syst"},{"key":"pone.0291070.ref052","author":"HN Choi","year":"2017"},{"key":"pone.0291070.ref053","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.procs.2018.10.392","article-title":"Classification of EEG Data using k-Nearest Neighbor approach for Concealed Information Test","volume":"143","author":"A Bablani","year":"2018","journal-title":"Procedia Comput. Sci"},{"key":"pone.0291070.ref054","volume-title":"Forest Weights, In-Bag (IB) and Out-of-Bag (OOB) Ensembles","author":"H Ishwaran","year":"2023"},{"key":"pone.0291070.ref055","first-page":"23","article-title":"A review on feature selection and feature extraction for text classification","author":"FP Shah","year":"2016","journal-title":"Proceedings of the 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India"},{"key":"pone.0291070.ref056","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/978-1-4302-5990-9_3","volume-title":"Efficient Learning Machines","author":"M Awad","year":"2015"},{"key":"pone.0291070.ref057","first-page":"165","volume-title":"Introduction to Machine Learning","author":"A Smola","year":"2008"},{"key":"pone.0291070.ref058","first-page":"21","article-title":"Using Stacking Approaches for Machine Learning Models","author":"B. Pavlyshenko","year":"2018","journal-title":"Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukrain"},{"key":"pone.0291070.ref059","first-page":"1205","article-title":"Efficient Feature Selection via Analysis of Relevance and Redundancy. J. Mach","volume":"5","author":"L Yu","year":"2004","journal-title":"Learn. Res"},{"issue":"7","key":"pone.0291070.ref060","doi-asserted-by":"crossref","first-page":"e6416","DOI":"10.1371\/journal.pone.0006416","article-title":"SlimPLS: a method for feature selection in gene expression-based disease classification","volume":"4","author":"M Gutkin","year":"2009","journal-title":"PLOS ONE"},{"key":"pone.0291070.ref061","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/1751-0759-4-6","article-title":"The Thai version of the PSS-10: An Investigation of its psychometric properties","volume":"4","author":"N Wongpakaran","year":"2010","journal-title":"Biopsychosoc. Med"},{"issue":"1","key":"pone.0291070.ref062","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/0002-9149(91)90112-X","article-title":"Physiologic neuroendocrine arousal by mental arithmetic stress test in healthy subjects","volume":"67","author":"K Ushiyama","year":"1991","journal-title":"Am. J. Cardiol"},{"key":"pone.0291070.ref063","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/0091-3057(89)90204-9","article-title":"Characterization of Stress Reactions to the Stroop Color Word Test","volume":"32","author":"HM Tulen","year":"1989","journal-title":"Pharmacol. Biochem. Behav"},{"key":"pone.0291070.ref064","first-page":"354","article-title":"Psychophysiological Stress and EMG Activity of The Trapezius Muscle","volume":"1","author":"U Lundberg","year":"1994","journal-title":"IJBM"},{"key":"pone.0291070.ref065","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.artres.2014.02.002","article-title":"The Acute Effects of Mental Arithmetic, Cold Pressor and Maximal Voluntary Contraction on Arterial Stiffness in Young Healthy Subjects","volume":"8","author":"N-A Jatoi","year":"2014","journal-title":"Artery. Res"},{"issue":"5","key":"pone.0291070.ref066","doi-asserted-by":"crossref","first-page":"292","DOI":"10.3390\/diagnostics10050292","article-title":"An Effective Mental Stress State Detection and Evaluation System Using Minimum Number of Frontal Brain Electrodes","volume":"10","author":"O. Attallah","year":"2020","journal-title":"Diagnostics"},{"issue":"12","key":"pone.0291070.ref067","doi-asserted-by":"crossref","first-page":"e113618","DOI":"10.1371\/journal.pone.0113618","article-title":"Stress Effects on Mood, HPA Axis, and Autonomic Response: Comparison of Three Psychosocial Stress Paradigms","volume":"9","author":"GE Giles","year":"2014","journal-title":"PLOS ONE"},{"key":"pone.0291070.ref068","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1111\/j.1469-8986.1991.tb03393.x","article-title":"What Do Arithmetic Stresss Measure? Protocol Variation and Cardiovascular Response","volume":"28","author":"W. Linden","year":"1994","journal-title":"Psychophysiology"},{"key":"pone.0291070.ref069","author":"A Hemakom","year":"2023","journal-title":"ECG&EEG Stress Features, kaggle, Dataset"},{"key":"pone.0291070.ref070","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2016\/5136705","article-title":"Stress Detection Using Low Cost Heart Rate Sensors","volume":"2016","author":"MI Salai","year":"2016","journal-title":"J. Healthc. Eng"},{"key":"pone.0291070.ref071","first-page":"16","article-title":"Detection of mental stress due to oral academic examination via ultra-short-term HRV analysis","author":"R Castaldo","year":"2016","journal-title":"Proceedings of the 38th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), Florida, USA"},{"key":"pone.0291070.ref072","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.eswa.2018.06.027","article-title":"Brain-computer interface for workload estimation: Assessment of mental efforts in learning processes","volume":"112","author":"A Zammouri","journal-title":"Expert Syst. Appl. 2018"},{"key":"pone.0291070.ref073","doi-asserted-by":"crossref","first-page":"376","DOI":"10.3390\/brainsci9120376","article-title":"A Hybrid Feature Pool-Based Emotional Stress State Detection Algorithm Using EEG Signals","volume":"9","author":"MJ Hasan","year":"2019","journal-title":"Brain Sci"},{"issue":"10","key":"pone.0291070.ref074","doi-asserted-by":"crossref","first-page":"3882","DOI":"10.1364\/BOE.7.003882","article-title":"Mental stress assessment using simultaneous measurement of EEG and fNIRS","volume":"7","author":"F Al-Shargie","year":"2016","journal-title":"Biomed. Opt. Express"},{"key":"pone.0291070.ref075","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.autcon.2018.05.027","article-title":"EEG-based workers\u2019 stress recognition at construction sites","volume":"93","author":"H Jebelli","journal-title":"Autom. Constr. 2018"},{"issue":"4","key":"pone.0291070.ref076","doi-asserted-by":"crossref","first-page":"e0174949","DOI":"10.1371\/journal.pone.0174949","article-title":"An evaluation of mental workload with frontal EEG","volume":"12","author":"WKY So","year":"2017","journal-title":"PLOS ONE"},{"issue":"2","key":"pone.0291070.ref077","first-page":"107","article-title":"Stress and Perception of Emotional Stimuli","volume":"9","author":"R. Khosrowabadi","year":"2018","journal-title":"Long-term Stress Rewiring the Brain. Basic Clin. Neurosci"},{"key":"pone.0291070.ref078","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/1049257","article-title":"Selection of Neural Oscillatory Features for Human Stress Classification with Single Channel EEG. Headset","volume":"2018","author":"SM Umar Saeed","year":"2018","journal-title":"BioMed Res. Int"},{"key":"pone.0291070.ref079","first-page":"239","volume-title":"Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience. FAC 2009. Lecture Notes in Computer Science()","author":"Springer"},{"key":"pone.0291070.ref080","article-title":"Estimating workload using EEG spectral power and ERPs in the n-back task","volume":"9","author":"A-M Brouwer","year":"2012","journal-title":"J. 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