A review on evaluating mental stress by deep learning using EEG signals

Mental stress is a common problem that affects individuals all over the world. Stress reduces human functionality during routine work and may lead to severe health defects. Early detection of stress is important for preventing diseases and other negative health-related consequences of stress. Severa...

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Vydáno v:	Neural computing & applications Ročník 36; číslo 21; s. 12629 - 12654
Hlavní autoři:	Badr, Yara, Tariq, Usman, Al-Shargie, Fares, Babiloni, Fabio, Al Mughairbi, Fadwa, Al-Nashash, Hasan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.07.2024 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Artificial neural networks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Deep learning Electroencephalography Graph neural networks Graphical representations Image Processing and Computer Vision Machine learning Medical imaging Neural networks Probability and Statistics in Computer Science Psychological stress Review Deep learning CNN Hybrid model LSTM EEG Mental stress
ISSN:	0941-0643, 1433-3058
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Abstract	Mental stress is a common problem that affects individuals all over the world. Stress reduces human functionality during routine work and may lead to severe health defects. Early detection of stress is important for preventing diseases and other negative health-related consequences of stress. Several neuroimaging techniques have been utilized to assess mental stress, however, due to its ease of use, robustness, and non-invasiveness, electroencephalography (EEG) is commonly used. This paper aims to fill a knowledge gap by reviewing the different EEG-related deep learning algorithms with a focus on Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the evaluation of mental stress. The review focuses on data representation, individual deep neural network model architectures, hybrid models, and results amongst others. The contributions of the paper address important issues such as data representation and model architectures. Out of all reviewed papers, 67% used CNN, 9% LSTM, and 24% hybrid models. Based on the reviewed literature, we found that dataset size and different representations contributed to the performance of the proposed networks. Raw EEG data produced classification accuracy around 62% while using spectral and topographical representation produced up to 88%. Nevertheless, the roles of generalizability across different deep learning models and individual differences remain key areas of inquiry. The review encourages the exploration of innovative avenues, such as EEG data image representations concurrently with graph convolutional neural networks (GCN), to mitigate the impact of inter-subject variability. This novel approach not only allows us to harmonize structural nuances within the data but also facilitates the integration of temporal dynamics, thereby enabling a more comprehensive assessment of mental stress levels.
AbstractList	Mental stress is a common problem that affects individuals all over the world. Stress reduces human functionality during routine work and may lead to severe health defects. Early detection of stress is important for preventing diseases and other negative health-related consequences of stress. Several neuroimaging techniques have been utilized to assess mental stress, however, due to its ease of use, robustness, and non-invasiveness, electroencephalography (EEG) is commonly used. This paper aims to fill a knowledge gap by reviewing the different EEG-related deep learning algorithms with a focus on Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the evaluation of mental stress. The review focuses on data representation, individual deep neural network model architectures, hybrid models, and results amongst others. The contributions of the paper address important issues such as data representation and model architectures. Out of all reviewed papers, 67% used CNN, 9% LSTM, and 24% hybrid models. Based on the reviewed literature, we found that dataset size and different representations contributed to the performance of the proposed networks. Raw EEG data produced classification accuracy around 62% while using spectral and topographical representation produced up to 88%. Nevertheless, the roles of generalizability across different deep learning models and individual differences remain key areas of inquiry. The review encourages the exploration of innovative avenues, such as EEG data image representations concurrently with graph convolutional neural networks (GCN), to mitigate the impact of inter-subject variability. This novel approach not only allows us to harmonize structural nuances within the data but also facilitates the integration of temporal dynamics, thereby enabling a more comprehensive assessment of mental stress levels.
Author	Al Mughairbi, Fadwa Badr, Yara Tariq, Usman Babiloni, Fabio Al-Nashash, Hasan Al-Shargie, Fares
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CitedBy_id	crossref_primary_10_1016_j_bspc_2025_108124 crossref_primary_10_17816_humeco678196 crossref_primary_10_1007_s44163_025_00412_8 crossref_primary_10_3390_jcm14155357 crossref_primary_10_1016_j_inffus_2025_103368 crossref_primary_10_1109_TNSRE_2025_3576924 crossref_primary_10_1007_s00521_024_10207_0 crossref_primary_10_1109_TCPMT_2025_3539978 crossref_primary_10_1371_journal_pone_0319702 crossref_primary_10_1109_ACCESS_2025_3553932 crossref_primary_10_1007_s13042_024_02455_2 crossref_primary_10_13005_bpj_3052 crossref_primary_10_3390_s24216965 crossref_primary_10_1109_ACCESS_2025_3571437 crossref_primary_10_1016_j_chemolab_2025_105375 crossref_primary_10_46604_peti_2024_14819
Cites_doi	10.1016/j.bspc.2021.102741 10.1016/j.bspc.2020.101989 10.1016/j.apnr.2006.02.001 10.1016/j.bspc.2015.02.012 10.1007/s00521-022-07540-7 10.3389/frai.2020.00004 10.1155/2021/4620487 10.1007/s11517-017-1733-8 10.1109/ACCESS.2019.2917718 10.1186/s40708-021-00133-5 10.1016/j.knosys.2020.105596 10.1016/j.bspc.2017.07.022 10.29252/NIRP.BCN.9.2.107 10.1109/TAFFC.2019.2927337 10.1155/2022/7607592 10.1007/s00521-021-06352-5 10.1016/j.imu.2020.100412 10.1016/j.patcog.2017.10.013 10.1016/j.matpr.2021.05.659 10.1186/s12916-017-0805-9 10.1364/BOE.7.003882 10.1364/BOE.8.002583 10.1007/s12529-016-9562-y 10.3390/w11071387 10.1155/2020/7426461 10.1007/s11571-018-9496-y 10.1109/ACCESS.2019.2912200 10.1016/j.compbiomed.2021.104377 10.1371/journal.pone.0180944 10.1155/2018/1049257 10.1016/j.pjnns.2016.03.006 10.1016/j.ijpsycho.2018.02.005 10.1109/IJCNN.2011.6033297 10.3390/s20071886 10.1109/ICASSP.2018.8462243 10.1007/978-981-10-0266-3_4 10.1007/978-981-16-6887-6_3 10.1523/JNEUROSCI.3422-03.2004 10.1149/10701.16459ecst 10.2224/sbp.2013.41.9.1457 10.1016/j.neubiorev.2011.02.003 10.1109/JBHI.2019.2926407 10.1109/ICACCI.2018.8554715 10.1007/s00213-005-0147-8 10.29252/jncog.1.1.16 10.1109/SSD54932.2022.9955724 10.3390/s19050987 10.31224/osf.io/kaqew 10.1109/TNB.2015.2420576 10.1109/ICCSCE.2011.6190573 10.1007/978-3-030-49724-8_4 10.23919/ICACT.2018.8323716 10.23919/ICACT.2019.8702048 10.1088/0967-3334/36/7/1351 10.1016/j.dajour.2023.100211 10.3390/s20092505 10.1109/EMBC.2016.7591884 10.1109/ACCESS.2019.2927768 10.1111/j.1440-1681.2008.04904.x 10.1002/9780470987483.ch7 10.3390/electronics10222840 10.1186/s40537-021-00444-8 10.1109/ICSGRC.2012.6287173 10.1109/ICACCS.2013.6938735 10.1109/TNSRE.2022.3174821 10.1109/CSNT51715.2021.9509713 10.1007/s11906-001-0047-1 10.3390/s21093050 10.3390/s21155043 10.3390/brainsci9120376 10.13053/rcs-145-1-12 10.1046/j.1526-4610.1997.3708499.x 10.1007/978-3-030-59277-6_21 10.1111/1469-8986.3950585 10.1109/ICBDACI.2017.8070809 10.3390/ijerph15112461 10.3389/fpsyg.2020.01759 10.1007/978-981-19-6004-8_52 10.1088/1741-2552/ab0ab5 10.1002/capr.12439 10.1007/s00779-012-0593-3 10.1089/tmj.2017.0250 10.1007/978-3-030-00220-6_21 10.1007/978-1-4614-4984-3 10.1097/00006842-199903000-00014 10.1007/978-1-4842-6150-7 10.1007/978-3-540-30120-2_57 10.1080/10447318.2013.848320 10.1109/ICMLA.2017.0-110 10.1007/s00420-009-0467-9 10.1111/j.1469-8986.2012.01384.x 10.1109/SMC.2016.7844738 10.1007/978-3-319-47653-7 10.1109/ICSIPA.2015.7412205 10.3390/jmse11020259 10.1007/s11886-016-0798-6 10.3126/kumj.v10i3.8021 10.1109/I2MTC.2013.6555658 10.1080/02646838.2018.1540861 10.1016/j.physbeh.2015.09.032 10.5772/9651 10.1016/S0022-3999(97)00305-X 10.1109/TNNLS.2022.3159573 10.1016/j.asoc.2020.106954 10.1016/j.neuroimage.2011.07.030
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References	Abhishek, Nallavan (CR111) 2022; 3 Phutela, Relan, Gabrani, Kumaraguru, Samuel (CR115) 2022 Kamińska, Smółka, Zwoliński (CR77) 2021; 10 Esler (CR11) 2008; 35 CR39 Penchina, Sundaresan, Cheong, Martel (CR70) 2020 Al-shargie, Tang, Badruddin, Kiguchi (CR49) 2018; 56 CR35 Ong, Abdullah, Danaee, Soh, Soh, Japar (CR21) 2019; 37 Lataster (CR43) 2011; 58 Siuly, Li, Zhang (CR61) 2016 Bao, Yue, Rao (CR102) 2017 Le, Ho, Lee, Jung (CR105) 2019; 11 Geslani, Gaebelein (CR19) 2013; 41 Duffy, Shankardass, McAnulty, Als (CR62) 2017 CR48 CR47 Al-Shargie, Tang, Kiguchi (CR37) 2017; 8 Malviya, Mal (CR116) 2022 Ikram (CR121) 2023; 11 Shon, Im, Park, Lim, Jang, Kim (CR78) 2018; 15 Arsalan, Majid, Butt, Anwar (CR51) 2019; 23 Pruessner, Champagne, Meaney, Dagher (CR42) 2004; 24 Fu (CR110) 2022; 30 Park, Choi, Lee, Lee, An, Kim (CR86) 2011; 3 Sundaresan, Penchina, Cheong, Grace, Valero-Cabré, Martel (CR72) 2021; 8 Islam, Islam, Asraf (CR95) 2020; 20 Sengupta (CR101) 2020; 194 Castaldo, Melillo, Bracale, Caserta, Triassi, Pecchia (CR18) 2015; 18 CR54 CR50 Alonso, Romero, Ballester, Antonijoan, Mañanas (CR80) 2015; 36 Gu (CR100) 2018; 77 Bhatnagar, Khandelwal, Jain, Vyawahare (CR57) 2023; 7 Wang, Tong, Heng (CR118) 2019; 7 Abdul Hamid, Goyal, Bedi (CR104) 2021 Kotlęga, Gołąb-Janowska, Masztalewicz, Ciećwież, Nowacki (CR12) 2016; 50 Katmah, Al-Shargie, Tariq, Babiloni, Al-Mughairbi, Al-Nashash (CR31) 2021; 21 Fischer, Nater, Laferton (CR17) 2016 Alturki, AlSharabi, Abdurraqeeb, Aljalal (CR82) 2020; 20 Zeng, Yang, Dai, Qin, Zhang, Kong (CR69) 2018; 12 CR68 Emmert-Streib, Yang, Feng, Tripathi, Dehmer (CR59) 2020 Altaheri (CR112) 2021 Saeed, Anwar, Khalid, Majid, Bagci (CR55) 2020; 20 Lee (CR5) 2022 Berntson, Cacioppo (CR28) 2004; 41 Hasan, Kim (CR81) 2019; 9 Koolhaas (CR4) 2011; 35 CR60 Al-Shargie, Tang, Badruddin, Kiguchi (CR38) 2016 Darzi, Azami, Khosrowabadi (CR56) 2019; 1 Aljerf, AlMasri (CR23) 2018; 2 Karyotaki (CR1) 2020; 11 Kiourt, Pavlidis, Markantonatou (CR98) 2020 Jiang, Bian, Tian (CR65) 2019; 19 Hu (CR40) 2015; 14 Aljerf, AlMasri (CR6) 2018; 130 Ramos-Aguilar, Olvera-López, Olmos-Pineda (CR89) 2017; 145 Ring, Burns, Carroll (CR29) 2002; 39 Alzubaidi (CR93) 2021; 8 Martínez-Rodrigo, García-Martínez, Huerta, Alcaraz (CR73) 2021; 21 CR79 Boucsein, Fowles, Grimnes, Ben-Shakhar, Roth, Dawson, Filion (CR32) 2012; 49 Buddeberg-Fischer, Stamm, Buddeberg, Klaghofer (CR20) 2010; 83 CR76 Gaurav, Kumar (CR41) 2018; 8 CR113 CR71 CR117 Das Chakladar, Dey, Roy, Dogra (CR75) 2020; 60 Andersson, Finset (CR30) 1998; 44 CR3 CR8 Alickovic, Kevric, Subasi (CR83) 2018; 39 CR88 CR87 Kanoga, Mitsukura (CR64) 2017; 69 CR84 CR122 CR120 Umar Saeed, Anwar, Majid, Awais, Alnowami (CR46) 2018; 2018 Craik, He, Contreras-Vidal (CR58) 2019; 16 Gibson, Checkley, Papadopoulos, Poon, Daley, Wardle (CR34) 1999; 61 Jebelli, Khalili, Lee (CR67) 2019 Shrestha, Mahmood (CR103) 2019; 7 Sulaiman, Taib, Lias, Murat, Aris, Hamid (CR52) 2011; 12 Hanrahan, McCarthy, Kleiber, Lutgendorf, Tsalikian (CR33) 2006; 19 Lampert (CR14) 2016; 18 Ogrodniczuk, Kealy, Laverdière (CR2) 2021; 21 Al-Shargie, Kiguchi, Badruddin, Dass, Hani, Tang (CR36) 2016; 7 CR99 Shriram, Sundhararajan, Daimiwal (CR63) 2013; 7 CR97 Basnet, Jaiswal, Adhikari, Shyangwa (CR16) 2012; 10 CR96 Rahman, Watanobe, Nakamura (CR108) 2020; 2020 Pickering (CR13) 2001; 3 CR92 CR91 CR90 Pedrotti (CR24) 2014; 30 Hwang, You, Vaessen, Myin-Germeys, Park, Zhang (CR109) 2018; 24 Giannakakis, Grigoriadis, Giannakaki, Simantiraki, Roniotis, Tsiknakis (CR7) 2019 Khosrowabadi (CR10) 2018; 9 Mumtaz, Rasheed, Irfan (CR66) 2021; 68 Masood, Alghamdi (CR94) 2019; 7 Arsalan, Majid (CR9) 2021; 133 Sinha (CR44) 2005; 183 Alhagry, Fahmy, El-Khoribi (CR114) 2017; 8 Gossett (CR45) 2018; 125 Peng (CR85) 2013; 17 CR27 CR26 CR22 Rajendran, Jayalalitha, Adalarasu, Usha (CR53) 2022; 107 Khan (CR74) 2021; 2021 Yin, Zheng, Hu, Cui (CR119) 2021; 100 Herborn (CR25) 2015; 152 Bansevicius, Westgaard, Jensen (CR15) 1997; 37 CR106 CR107 C Ring (9809_CR29) 2002; 39 B Basnet (9809_CR16) 2012; 10 KA Herborn (9809_CR25) 2015; 152 D Shon (9809_CR78) 2018; 15 S Alhagry (9809_CR114) 2017; 8 9809_CR120 H Jebelli (9809_CR67) 2019 EL Gibson (9809_CR34) 1999; 61 9809_CR96 9809_CR122 R Lampert (9809_CR14) 2016; 18 9809_CR97 XH Le (9809_CR105) 2019; 11 9809_CR99 GG Berntson (9809_CR28) 2004; 41 B Penchina (9809_CR70) 2020 9809_CR26 9809_CR27 X Jiang (9809_CR65) 2019; 19 9809_CR22 SM Umar Saeed (9809_CR46) 2018; 2018 A Arsalan (9809_CR9) 2021; 133 D Kamińska (9809_CR77) 2021; 10 R Katmah (9809_CR31) 2021; 21 B Hwang (9809_CR109) 2018; 24 F Al-Shargie (9809_CR37) 2017; 8 JM Koolhaas (9809_CR4) 2011; 35 R Sinha (9809_CR44) 2005; 183 S Fischer (9809_CR17) 2016 MZ Islam (9809_CR95) 2020; 20 9809_CR79 R Ramos-Aguilar (9809_CR89) 2017; 145 9809_CR71 K Hanrahan (9809_CR33) 2006; 19 EW Gossett (9809_CR45) 2018; 125 9809_CR76 9809_CR106 9809_CR107 D Das Chakladar (9809_CR75) 2020; 60 E Karyotaki (9809_CR1) 2020; 11 RS Lee (9809_CR5) 2022 D Kotlęga (9809_CR12) 2016; 50 L Aljerf (9809_CR23) 2018; 2 AR Gaurav (9809_CR41) 2018; 8 M Esler (9809_CR11) 2008; 35 KS Park (9809_CR86) 2011; 3 SL Ong (9809_CR21) 2019; 37 W Boucsein (9809_CR32) 2012; 49 M Pedrotti (9809_CR24) 2014; 30 F Al-shargie (9809_CR49) 2018; 56 9809_CR84 L Aljerf (9809_CR6) 2018; 130 9809_CR87 R Fu (9809_CR110) 2022; 30 9809_CR113 9809_CR88 9809_CR117 S Siuly (9809_CR61) 2016 9809_CR90 L Malviya (9809_CR116) 2022 A Martínez-Rodrigo (9809_CR73) 2021; 21 9809_CR91 9809_CR92 S Kanoga (9809_CR64) 2017; 69 N Phutela (9809_CR115) 2022 X Yin (9809_CR119) 2021; 100 R Khosrowabadi (9809_CR10) 2018; 9 F Al-Shargie (9809_CR36) 2016; 7 A Craik (9809_CR58) 2019; 16 JC Pruessner (9809_CR42) 2004; 24 M Rahman (9809_CR108) 2020; 2020 9809_CR50 F Emmert-Streib (9809_CR59) 2020 9809_CR54 R Shriram (9809_CR63) 2013; 7 W Mumtaz (9809_CR66) 2021; 68 S Andersson (9809_CR30) 1998; 44 V Rajendran (9809_CR53) 2022; 107 FM Al-Shargie (9809_CR38) 2016 Z Wang (9809_CR118) 2019; 7 B Hu (9809_CR40) 2015; 14 C Kiourt (9809_CR98) 2020 9809_CR68 J Alonso (9809_CR80) 2015; 36 9809_CR60 F Duffy (9809_CR62) 2017 SMU Saeed (9809_CR55) 2020; 20 W Bao (9809_CR102) 2017 DSB Abdul Hamid (9809_CR104) 2021 JS Ogrodniczuk (9809_CR2) 2021; 21 E Alickovic (9809_CR83) 2018; 39 R Castaldo (9809_CR18) 2015; 18 GP Geslani (9809_CR19) 2013; 41 T Khan (9809_CR74) 2021; 2021 9809_CR35 A Abhishek (9809_CR111) 2022; 3 9809_CR39 S Bhatnagar (9809_CR57) 2023; 7 MJ Hasan (9809_CR81) 2019; 9 FA Alturki (9809_CR82) 2020; 20 G Giannakakis (9809_CR7) 2019 A Sundaresan (9809_CR72) 2021; 8 H Peng (9809_CR85) 2013; 17 A Shrestha (9809_CR103) 2019; 7 A Arsalan (9809_CR51) 2019; 23 A Darzi (9809_CR56) 2019; 1 9809_CR47 B Buddeberg-Fischer (9809_CR20) 2010; 83 9809_CR48 J Lataster (9809_CR43) 2011; 58 J Gu (9809_CR100) 2018; 77 RMA Ikram (9809_CR121) 2023; 11 9809_CR8 L Alzubaidi (9809_CR93) 2021; 8 H Altaheri (9809_CR112) 2021 N Sulaiman (9809_CR52) 2011; 12 S Sengupta (9809_CR101) 2020; 194 D Bansevicius (9809_CR15) 1997; 37 9809_CR3 TG Pickering (9809_CR13) 2001; 3 K Masood (9809_CR94) 2019; 7 H Zeng (9809_CR69) 2018; 12
References_xml	– ident: CR22 – volume: 17 start-page: 1341 issue: 7 year: 2013 end-page: 1347 ident: CR85 article-title: A method of identifying chronic stress by EEG publication-title: Pers Ubiquit Comput – volume: 183 start-page: 171 issue: 2 year: 2005 end-page: 180 ident: CR44 article-title: Neural activity associated with stress-induced cocaine craving: a functional magnetic resonance imaging study publication-title: Psychopharmacology – volume: 19 start-page: 987 issue: 5 year: 2019 ident: CR65 article-title: Removal of Artifacts from EEG Signals: A Review publication-title: Sensors – ident: CR97 – ident: CR68 – volume: 8 start-page: 1 issue: 1 year: 2021 end-page: 74 ident: CR93 article-title: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions publication-title: J Big Data – ident: CR39 – start-page: 15 year: 2016 end-page: 19 ident: CR38 article-title: Mental stress quantification using EEG signals publication-title: International conference for innovation in biomedical engineering and life sciences – volume: 83 start-page: 373 issue: 4 year: 2010 end-page: 379 ident: CR20 article-title: Chronic stress experience in young physicians: impact of person-and workplace-related factors publication-title: Int Arch Occup Environ Health – volume: 68 start-page: 102741 year: 2021 ident: CR66 article-title: Review of challenges associated with the EEG artifact removal methods publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2021.102741 – ident: CR54 – start-page: 173 year: 2019 end-page: 180 ident: CR67 article-title: Mobile EEG-based workers’ stress recognition by applying deep neural network publication-title: Advances in informatics and computing in civil and construction engineering – volume: 60 start-page: 101989 year: 2020 ident: CR75 article-title: EEG-based mental workload estimation using deep BLSTM-LSTM network and evolutionary algorithm publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2020.101989 – ident: CR8 – volume: 7 start-page: 93711 year: 2019 end-page: 93722 ident: CR118 article-title: Phase-locking value based graph convolutional neural networks for emotion recognition publication-title: IEEE Access – ident: CR106 – volume: 100 start-page: 106954 year: 2021 ident: CR119 article-title: EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM publication-title: Appl Soft Comp – volume: 36 start-page: 1351 issue: 7 year: 2015 ident: CR80 article-title: Stress assessment based on EEG univariate features and functional connectivity measures publication-title: Physiol Meas – ident: CR71 – volume: 39 start-page: 585 issue: 5 year: 2002 end-page: 590 ident: CR29 article-title: Shifting hemodynamics of blood pressure control during prolonged mental stress publication-title: Psychophysiology – volume: 19 start-page: 95 issue: 2 year: 2006 end-page: 101 ident: CR33 article-title: Strategies for salivary cortisol collection and analysis in research with children publication-title: Appl Nurs Res doi: 10.1016/j.apnr.2006.02.001 – volume: 24 start-page: 2825 issue: 11 year: 2004 end-page: 2831 ident: CR42 article-title: Dopamine release in response to a psychological stress in humans and its relationship to early life maternal care: a positron emission tomography study using [11C] raclopride publication-title: J Neurosci – ident: CR92 – ident: CR88 – volume: 145 start-page: 151 year: 2017 end-page: 162 ident: CR89 article-title: Analysis of EEG signal processing techniques based on spectrograms publication-title: Res Comput Sci – volume: 18 start-page: 370 year: 2015 end-page: 377 ident: CR18 article-title: Acute mental stress assessment via short term HRV analysis in healthy adults: a systematic review with meta-analysis publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2015.02.012 – ident: CR60 – year: 2022 ident: CR116 article-title: A novel technique for stress detection from EEG signal using hybrid deep learning model publication-title: Neural Comput Appl doi: 10.1007/s00521-022-07540-7 – volume: 41 start-page: 57 issue: 2 year: 2004 end-page: 64 ident: CR28 article-title: Heart rate variability: stress and psychiatric conditions publication-title: Dyn Electrocardiogr – start-page: 227 year: 2020 end-page: 238 ident: CR70 article-title: Deep LSTM recurrent neural network for anxiety classification from EEG in adolescents with autism publication-title: Brain Informatics: 13th international conference, BI 2020, Padua, Italy, September 19, 2020 proceedings – volume: 14 start-page: 553 issue: 5 year: 2015 end-page: 561 ident: CR40 article-title: Signal quality assessment model for wearable EEG sensor on prediction of mental stress publication-title: IEEE Trans Nanobiosci – ident: CR91 – ident: CR47 – volume: 58 start-page: 1081 issue: 4 year: 2011 end-page: 1089 ident: CR43 article-title: Psychosocial stress is associated with in vivo dopamine release in human ventromedial prefrontal cortex: a positron emission tomography study using [18F] fallypride publication-title: Neuroimage – year: 2020 ident: CR59 article-title: An introductory review of deep learning for prediction models with big data publication-title: Front Artif Intell doi: 10.3389/frai.2020.00004 – ident: CR117 – volume: 30 start-page: 1384 year: 2022 end-page: 1400 ident: CR110 article-title: Symmetric convolutional and adversarial neural network enables improved mental stress classification from EEG publication-title: IEEE Trans Neural Syst Rehabil Eng – volume: 2 start-page: 502 issue: 2 year: 2018 end-page: 509 ident: CR23 article-title: Syrian case study: Behçet’s disease clinical symptomatologies, ocular manifestations, and treatment publication-title: Chron Pharm Sci – volume: 69 start-page: 69 year: 2017 end-page: 89 ident: CR64 article-title: Review of artifact rejection methods for electroencephalographic systems publication-title: Electroencephalography – volume: 2021 start-page: 4620487 year: 2021 ident: CR74 article-title: EEG based aptitude detection system for stress regulation in health care workers publication-title: Sci Program doi: 10.1155/2021/4620487 – volume: 37 start-page: 193 issue: 2 year: 2019 end-page: 205 ident: CR21 article-title: Stress and anxiety among mothers of premature infants in a Malaysian neonatal intensive care unit publication-title: J Reprod Infant Psychol – volume: 20 start-page: 2505 issue: 9 year: 2020 ident: CR82 article-title: EEG signal analysis for diagnosing neurological disorders using discrete wavelet transform and intelligent techniques publication-title: Sensors – volume: 56 start-page: 125 issue: 1 year: 2018 end-page: 136 ident: CR49 article-title: Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach publication-title: Med Biol Eng Compu doi: 10.1007/s11517-017-1733-8 – ident: CR27 – volume: 20 start-page: 1886 issue: 7 year: 2020 ident: CR55 article-title: EEG based classification of long-term stress using psychological labeling publication-title: Sensors – volume: 30 start-page: 220 issue: 3 year: 2014 end-page: 236 ident: CR24 article-title: Automatic stress classification with pupil diameter analysis publication-title: Int J Human-Comp Interact – volume: 7 start-page: 68446 year: 2019 end-page: 68454 ident: CR94 article-title: Modeling mental stress using a deep learning framework publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2917718 – volume: 125 start-page: 35 year: 2018 end-page: 41 ident: CR45 article-title: Anticipatory stress associated with functional magnetic resonance imaging: Implications for psychosocial stress research publication-title: Int J Psychophysiol – volume: 10 start-page: 2840 issue: 22 year: 2021 ident: CR77 article-title: Detection of mental stress through EEG signal in virtual reality environment publication-title: Electronics – ident: CR3 – volume: 18 start-page: 1 issue: 12 year: 2016 end-page: 7 ident: CR14 article-title: Mental stress and ventricular arrhythmias publication-title: Curr Cardiol Rep – volume: 3 start-page: 381 issue: 14 year: 2011 end-page: 389 ident: CR86 article-title: Patterns of electroencephalography (EEG) change against stress through noise and memorization test publication-title: Int J Med Med Sci – start-page: 83 year: 2020 end-page: 108 ident: CR98 article-title: Deep learning approaches in food recognition publication-title: Machine learning paradigms: advances in deep learning-based technological applications – volume: 35 start-page: 498 issue: 4 year: 2008 end-page: 502 ident: CR11 article-title: Chronic mental stress is a cause of essential hypertension: presence of biological markers of stress publication-title: Clin Exp Pharmacol Physiol – volume: 9 start-page: 376 issue: 12 year: 2019 ident: CR81 article-title: A hybrid feature pool-based emotional stress state detection algorithm using EEG signals publication-title: Brain Sci – ident: CR120 – volume: 3 start-page: 524 issue: 3 year: 2022 end-page: 531 ident: CR111 article-title: Classification of mental stress on a sports person using EEG publication-title: Int J Innov Res Eng – volume: 8 start-page: 13 issue: 1 year: 2021 ident: CR72 article-title: Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI publication-title: Brain Inf doi: 10.1186/s40708-021-00133-5 – volume: 194 start-page: 105596 year: 2020 ident: CR101 article-title: A review of deep learning with special emphasis on architectures, applications and recent trends publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2020.105596 – volume: 37 start-page: 499 issue: 8 year: 1997 end-page: 510 ident: CR15 article-title: Mental stress of long duration: EMG activity, perceived tension, fatigue, and pain development in pain-free subjects publication-title: Headache: J Head Face Pain – volume: 152 start-page: 225 year: 2015 end-page: 230 ident: CR25 article-title: Skin temperature reveals the intensity of acute stress publication-title: Physiol Behav – volume: 10 start-page: 56 issue: 3 year: 2012 end-page: 59 ident: CR16 article-title: Depression among undergraduate medical students publication-title: Kathmandu Univ Med J – volume: 39 start-page: 94 year: 2018 end-page: 102 ident: CR83 article-title: Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2017.07.022 – ident: CR87 – volume: 9 start-page: 107 issue: 2 year: 2018 end-page: 120 ident: CR10 article-title: Stress and perception of emotional stimuli: long-term stress rewiring the brain publication-title: Basic Clin Neurosci doi: 10.29252/NIRP.BCN.9.2.107 – volume: 23 start-page: 2257 issue: 6 year: 2019 end-page: 2264 ident: CR51 article-title: Classification of perceived mental stress using a commercially available EEG headband publication-title: IEEE J Biomed Health Inform – year: 2019 ident: CR7 article-title: Review on psychological stress detection using biosignals publication-title: IEEE Trans Affective Comp doi: 10.1109/TAFFC.2019.2927337 – volume: 130 start-page: 9 issue: 1 year: 2018 ident: CR6 article-title: Beyond pain, fear, withdrawal-findings, and problems involving change-treatment and application for a chronic addiction on alcohol do not end 2 (1) publication-title: DDIPIJ MS ID – start-page: 1 year: 2022 end-page: 18 ident: CR5 article-title: The physiology of stress and the human body’s response to stress publication-title: Epigenetics of stress and stress disorders – ident: CR35 – year: 2016 ident: CR61 publication-title: EEG signal analysis and classification: techniques and applications (Health Information Science) – ident: CR84 – year: 2022 ident: CR115 article-title: Stress classification using brain signals based on LSTM network publication-title: Comp Intell Neurosci doi: 10.1155/2022/7607592 – volume: 21 start-page: 837 issue: 4 year: 2021 end-page: 845 ident: CR2 article-title: Who is coming through the door? A national survey of self-reported problems among post-secondary school students who have attended campus mental health services in Canada publication-title: Couns Psychother Res – volume: 3 start-page: 249 issue: 3 year: 2001 end-page: 254 ident: CR13 article-title: Mental stress as a causal factor in the development of hypertension and cardiovascular disease publication-title: Curr Hypertens Rep – year: 2021 ident: CR112 article-title: Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review publication-title: Neural Comp Appl doi: 10.1007/s00521-021-06352-5 – ident: CR96 – ident: CR50 – volume: 50 start-page: 265 issue: 4 year: 2016 end-page: 270 ident: CR12 article-title: The emotional stress and risk of ischemic stroke publication-title: Neurol Neurochir Pol – volume: 1 start-page: 16 issue: 1 year: 2019 end-page: 41 ident: CR56 article-title: Brain functional connectivity changes in long-term mental stress publication-title: J Neurodev Cognit – volume: 49 start-page: 1017 issue: 8 year: 2012 end-page: 1034 ident: CR32 article-title: Publication recommendations for electrodermal measurements publication-title: Psychophysiology – volume: 20 start-page: 100412 year: 2020 ident: CR95 article-title: A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images publication-title: Inf Med Unlocked doi: 10.1016/j.imu.2020.100412 – volume: 77 start-page: 354 year: 2018 end-page: 377 ident: CR100 article-title: Recent advances in convolutional neural networks publication-title: Pattern Recogn doi: 10.1016/j.patcog.2017.10.013 – volume: 21 start-page: 3050 issue: 9 year: 2021 ident: CR73 article-title: Detection of negative stress through spectral features of electroencephalographic recordings and a convolutional neural network publication-title: Sensors – volume: 16 start-page: 031001 issue: 3 year: 2019 ident: CR58 article-title: Deep learning for electroencephalogram (EEG) classification tasks: a review publication-title: J Neural Eng – volume: 7 start-page: 100211 year: 2023 ident: CR57 article-title: A deep learning approach for assessing stress levels in patients using electroencephalogram signals publication-title: Decis Anal J – ident: CR26 – ident: CR99 – ident: CR122 – year: 2021 ident: CR104 article-title: Integration of deep learning for improved diagnosis of depression using EEG and facial features publication-title: Mater Today: Proc doi: 10.1016/j.matpr.2021.05.659 – volume: 35 start-page: 1291 issue: 5 year: 2011 end-page: 1301 ident: CR4 article-title: Stress revisited: a critical evaluation of the stress concept publication-title: Neurosci Biobehav Rev – year: 2017 ident: CR62 article-title: A unique pattern of cortical connectivity characterizes patients with attention deficit disorders: a large electroencephalographic coherence study publication-title: BMC Med doi: 10.1186/s12916-017-0805-9 – volume: 7 start-page: 3882 issue: 10 year: 2016 end-page: 3898 ident: CR36 article-title: Mental stress assessment using simultaneous measurement of EEG and fNIRS publication-title: Biomed Opt Express doi: 10.1364/BOE.7.003882 – volume: 107 start-page: 1845 issue: 1 year: 2022 ident: CR53 article-title: A Review on Mental Stress Detection Using PSS Method and EEG Signal Method publication-title: ECS Trans – volume: 21 start-page: 5043 issue: 15 year: 2021 ident: CR31 article-title: A review on mental stress assessment methods using EEG signals publication-title: Sensors – volume: 61 start-page: 214 issue: 2 year: 1999 end-page: 224 ident: CR34 article-title: Increased salivary cortisol reliably induced by a protein-rich midday meal publication-title: Psychosom Med – volume: 8 start-page: 2583 issue: 5 year: 2017 end-page: 2598 ident: CR37 article-title: Assessment of mental stress effects on prefrontal cortical activities using canonical correlation analysis: an fNIRS-EEG study publication-title: Biomed Opt Express doi: 10.1364/BOE.8.002583 – year: 2016 ident: CR17 article-title: Negative stress beliefs predict somatic symptoms in students under academic stress publication-title: Int J Behav Med doi: 10.1007/s12529-016-9562-y – volume: 11 start-page: 259 issue: 2 year: 2023 ident: CR121 article-title: Water temperature prediction using improved deep learning methods through reptile search algorithm and weighted mean of vectors optimizer publication-title: J Mar Sci Eng – volume: 11 start-page: 1387 year: 2019 ident: CR105 article-title: Application of long short-term memory (LSTM) neural network for flood forecasting publication-title: Water doi: 10.3390/w11071387 – ident: CR113 – volume: 11 start-page: 1759 year: 2020 ident: CR1 article-title: Sources of stress and their associations with mental disorders among college students: results of the world health organization world mental health surveys international college student initiative publication-title: Front Psychol – ident: CR79 – volume: 2020 start-page: 1 year: 2020 end-page: 18 ident: CR108 article-title: A neural network based intelligent support model for program code completion publication-title: Sci Program doi: 10.1155/2020/7426461 – volume: 41 start-page: 1457 issue: 9 year: 2013 end-page: 1468 ident: CR19 article-title: Perceived stress, stressors, and mental distress among doctor of pharmacy students publication-title: Soc Behav Personal Int J – volume: 12 start-page: 597 issue: 6 year: 2018 end-page: 606 ident: CR69 article-title: EEG classification of driver mental states by deep learning publication-title: Cogn Neurodyn doi: 10.1007/s11571-018-9496-y – volume: 7 start-page: 34 year: 2013 end-page: 38 ident: CR63 article-title: EEG based cognitive workload assessment for maximum efficiency publication-title: Int Organ Sci Res IOSR – volume: 7 start-page: 53040 year: 2019 end-page: 53065 ident: CR103 article-title: Review of deep learning algorithms and architectures publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2912200 – ident: CR48 – ident: CR90 – volume: 44 start-page: 645 issue: 6 year: 1998 end-page: 656 ident: CR30 article-title: Heart rate and skin conductance reactivity to brief psychological stress in brain-injured patients publication-title: J Psychosom Res – volume: 133 start-page: 104377 year: 2021 ident: CR9 article-title: Human stress classification during public speaking using physiological signals publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2021.104377 – year: 2017 ident: CR102 article-title: A deep learning framework for financial time series using stacked autoencoders and long-short term memory publication-title: PLoS ONE doi: 10.1371/journal.pone.0180944 – volume: 24 start-page: 753 issue: 10 year: 2018 end-page: 772 ident: CR109 article-title: Deep ECGNet: An optimal deep learning framework for monitoring mental stress using ultra short-term ECG signals publication-title: Telemed and e-HEALTH – volume: 8 start-page: 25 issue: 1 year: 2018 end-page: 34 ident: CR41 article-title: EEG-metric based mental stress detection publication-title: Netw Biol – volume: 2018 start-page: 1049257 year: 2018 ident: CR46 article-title: Selection of neural oscillatory features for human stress classification with single channel EEG headset publication-title: BioMed Res Int doi: 10.1155/2018/1049257 – volume: 15 start-page: 2461 issue: 11 year: 2018 ident: CR78 article-title: Emotional stress state detection using genetic algorithm-based feature selection on EEG signals publication-title: Int J Environ Res Public Health – ident: CR76 – volume: 8 start-page: 355 issue: 10 year: 2017 end-page: 358 ident: CR114 article-title: Emotion recognition based on EEG using LSTM recurrent neural network publication-title: Emotion – ident: CR107 – volume: 12 start-page: 27 issue: 1 year: 2011 end-page: 33 ident: CR52 article-title: Novel methods for stress features identification using EEG signals publication-title: Int J Simul Syst Sci Technol – volume: 50 start-page: 265 issue: 4 year: 2016 ident: 9809_CR12 publication-title: Neurol Neurochir Pol doi: 10.1016/j.pjnns.2016.03.006 – volume: 125 start-page: 35 year: 2018 ident: 9809_CR45 publication-title: Int J Psychophysiol doi: 10.1016/j.ijpsycho.2018.02.005 – ident: 9809_CR79 doi: 10.1109/IJCNN.2011.6033297 – volume: 20 start-page: 1886 issue: 7 year: 2020 ident: 9809_CR55 publication-title: Sensors doi: 10.3390/s20071886 – ident: 9809_CR71 doi: 10.1109/ICASSP.2018.8462243 – volume: 56 start-page: 125 issue: 1 year: 2018 ident: 9809_CR49 publication-title: Med Biol Eng Compu doi: 10.1007/s11517-017-1733-8 – volume: 39 start-page: 94 year: 2018 ident: 9809_CR83 publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2017.07.022 – volume: 69 start-page: 69 year: 2017 ident: 9809_CR64 publication-title: Electroencephalography – start-page: 15 volume-title: International conference for innovation in biomedical engineering and life sciences year: 2016 ident: 9809_CR38 doi: 10.1007/978-981-10-0266-3_4 – ident: 9809_CR113 doi: 10.1007/978-981-16-6887-6_3 – volume: 24 start-page: 2825 issue: 11 year: 2004 ident: 9809_CR42 publication-title: J Neurosci doi: 10.1523/JNEUROSCI.3422-03.2004 – volume: 18 start-page: 370 year: 2015 ident: 9809_CR18 publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2015.02.012 – volume: 107 start-page: 1845 issue: 1 year: 2022 ident: 9809_CR53 publication-title: ECS Trans doi: 10.1149/10701.16459ecst – volume: 41 start-page: 1457 issue: 9 year: 2013 ident: 9809_CR19 publication-title: Soc Behav Personal Int J doi: 10.2224/sbp.2013.41.9.1457 – volume: 7 start-page: 34 year: 2013 ident: 9809_CR63 publication-title: Int Organ Sci Res IOSR – ident: 9809_CR120 – volume: 35 start-page: 1291 issue: 5 year: 2011 ident: 9809_CR4 publication-title: Neurosci Biobehav Rev doi: 10.1016/j.neubiorev.2011.02.003 – volume: 23 start-page: 2257 issue: 6 year: 2019 ident: 9809_CR51 publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2019.2926407 – year: 2022 ident: 9809_CR116 publication-title: Neural Comput Appl doi: 10.1007/s00521-022-07540-7 – ident: 9809_CR26 doi: 10.1109/ICACCI.2018.8554715 – volume: 77 start-page: 354 year: 2018 ident: 9809_CR100 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2017.10.013 – volume: 8 start-page: 355 issue: 10 year: 2017 ident: 9809_CR114 publication-title: Emotion – volume: 183 start-page: 171 issue: 2 year: 2005 ident: 9809_CR44 publication-title: Psychopharmacology doi: 10.1007/s00213-005-0147-8 – ident: 9809_CR96 – volume: 7 start-page: 53040 year: 2019 ident: 9809_CR103 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2912200 – volume: 1 start-page: 16 issue: 1 year: 2019 ident: 9809_CR56 publication-title: J Neurodev Cognit doi: 10.29252/jncog.1.1.16 – volume: 11 start-page: 1387 year: 2019 ident: 9809_CR105 publication-title: Water doi: 10.3390/w11071387 – ident: 9809_CR60 doi: 10.1109/SSD54932.2022.9955724 – volume: 19 start-page: 987 issue: 5 year: 2019 ident: 9809_CR65 publication-title: Sensors doi: 10.3390/s19050987 – ident: 9809_CR91 doi: 10.31224/osf.io/kaqew – volume: 14 start-page: 553 issue: 5 year: 2015 ident: 9809_CR40 publication-title: IEEE Trans Nanobiosci doi: 10.1109/TNB.2015.2420576 – ident: 9809_CR84 doi: 10.1109/ICCSCE.2011.6190573 – start-page: 83 volume-title: Machine learning paradigms: advances in deep learning-based technological applications year: 2020 ident: 9809_CR98 doi: 10.1007/978-3-030-49724-8_4 – ident: 9809_CR68 doi: 10.23919/ICACT.2018.8323716 – ident: 9809_CR76 doi: 10.23919/ICACT.2019.8702048 – volume: 12 start-page: 597 issue: 6 year: 2018 ident: 9809_CR69 publication-title: Cogn Neurodyn doi: 10.1007/s11571-018-9496-y – volume: 36 start-page: 1351 issue: 7 year: 2015 ident: 9809_CR80 publication-title: Physiol Meas doi: 10.1088/0967-3334/36/7/1351 – volume: 7 start-page: 100211 year: 2023 ident: 9809_CR57 publication-title: Decis Anal J doi: 10.1016/j.dajour.2023.100211 – volume: 20 start-page: 2505 issue: 9 year: 2020 ident: 9809_CR82 publication-title: Sensors doi: 10.3390/s20092505 – ident: 9809_CR35 doi: 10.1109/EMBC.2016.7591884 – volume: 8 start-page: 2583 issue: 5 year: 2017 ident: 9809_CR37 publication-title: Biomed Opt Express doi: 10.1364/BOE.8.002583 – volume: 7 start-page: 68446 year: 2019 ident: 9809_CR94 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2917718 – ident: 9809_CR99 – volume: 7 start-page: 93711 year: 2019 ident: 9809_CR118 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2927768 – volume: 35 start-page: 498 issue: 4 year: 2008 ident: 9809_CR11 publication-title: Clin Exp Pharmacol Physiol doi: 10.1111/j.1440-1681.2008.04904.x – volume: 3 start-page: 524 issue: 3 year: 2022 ident: 9809_CR111 publication-title: Int J Innov Res Eng – year: 2016 ident: 9809_CR17 publication-title: Int J Behav Med doi: 10.1007/s12529-016-9562-y – volume: 41 start-page: 57 issue: 2 year: 2004 ident: 9809_CR28 publication-title: Dyn Electrocardiogr doi: 10.1002/9780470987483.ch7 – volume: 10 start-page: 2840 issue: 22 year: 2021 ident: 9809_CR77 publication-title: Electronics doi: 10.3390/electronics10222840 – volume: 8 start-page: 1 issue: 1 year: 2021 ident: 9809_CR93 publication-title: J Big Data doi: 10.1186/s40537-021-00444-8 – volume: 7 start-page: 3882 issue: 10 year: 2016 ident: 9809_CR36 publication-title: Biomed Opt Express doi: 10.1364/BOE.7.003882 – volume: 2018 start-page: 1049257 year: 2018 ident: 9809_CR46 publication-title: BioMed Res Int doi: 10.1155/2018/1049257 – ident: 9809_CR90 doi: 10.1109/ICSGRC.2012.6287173 – ident: 9809_CR8 doi: 10.1109/ICACCS.2013.6938735 – volume: 30 start-page: 1384 year: 2022 ident: 9809_CR110 publication-title: IEEE Trans Neural Syst Rehabil Eng doi: 10.1109/TNSRE.2022.3174821 – volume: 60 start-page: 101989 year: 2020 ident: 9809_CR75 publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2020.101989 – ident: 9809_CR48 doi: 10.1109/CSNT51715.2021.9509713 – volume: 19 start-page: 95 issue: 2 year: 2006 ident: 9809_CR33 publication-title: Appl Nurs Res doi: 10.1016/j.apnr.2006.02.001 – volume: 3 start-page: 249 issue: 3 year: 2001 ident: 9809_CR13 publication-title: Curr Hypertens Rep doi: 10.1007/s11906-001-0047-1 – volume: 21 start-page: 3050 issue: 9 year: 2021 ident: 9809_CR73 publication-title: Sensors doi: 10.3390/s21093050 – volume: 21 start-page: 5043 issue: 15 year: 2021 ident: 9809_CR31 publication-title: Sensors doi: 10.3390/s21155043 – volume: 9 start-page: 376 issue: 12 year: 2019 ident: 9809_CR81 publication-title: Brain Sci doi: 10.3390/brainsci9120376 – ident: 9809_CR97 – volume: 68 start-page: 102741 year: 2021 ident: 9809_CR66 publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2021.102741 – volume: 145 start-page: 151 year: 2017 ident: 9809_CR89 publication-title: Res Comput Sci doi: 10.13053/rcs-145-1-12 – volume: 37 start-page: 499 issue: 8 year: 1997 ident: 9809_CR15 publication-title: Headache: J Head Face Pain doi: 10.1046/j.1526-4610.1997.3708499.x – start-page: 227 volume-title: Brain Informatics: 13th international conference, BI 2020, Padua, Italy, September 19, 2020 proceedings year: 2020 ident: 9809_CR70 doi: 10.1007/978-3-030-59277-6_21 – year: 2022 ident: 9809_CR115 publication-title: Comp Intell Neurosci doi: 10.1155/2022/7607592 – year: 2021 ident: 9809_CR112 publication-title: Neural Comp Appl doi: 10.1007/s00521-021-06352-5 – year: 2017 ident: 9809_CR62 publication-title: BMC Med doi: 10.1186/s12916-017-0805-9 – volume: 39 start-page: 585 issue: 5 year: 2002 ident: 9809_CR29 publication-title: Psychophysiology doi: 10.1111/1469-8986.3950585 – ident: 9809_CR54 doi: 10.1109/ICBDACI.2017.8070809 – volume: 15 start-page: 2461 issue: 11 year: 2018 ident: 9809_CR78 publication-title: Int J Environ Res Public Health doi: 10.3390/ijerph15112461 – volume: 2020 start-page: 1 year: 2020 ident: 9809_CR108 publication-title: Sci Program doi: 10.1155/2020/7426461 – volume: 11 start-page: 1759 year: 2020 ident: 9809_CR1 publication-title: Front Psychol doi: 10.3389/fpsyg.2020.01759 – volume: 2 start-page: 502 issue: 2 year: 2018 ident: 9809_CR23 publication-title: Chron Pharm Sci – volume: 8 start-page: 13 issue: 1 year: 2021 ident: 9809_CR72 publication-title: Brain Inf doi: 10.1186/s40708-021-00133-5 – ident: 9809_CR122 doi: 10.1007/978-981-19-6004-8_52 – volume: 16 start-page: 031001 issue: 3 year: 2019 ident: 9809_CR58 publication-title: J Neural Eng doi: 10.1088/1741-2552/ab0ab5 – volume: 21 start-page: 837 issue: 4 year: 2021 ident: 9809_CR2 publication-title: Couns Psychother Res doi: 10.1002/capr.12439 – ident: 9809_CR92 – volume: 17 start-page: 1341 issue: 7 year: 2013 ident: 9809_CR85 publication-title: Pers Ubiquit Comput doi: 10.1007/s00779-012-0593-3 – volume: 9 start-page: 107 issue: 2 year: 2018 ident: 9809_CR10 publication-title: Basic Clin Neurosci doi: 10.29252/NIRP.BCN.9.2.107 – volume: 24 start-page: 753 issue: 10 year: 2018 ident: 9809_CR109 publication-title: Telemed and e-HEALTH doi: 10.1089/tmj.2017.0250 – start-page: 173 volume-title: Advances in informatics and computing in civil and construction engineering year: 2019 ident: 9809_CR67 doi: 10.1007/978-3-030-00220-6_21 – year: 2019 ident: 9809_CR7 publication-title: IEEE Trans Affective Comp doi: 10.1109/TAFFC.2019.2927337 – ident: 9809_CR47 doi: 10.1007/978-1-4614-4984-3 – volume: 61 start-page: 214 issue: 2 year: 1999 ident: 9809_CR34 publication-title: Psychosom Med doi: 10.1097/00006842-199903000-00014 – ident: 9809_CR106 doi: 10.1007/978-1-4842-6150-7 – start-page: 1 volume-title: Epigenetics of stress and stress disorders year: 2022 ident: 9809_CR5 – volume: 12 start-page: 27 issue: 1 year: 2011 ident: 9809_CR52 publication-title: Int J Simul Syst Sci Technol – volume: 130 start-page: 9 issue: 1 year: 2018 ident: 9809_CR6 publication-title: DDIPIJ MS ID – ident: 9809_CR27 doi: 10.1007/978-3-540-30120-2_57 – volume: 8 start-page: 25 issue: 1 year: 2018 ident: 9809_CR41 publication-title: Netw Biol – volume: 30 start-page: 220 issue: 3 year: 2014 ident: 9809_CR24 publication-title: Int J Human-Comp Interact doi: 10.1080/10447318.2013.848320 – ident: 9809_CR107 doi: 10.1109/ICMLA.2017.0-110 – ident: 9809_CR3 – volume: 133 start-page: 104377 year: 2021 ident: 9809_CR9 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2021.104377 – volume: 83 start-page: 373 issue: 4 year: 2010 ident: 9809_CR20 publication-title: Int Arch Occup Environ Health doi: 10.1007/s00420-009-0467-9 – volume: 49 start-page: 1017 issue: 8 year: 2012 ident: 9809_CR32 publication-title: Psychophysiology doi: 10.1111/j.1469-8986.2012.01384.x – ident: 9809_CR88 doi: 10.1109/SMC.2016.7844738 – year: 2020 ident: 9809_CR59 publication-title: Front Artif Intell doi: 10.3389/frai.2020.00004 – volume-title: EEG signal analysis and classification: techniques and applications (Health Information Science) year: 2016 ident: 9809_CR61 doi: 10.1007/978-3-319-47653-7 – ident: 9809_CR87 doi: 10.1109/ICSIPA.2015.7412205 – volume: 11 start-page: 259 issue: 2 year: 2023 ident: 9809_CR121 publication-title: J Mar Sci Eng doi: 10.3390/jmse11020259 – volume: 18 start-page: 1 issue: 12 year: 2016 ident: 9809_CR14 publication-title: Curr Cardiol Rep doi: 10.1007/s11886-016-0798-6 – volume: 10 start-page: 56 issue: 3 year: 2012 ident: 9809_CR16 publication-title: Kathmandu Univ Med J doi: 10.3126/kumj.v10i3.8021 – ident: 9809_CR50 doi: 10.1109/I2MTC.2013.6555658 – volume: 3 start-page: 381 issue: 14 year: 2011 ident: 9809_CR86 publication-title: Int J Med Med Sci – volume: 37 start-page: 193 issue: 2 year: 2019 ident: 9809_CR21 publication-title: J Reprod Infant Psychol doi: 10.1080/02646838.2018.1540861 – volume: 152 start-page: 225 year: 2015 ident: 9809_CR25 publication-title: Physiol Behav doi: 10.1016/j.physbeh.2015.09.032 – volume: 194 start-page: 105596 year: 2020 ident: 9809_CR101 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2020.105596 – ident: 9809_CR39 doi: 10.5772/9651 – volume: 20 start-page: 100412 year: 2020 ident: 9809_CR95 publication-title: Inf Med Unlocked doi: 10.1016/j.imu.2020.100412 – volume: 44 start-page: 645 issue: 6 year: 1998 ident: 9809_CR30 publication-title: J Psychosom Res doi: 10.1016/S0022-3999(97)00305-X – ident: 9809_CR117 doi: 10.1109/TNNLS.2022.3159573 – year: 2021 ident: 9809_CR104 publication-title: Mater Today: Proc doi: 10.1016/j.matpr.2021.05.659 – ident: 9809_CR22 – volume: 2021 start-page: 4620487 year: 2021 ident: 9809_CR74 publication-title: Sci Program doi: 10.1155/2021/4620487 – volume: 100 start-page: 106954 year: 2021 ident: 9809_CR119 publication-title: Appl Soft Comp doi: 10.1016/j.asoc.2020.106954 – volume: 58 start-page: 1081 issue: 4 year: 2011 ident: 9809_CR43 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2011.07.030 – year: 2017 ident: 9809_CR102 publication-title: PLoS ONE doi: 10.1371/journal.pone.0180944
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Snippet	Mental stress is a common problem that affects individuals all over the world. Stress reduces human functionality during routine work and may lead to severe...
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SubjectTerms	Algorithms Artificial Intelligence Artificial neural networks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Deep learning Electroencephalography Graph neural networks Graphical representations Image Processing and Computer Vision Machine learning Medical imaging Neural networks Probability and Statistics in Computer Science Psychological stress Review
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Title	A review on evaluating mental stress by deep learning using EEG signals
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