{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T14:25:51Z","timestamp":1774535151166,"version":"3.50.1"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030722531","type":"print"},{"value":"9783030722548","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-72254-8_12","type":"book-chapter","created":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T04:02:31Z","timestamp":1616990551000},"page":"119-128","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Measuring Stress Response via the EEG - A Review"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9562-7345","authenticated-orcid":false,"given":"Adam","family":"\u0141ysiak","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","unstructured":"Abhang, P.A., Gawali, B.W., Mehrotra, S.C.: Technological basics of EEG recording and operation of apparatus. In: Introduction to EEG- and Speech-Based Emotion Recognition, pp. 19\u201350. Elsevier\/AP, Academic Press is an imprint of Elsevier. https:\/\/doi.org\/10.1016\/B978-0-12-804490-2.00002-6","DOI":"10.1016\/B978-0-12-804490-2.00002-6"},{"key":"12_CR2","doi-asserted-by":"publisher","unstructured":"Al-Shargie, F., Kiguchi, M., Badruddin, N., Dass, S.C., Hani, A.F.M., Tang, T.B.: Mental stress assessment using simultaneous measurement of EEG and fNIRS. 7(10), 3882\u20133898. https:\/\/doi.org\/10.1364\/BOE.7.003882","DOI":"10.1364\/BOE.7.003882"},{"key":"12_CR3","doi-asserted-by":"publisher","unstructured":"Al-shargie, F., Tang, T.B., Badruddin, N., Dass, S.C., Kiguchi, M.: Mental stress assessment based on feature level fusion of fNIRS and EEG signals. In: 2016 6th International Conference on Intelligent and Advanced Systems (ICIAS), pp.\u00a01\u20135. IEEE. https:\/\/doi.org\/10.1109\/ICIAS.2016.7824060","DOI":"10.1109\/ICIAS.2016.7824060"},{"key":"12_CR4","doi-asserted-by":"publisher","unstructured":"Arsalan, A., Majid, M., Butt, A.R., Anwar, S.M.: Classification of perceived mental stress using a commercially available EEG headband. 23(6), 2257\u20132264. https:\/\/doi.org\/10.1109\/JBHI.2019.2926407","DOI":"10.1109\/JBHI.2019.2926407"},{"key":"12_CR5","doi-asserted-by":"publisher","unstructured":"Bandt, C., Pompe, B.: Permutation entropy: a natural complexity measure for time series. https:\/\/doi.org\/10.1103\/PHYSREVLETT.88.174102","DOI":"10.1103\/PHYSREVLETT.88.174102"},{"key":"12_CR6","doi-asserted-by":"publisher","unstructured":"Bernstein, E.E., McNally, R.J.: Exercise as a buffer against difficulties with emotion regulation: a pathway to emotional wellbeing. 109, 29\u201336. https:\/\/doi.org\/10.1016\/j.brat.2018.07.010","DOI":"10.1016\/j.brat.2018.07.010"},{"key":"12_CR7","unstructured":"Cohen, S., Kamarck, T., Mermelstein, R., et\u00a0al.: Perceived stress scale. 10, 1\u20132 (1994)"},{"key":"12_CR8","doi-asserted-by":"publisher","unstructured":"Dzedzickis, A., Kaklauskas, A., Bucinskas, V.: Human emotion recognition: review of sensors and methods. 20(3), \u00a0592. https:\/\/doi.org\/10.3390\/s20030592","DOI":"10.3390\/s20030592"},{"key":"12_CR9","doi-asserted-by":"publisher","unstructured":"Goyal, M., Singh, S., Sibinga, E.M.S., Gould, N.F., Rowland-Seymour, A., Sharma, R., Berger, Z., Sleicher, D., Maron, D.D., Shihab, H.M., Ranasinghe, P.D., Linn, S., Saha, S., Bass, E.B., Haythornthwaite, J.A.: Meditation programs for psychological stress and well-being: a systematic review and meta-analysis. 174(3), 357\u2013368. https:\/\/doi.org\/10.1001\/jamainternmed.2013.13018","DOI":"10.1001\/jamainternmed.2013.13018"},{"key":"12_CR10","doi-asserted-by":"publisher","unstructured":"G\u00e4rtner, M., Grimm, S., Bajbouj, M.: Frontal midline theta oscillations during mental arithmetic: effects of stress. 9. https:\/\/doi.org\/10.3389\/fnbeh.2015.00096","DOI":"10.3389\/fnbeh.2015.00096"},{"key":"12_CR11","doi-asserted-by":"publisher","unstructured":"Hamid, N.H.A., Sulaiman, N., Aris, S.A.M., Murat, Z.H., Taib, M.N.: Evaluation of human stress using EEG Power Spectrum. In: 2010 6th International Colloquium on Signal Processing & Its Applications, pp.\u00a01\u20134. IEEE. https:\/\/doi.org\/10.1109\/CSPA.2010.5545282","DOI":"10.1109\/CSPA.2010.5545282"},{"key":"12_CR12","doi-asserted-by":"publisher","unstructured":"Hamid, N.H.A., Sulaiman, N., Murat, Z.H., Taib, M.N.: Brainwaves stress pattern based on perceived stress scale test. In: 2015 IEEE 6th Control and System Graduate Research Colloquium (ICSGRC), pp. 135\u2013140. IEEE. https:\/\/doi.org\/10.1109\/ICSGRC.2015.7412480","DOI":"10.1109\/ICSGRC.2015.7412480"},{"key":"12_CR13","doi-asserted-by":"publisher","unstructured":"Harmony, T., Fern\u00e1ndez, T., Silva, J., Bernal, J., D\u00edaz-Comas, L., Reyes, A., Marosi, E., Rodr\u00edguez, M., Rodr\u00edguez, M.: EEG delta activity: an indicator of attention to internal processing during performance of mental tasks. 24(1\u20132), 161\u2013171. https:\/\/doi.org\/10.1016\/S0167-8760(96)00053-0","DOI":"10.1016\/S0167-8760(96)00053-0"},{"key":"12_CR14","doi-asserted-by":"publisher","unstructured":"Herman, J.P., Cullinan, W.E.: Neurocircuitry of stress: central control of the hypothalamo\u2013pituitary\u2013adrenocortical axis. 20(2), 78\u201384. https:\/\/doi.org\/10.1016\/S0166-2236(96)10069-2","DOI":"10.1016\/S0166-2236(96)10069-2"},{"key":"12_CR15","doi-asserted-by":"publisher","unstructured":"Higuchi, T.: Approach to an irregular time series on the basis of the fractal theory. 31(2), 277\u2013283. https:\/\/doi.org\/10.1016\/0167-2789(88)90081-4","DOI":"10.1016\/0167-2789(88)90081-4"},{"key":"12_CR16","doi-asserted-by":"publisher","unstructured":"Hjorth, B.: EEG analysis based on time domain properties. 29(3), 306\u2013310. https:\/\/doi.org\/10.1016\/0013-4694(70)90143-4","DOI":"10.1016\/0013-4694(70)90143-4"},{"key":"12_CR17","doi-asserted-by":"publisher","unstructured":"Hou, X., Liu, Y., Sourina, O., Tan, Y.R.E., Wang, L., Mueller-Wittig, W.: EEG based stress monitoring. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3110\u20133115. https:\/\/doi.org\/10.1109\/SMC.2015.540","DOI":"10.1109\/SMC.2015.540"},{"key":"12_CR18","doi-asserted-by":"publisher","unstructured":"Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. 454, 903\u2013995 (1971). https:\/\/doi.org\/10.1098\/rspa.1998.0193","DOI":"10.1098\/rspa.1998.0193"},{"key":"12_CR19","doi-asserted-by":"publisher","unstructured":"Jebelli, H., Hwang, S., Lee, S.: EEG-based workers\u2019 stress recognition at construction sites. 93, 315\u2013324. https:\/\/doi.org\/10.1016\/j.autcon.2018.05.027","DOI":"10.1016\/j.autcon.2018.05.027"},{"key":"12_CR20","doi-asserted-by":"publisher","unstructured":"Jebelli, H., Mahdi\u00a0Khalili, M., Lee, S.: A continuously updated, computationally efficient stress recognition framework using electroencephalogram (EEG) by applying online multitask learning algorithms (OMTL). 23(5), 1928\u20131939. https:\/\/doi.org\/10.1109\/JBHI.2018.2870963","DOI":"10.1109\/JBHI.2018.2870963"},{"key":"12_CR21","doi-asserted-by":"publisher","unstructured":"Jun, G., Smitha, K.G.: EEG based stress level identification. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 003270\u2013003274. IEEE. https:\/\/doi.org\/10.1109\/SMC.2016.7844738","DOI":"10.1109\/SMC.2016.7844738"},{"key":"12_CR22","doi-asserted-by":"publisher","unstructured":"Khosrowabadi, R., Quek, C., Ang, K.K., Tung, S.W., Heijnen, M.: A Brain-Computer Interface for classifying EEG correlates of chronic mental stress. In: The 2011 International Joint Conference on Neural Networks, pp. 757\u2013762. https:\/\/doi.org\/10.1109\/IJCNN.2011.6033297","DOI":"10.1109\/IJCNN.2011.6033297"},{"key":"12_CR23","doi-asserted-by":"publisher","unstructured":"Koudouovoh-Tripp, P., H\u00fcfner, K., Egeter, J., Kandler, C., Giesinger, J.M., Sopper, S., Humpel, C., Sperner-Unterweger, B.: Stress enhances proinflammatory platelet activity: the impact of acute and chronic mental stress. https:\/\/doi.org\/10.1007\/s11481-020-09945-4","DOI":"10.1007\/s11481-020-09945-4"},{"key":"12_CR24","doi-asserted-by":"publisher","unstructured":"Laurent, F., Valderrama, M., Besserve, M., Guillard, M., Lachaux, J.P., Martinerie, J., Florence, G.: Multimodal information improves the rapid detection of mental fatigue 8(4), 400\u2013408. https:\/\/doi.org\/10.1016\/j.bspc.2013.01.007","DOI":"10.1016\/j.bspc.2013.01.007"},{"key":"12_CR25","doi-asserted-by":"publisher","unstructured":"Le\u00a0Fevre, M., Matheny, J., Kolt, G.S.: Eustress, distress, and interpretation in occupational stress. 18(7), 726\u2013744. https:\/\/doi.org\/10.1108\/02683940310502412","DOI":"10.1108\/02683940310502412"},{"key":"12_CR26","doi-asserted-by":"publisher","unstructured":"Mart\u00ednez-Rodrigo, A., Garc\u00eda-Mart\u00ednez, B., Zunino, L., Alcaraz, R., Fern\u00e1ndez-Caballero, A.: Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition. 13. https:\/\/doi.org\/10.3389\/fninf.2019.00040","DOI":"10.3389\/fninf.2019.00040"},{"key":"12_CR27","doi-asserted-by":"publisher","unstructured":"Minguillon, J., Lopez-Gordo, M.A., Pelayo, F.: stress assessment by prefrontal relative gamma. 10. https:\/\/doi.org\/10.3389\/fncom.2016.00101","DOI":"10.3389\/fncom.2016.00101"},{"key":"12_CR28","unstructured":"Niedermeyer, E., Schomer, D.L., Lopes\u00a0da Silva, F.H. (eds.): Niedermeyer\u2019s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 6th edn. Wolters Kluwer, Lippincott Williams & Wilkins (2017)"},{"key":"12_CR29","doi-asserted-by":"publisher","unstructured":"Paszkiel, S., Dobrakowski, P., \u0141ysiak, A.: The impact of different sounds on stress level in the context of EEG, cardiac measures and subjective stress level: a pilot study. 10(10), \u00a0728. https:\/\/doi.org\/10.3390\/brainsci10100728","DOI":"10.3390\/brainsci10100728"},{"key":"12_CR30","doi-asserted-by":"publisher","unstructured":"Saeed, S.M.U., Anwar, S.M., Majid, M., Bhatti, A.M.: Psychological stress measurement using low cost single channel EEG headset. In: 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 581\u2013585. https:\/\/doi.org\/10.1109\/ISSPIT.2015.7394404","DOI":"10.1109\/ISSPIT.2015.7394404"},{"key":"12_CR31","doi-asserted-by":"publisher","unstructured":"Saeed, S.M.U., Anwar, S.M., Majid, M.: Quantification of human stress using commercially available single channel EEG headset. E100.D(9), 2241\u20132244. https:\/\/doi.org\/10.1587\/transinf.2016EDL8248","DOI":"10.1587\/transinf.2016EDL8248"},{"key":"12_CR32","doi-asserted-by":"publisher","unstructured":"Saeed, S.M.U., Anwar, S.M., Majid, M., Awais, M., Alnowami, M.: Selection of neural oscillatory features for human stress classification with single channel EEG headset. https:\/\/doi.org\/10.1155\/2018\/1049257","DOI":"10.1155\/2018\/1049257"},{"key":"12_CR33","doi-asserted-by":"publisher","unstructured":"Secerbegovic, A., Ibric, S., Nisic, J., Suljanovic, N., Mujcic, A.: Mental workload vs. stress differentiation using single-channel EEG. In: Badnjevic, A. (ed.) CMBEBIH 2017, IFMBE Proceedings, vol.\u00a062, pp. 511\u2013515. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-10-4166-2_78","DOI":"10.1007\/978-981-10-4166-2_78"},{"key":"12_CR34","doi-asserted-by":"publisher","unstructured":"Sharma, N., Gedeon, T.: Objective measures, sensors and computational techniques for stress recognition and classification: a survey. 108(3), 1287\u20131301. https:\/\/doi.org\/10.1016\/j.cmpb.2012.07.003","DOI":"10.1016\/j.cmpb.2012.07.003"},{"key":"12_CR35","doi-asserted-by":"publisher","unstructured":"Steingrimsson, S., Bilonic, G., Ekelund, A.C., Larson, T., Stadig, I., Svensson, M., Vukovic, I.S., Wartenberg, C., Wrede, O., Bernhardsson, S.: Electroencephalography-based neurofeedback as treatment for post-traumatic stress disorder: a systematic review and meta-analysis. 63(1) (2020). https:\/\/doi.org\/10.1192\/j.eurpsy.2019.7","DOI":"10.1192\/j.eurpsy.2019.7"},{"key":"12_CR36","doi-asserted-by":"publisher","unstructured":"Subhani, A.R., Mumtaz, W., Saad, M.N.B.M., Kamel, N., Malik, A.S.: Machine learning framework for the detection of mental stress at multiple levels. 5, 13545\u201313556. https:\/\/doi.org\/10.1109\/ACCESS.2017.2723622","DOI":"10.1109\/ACCESS.2017.2723622"},{"key":"12_CR37","doi-asserted-by":"publisher","unstructured":"Suhaimi, N.S., Mountstephens, J., Teo, J.: EEG-based emotion recognition: a state-of-the-art review of current trends and opportunities. https:\/\/doi.org\/10.1155\/2020\/8875426","DOI":"10.1155\/2020\/8875426"},{"key":"12_CR38","doi-asserted-by":"publisher","unstructured":"Sulaiman, N., Taib, M.N., Lias, S., Murat, Z.H., Mustafa, M., Aris, S.A.M., Rashid, N.A.: Electroencephalogram-based stress index. 2(3), 327\u2013335. https:\/\/doi.org\/10.1166\/jmihi.2012.1106","DOI":"10.1166\/jmihi.2012.1106"},{"key":"12_CR39","doi-asserted-by":"publisher","unstructured":"Sulaiman, N., Ying, B.S., Mustafa, M., Jadin, M.S.: Offline labview-based EEG signals analysis for human stress monitoring. In: 2018 9th IEEE Control and System Graduate Research Colloquium (ICSGRC), pp. 126\u2013131. IEEE. https:\/\/doi.org\/10.1109\/ICSGRC.2018.8657606","DOI":"10.1109\/ICSGRC.2018.8657606"},{"key":"12_CR40","doi-asserted-by":"publisher","unstructured":"Ulstein, I., Wyller, T.B., Engedal, K.: High score on the Relative Stress Scale, a marker of possible psychiatric disorder in family carers of patients with dementia. 22(3), 195\u2013202. https:\/\/doi.org\/10.1002\/gps.1660","DOI":"10.1002\/gps.1660"},{"key":"12_CR41","doi-asserted-by":"publisher","unstructured":"Wang, Q., Sourina, O.: Real-time mental arithmetic task recognition from EEG signals. 21(2), 225\u2013232. https:\/\/doi.org\/10.1109\/TNSRE.2012.2236576","DOI":"10.1109\/TNSRE.2012.2236576"},{"key":"12_CR42","doi-asserted-by":"publisher","unstructured":"Zheng, Y., Wong, T.C.H., Leung, B.H.K., Poon, C.C.Y.: Unobtrusive and multimodal wearable sensing to quantify anxiety. 16(10), 3689\u20133696. https:\/\/doi.org\/10.1109\/JSEN.2016.2539383","DOI":"10.1109\/JSEN.2016.2539383"}],"container-title":["Advances in Intelligent Systems and Computing","Control, Computer Engineering and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-72254-8_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T04:04:20Z","timestamp":1616990660000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-72254-8_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030722531","9783030722548"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72254-8_12","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"value":"2194-5357","type":"print"},{"value":"2194-5365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICBCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Scientific Conference on Brain-Computer Interfaces BCI Opole","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Opole","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icbci 2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bci.po.opole.pl\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}