ESP-4165

Published

2025-10-30

Issue

Vol. 10 No. 10 (2025): Published

Section

Research Articles

License

Copyright (c) 2025 Tolstoguzov N. Sergey, Savin R. Michael, Elifanov V. Andrey

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

The journal adopts the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), which means that anyone can reuse and redistribute the materials for non-commercial purposes as long as you follow the license terms and the original source is properly cited.

Author(s) shall retain the copyright of their work and grant the Journal/Publisher rights for the first publication with the work concurrently licensed since 2023 Vol.8 No.2.

Under this license, author(s) will allow third parties to download, reuse, reprint, modify, distribute and/or copy the content under the condition that the authors are given credit. No permission is required from the authors or the publisher.

This broad license intends to facilitate free access, as well as the unrestricted use of original works of all types. This ensures that the published work is freely and openly available in perpetuity.

By providing open access, the following benefits are brought about:

  • Higher Visibility, Availability and Citations-free and unlimited accessibility of the publication over the internet without any restrictions increases citation of the article.
  • Ease of search-publications are easily searchable in search engines and indexing databases.
  • Rapid Publication – accepted papers are immediately published online.
  • Available for free download immediately after publication at https://esp.as-pub.com/index.php/ESP

 

Copyright Statement

1.The authors certify that the submitted manuscripts are original works, do not infringe the rights of others, are free from academic misconduct and confidentiality issues, and that there are no disputes over the authorship scheme of the collaborative articles. In case of infringement, academic misconduct and confidentiality issues, as well as disputes over the authorship scheme, all responsibilities will be borne by the authors.

2. The author agrees to grant the Editorial Office of Environment and Social Psychology a licence to use the reproduction right, distribution right, information network dissemination right, performance right, translation right, and compilation right of the submitted manuscript, including the work as a whole, as well as the diagrams, tables, abstracts, and any other parts that can be extracted from the work and used in accordance with the characteristics of the journal. The Editorial Board of Environment and Social Psychology has the right to use and sub-licence the above mentioned works for wide dissemination in print, electronic and online versions, and, in accordance with the characteristics of the periodical, for the period of legal protection of the property right of the copyright in the work, and for the territorial scope of the work throughout the world.

3. The authors are entitled to the copyright of their works under the relevant laws of Singapore, provided that they do not exercise their rights in a manner prejudicial to the interests of the Journal.

About Licence

Environment and Social Psychology is an open access journal and all published work is available under the Creative Commons Licence, Authors shall retain copyright of their work and grant the journal/publisher the right of first publication, and their work shall be licensed under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).

Under this licence, the author grants permission to third parties to download, reuse, reprint, modify, distribute and/or copy the content with attribution to the author. No permission from the author or publisher is required.

This broad licence is intended to facilitate free access to and unrestricted use of original works of all kinds. This ensures that published works remain free and accessible in perpetuity. Submitted manuscripts, once accepted, are immediately available to the public and permanently accessible free of charge on the journal’s official website (https://esp.as-pub.com/index.php/ESP). Allowing users to read, download, copy, print, search for or link to the full text of the article, or use it for other legal purposes. However, the use of the work must retain the author's signature, be limited to non-commercial purposes, and not be interpretative.

Click to download <Agreement on the Licence for the Use of Copyright on Environmental and Social Psychology>.

How to Cite

Tolstoguzov N. Sergey, Savin R. Michael, & Elifanov V. Andrey. (2025). Dynamics of spectral characteristics of EEG under induced stress of psychosocial genesis. Environment and Social Psychology, 10(10), ESP-4165. https://doi.org/10.59429/esp.v10i10.4165

Dynamics of spectral characteristics of EEG under induced stress of psychosocial genesis

Tolstoguzov N. Sergey

School of Natural Sciences, University of Tyumen, Tyumen, 625003, Russia

Savin R. Michael

master's student, Tomsk Polytechnic University, Tomsk, 634050, Russia

Elifanov V. Andrey

School of Natural Sciences, University of Tyumen, Tyumen, 625003, Russia


DOI: https://doi.org/10.59429/esp.v10i10.4165


Keywords: EEG; full spectrum power; alpha/theta; alpha frontal asymmetry; Trier test; psychosocial stress

Abstract

The purpose of this work was to study the spectral characteristics of the EEG during induced stress of psychosocial origin in young people studying at a higher educational institution.

In 40 subjects (19 men and 21 women), the indicators of the full power of the spectrum (μV2) in the given frequency ranges, the integral EEG alpha/theta indices for the power of the spectrum were studied using 16 standard leads, and the averaged indicators of the full power of the spectrum (μV2) in the frontal areas of the left (Fp1, F3, F7) and right (Fp2, F4, F8) hemispheres.

According to the full spectrum power indicator, averaged over 16 active leads, an increase was noted in the Δ-, θ-, α- and β-ranges in the preparatory, reactive and cognitive stages of the stress reaction, which indicated the involvement of all oscillatory systems in the implementation of the stress response, regardless of their relationship with the brainstem, limbic (Δ- and θ-rhythms) or thalamocortical (α- and β-rhythms) structures. The alpha/theta index decreased according to the stages of the Trier test. Psychosocial reactivity in the slow-wave components (Δ- and θ-rhythms) of the power spectrum was more pronounced in men, while in the fast-wave components (β1- and β2-rhythms) - in women. No frontal asymmetry of the alpha rhythm was detected during the experiment.

References

[1]. 1.Houssein E.H., Hammad A., Ali A.A. (2022). Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review. Neural Computing and Applications. 34, 12527–12557. DOI: 10.1007/s00521-022-07292-4.

[2]. 2.Patel P.R., Annavarapu R.N. (2021). EEG-based human emotion recognition using entropy as a feature extraction measure. Brain Informatics. 8(20). DOI: 10.1186/s40708-021-00141-5.

[3]. 3.Suhaimi N.S., Mountstephens J., Teo J. (2022). A dataset for emotion recognition using virtual reality and EEG (DER-VREEG): Emotional state classification using low-cost wearable VR-EEG headsets. Big Data and Cognitive Computing. 6(16). DOI:10.3390/bdcc6010016.

[4]. 4.Badr Y., Tariq U., Al-Shargie F. et. al. (2024). A review on evaluating mental stress by deep learning using EEG signals. Neural Computing and Applications. DOI: 10.1007/s00521-024-09809-5.

[5]. 5.Bhatnagar S., Khandelwal S., Jain S. et. al. (2023). A deep learning approach for assessing stress levels in patients using electroencephalogram signals. Decision Analytics Journal. 7, 100211. DOI: 10.1016/j.dajour.2023.100211.

[6]. 6.Katmah R., Al-Shargie F., Tariq U. et. al. (2021). A review on mental stress assessment methods using EEG signals. Sensors. 21(15), 5043. DOI: 10.3390/s21155043.

[7]. 7.Dick O.E., Svyatogor I.A., Reznikova T.N., Fedoryaka D.А., Nozdrachev А.D. (2020). Analysis of the EEG patterns in subjects with panic attacks. Human Physiology. 46(2), 63-75. DOI: 10.31857/S0131164620010063.

[8]. 8.Hag A., Handayani D., Altalhi M. et. al. (2021). Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm. Sensors. 21, 8370. DOI: 10.3390/s21248370.

[9]. 9.Swaymprabha Alias Megha Mane, Arundhati Shinde. (2023). StressNet: Hybrid model of LSTM and CNN for stress detection from electroencephalogram signal (EEG). Results in Control and Optimization. 11. DOI: 10.1016/j.rico.2023.100231.

[10]. 10.Shah J.L., Paquin V., Mcllwaine S.V. et. al. (2023). Examining the psychobiological response to acute social stress across clinical stages and symptom trajectories in the early psychosis continuum. Development and Psychopathology. 36(2), 1-13. DOI: 10.1017/S0954579423000056.

[11]. 11.Jeunet C., Tonin L., Albert L. et. al. (2020). Uncovering EEG Correlates of Covert Attention in Soccer Goalkeepers: Towards Innovative Sport Training Procedures. Scientific Reports. 10(1). DOI:10.1038/s41598-020-58533-2.

[12]. 12.Perez-Valero E., Vaquero-Blasco M.A., Lopez-Gordo M.A. et.al. (2021). Quantitative assessment of stress through EEG during a virtual reality stress-relax session. Frontiers in Computational Neuroscience. 15, 684423. DOI: 10.3389/fncom.2021.684423.

[13]. 13.Muscatell K.A., Merritt C.C., Cohen J.R. et. al. (2022). The Stressed Brain: Neural Underpinnings of Social Stress Processing in Humans. Current topics in behavioral neurosciences. 54, 373-392. DOI: 10.1007/7854_2021_281.

[14]. 14.Vasilenko E.A. (2019). The problem of essence and functions of social stress in psychology. South Ural State Humanitarian Pedagogical University Bulletin. 3, 299-319. DOI: 10.25588/CSPU.2019.27.22.020.

[15]. 15.Kirschbaum C., Pirke K-M., Hellhammer D.H. (1993). The “Trier social stress test” – a tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology. 28, 76–81. DOI: 10.1159/000119004.

[16]. 16.Attar E.T. (2022). Review of electroencephalography signals approaches for mental stress assessment. Neurosciences (Riyadh). 27(4), 209-215. DOI: 10.17712/nsj.2022.4.20220025.

[17]. 17.Vanhollebeke G., Kappen M., Raedt R., Baeken C., Mierlo P., Vanderhasselt M-A. (2023). Effects of acute psychosocial stress on source level EEG power and functional connectivity measures. Scientific Reports. 13(1), 8807. DOI: 10.1038/s41598-023-35808-y.

[18]. 18.Vanhollebeke G., Smet S., Raedt R., Baeken C., Mierlo P., Vanderhasselt M-A. (2022). The neural correlates of psychosocial stress: A systematic review and meta-analysis of spectral analysis EEG studies. Neurobiology of Stress. 18, 100452. DOI: 10.1016/ j.ynstr.2022.100452.

[19]. 19.Raufi B., Longo L. (2022). An Evaluation of the EEG Alpha-to-Theta and Theta-to-Alpha Band Ratios as Indexes of Mental Workload. Frontiers in Neuroinformatics. 16. DOI:10.3389/fninf.2022.861967.

[20]. 20.Raufi B., Longo L. (2024). Comparing ANOVA and PowerShap Feature Selection Methods via Shapley Additive Explanations of Models of Mental Workload Built with the Theta and Alpha EEG Band Ratios. BioMedInformatics. 4(1), 853-876. DOI: 10.3390/biomedinformatics4010048.

[21]. 21.Svyatogor I.A., Mokhovikova I.A., Bekshayev S.S., Nozdrachev A.D. (2005). EEG Pattern as Instrument for Evaluation of Neurophysiological Mechanisms Underlyng Adaptation Disorders. I.P. Pavlov Journal of Higher Nervous Activity. 55(2), 178-188.

[22]. 22.Berretz G., Packheiser J.,Wolf O.T., Ocklenburg S. (2022). Acute stress increases left hemispheric activity measured via changes in frontal alpha asymmetries. iScience. 25(2). DOI: 10.1016/j.isci.2022.103841.

[23]. 23.Dzhebrailova T.D., Korobeinikova I.I., Karatygina N.A., Biryukovab E.V., Venerinab Y.A. (2021). Dynamics of EEG spectral characteristics in subjects with various trait anxiety levels performing cognitive tests. Human Physiology. 47(6), 20-30. DOI: 10.31857/S0131164621010033.

[24]. 24.Krivonogova E.V., Krivonogova O.V., Poskotinova L.V. (2022). Changes in EEG rhythms and heart rate variability in response to cold, depending on the parameters of voluntary attention in young people. Experimental Psychology (Russia). 15(1), 56-71. DOI: 10.17759/exppsy.2022150104.

[25]. 25.Murtazina E.P., Ginzburg-Shic Yu.A. (2023). Theta and alpha bands spectral power of resting-state EEG in groups with different efficiency of joint activity in diads. Pavlov Journal of Higher Nervous Activity. 73(1), 24-37. DOI: 10.31857/S0044467723010112.

[26]. 26.Saffari F., Norouzi K., Bruni L.E., Zarei S., Ramsoy T.Z. (2023). Impact of varying levels of mental stress on phase information of EEG Signals: A study on the Frontal, Central, and parietal regions. Biomedical Signal Processing and Control. 86, 105236. DOI: 10.1016/j.bspc.2023.105236.

[27]. 27.Volkind N.Ya. (1984). Electroencephalographic indicators in students during exams. Hygiene and sanitation. 7, 81-83.

[28]. 28.Rabadanova A.I., Taygibova Z.A. (2020). Characteristic EEG Patterns in Individuals with Non-Chemical Addiction. Human Physiology. 46(6), 60-69. DOI: 10.31857/S0131164620050112.

[29]. 29.Yao M., Lei Y., Li P., Ye Q., Liu Y., Li X., Peng W. (2020). Shared Sensitivity to Physical Pain and Social Evaluation. The Journal of Pain. 21(5-6), 677-688. DOI: 10.1016/j.jpain.2019.10.007.

[30]. 30.Karakas S. (2020). A review of theta oscillation and its functional correlates. International Journal of Psychophysiology. 157, 82-99. DOI: 10.1016/j.ijpsycho.2020.04.008.

[31]. 31.Kortink E.D., Weeda W.D., Crowley M.J., Gunther Moor B., van der Molen M.J.W. (2018). Community structure analysis of rejection sensitive personality profiles: A common neural response to social evaluative threat? Cognitive, Affective and Behavioral Neuroscience. 18(3), 581-595. DOI: 10.3758/s13415-018-0589-1.

[32]. 32.Van der Molen M.J.W., Dekkers L.M.S., Westenberg P.M., van der Veen F.M., van der Molen, M.W. (2017). Why don't you like me? Midfrontal theta power in response to unexpected peer rejection feedback. NeuroImage. 146, 474-483. DOI: 10.1016/j.neuroimage.2016.08.045.

[33]. 33.Machinskaya R.I., Rozovskaya R.I., Kurgansky A.V., Pechenkova E.V. (2016). Cortical functional connectivity during retention of affective pictures in working memory: EEG-source theta coherence analysis. Human Physiology. 42(3). 56-73. DOI: 10.7868/S0131164616020120.

[34]. 34.Basar E., Schurmann M., Sakowitz O. (2001). The selectively distributed theta system: functions. International Journal of Psychophysiology. 39(2–3), 197-212. DOI: 10.1016/s0167-8760(00)00141-0.

[35]. 35.Hafeez M.A., Shakil S., Jangsher S. (2018). Stress Effects on Exam Performance using EEG. 14th International Conference on Emerging Technologies (ICET). DOI: 10.1109/ICET.2018.8603652.

[36]. 36.Al-Ezzi А., Al-Shargabi A.A., Al-Shargie F., Zahary A.T. (2022). Complexity Analysis of EEG in Patients With Social Anxiety Disorder Using Fuzzy Entropy and Machine Learning Techniques. IEEE Access. 10, 39926-39938. DOI: 10.1109/ACCESS.2022.3165199.

[37]. 37.Al-Shargie F., Tang T.B., Badruddin N., Kiguchi M. (2018). Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach. Medical and Biological Engineering and Computing. 56(1), 125-136. DOI: 10.1007/s11517-017-1733-8.

[38]. 38.Betti S., Lova R.M., Rovini E., Acerbi G., Santarelli L., Cabiati M., Ry S.D., Cavallo F. (2018). Evaluation of an integrated system of wearable physiological sensors for stress monitoring in working environments by using biological markers. IEEE Transactions on Biomedical Engineering. 65(8), 1748-1758. DOI: 10.1109/TBME.2017.2764507.

[39]. 39.Boytsova J.A., Danko S.G., Solovjeva M.L., Kachalova L.M., Kuperman I.A. (2020). EEG-correlates of preparatory and executive attention at the performance of tasks with various directionality of attention. Human Physiology. 46(6), 16-26. DOI: 10.31857/S0131164620050033.

[40]. 40.Jensen O., Gelfand J., Kounios J., Lisman J.E. (2002). Oscillations in the alpha band (9–12 Hz) increase with memory load during retention in a short-term memory task. Cerebral Cortex. 12(8), 877-882. DOI: 10.1093/cercor/12.8.877.

[41]. 41.Sauseng P., Klimesch W., Doppelmayr M. et al. (2005). EEG alpha synchronization and functional coupling during top-down processing in a working memory task. Human Brain Mapping. 26(2), 148-155. DOI: 10.1002/hbm.20150.

[42]. 42.Ehrhardt N.M., Fietz J., Kopf-Beck J., Kappelmann N., Brem A-K. (2022). Separating EEG correlates of stress: Cognitive effort, time pressure, and social-evaluative threat. European Journal of Neuroscience. Special Issue: Stress Brain and Behavior. 55(9-10). 2464-2473. DOI: 10.1111/ejn.15211.

[43]. 43.Perrin S.L., Jay S.M., Vincent G.E., Sprajcer M., Lack L., Ferguson S.A., Vakulin A. (2019) Waking qEEG to assess psychophysiological stress and alertness during simulated on-call conditions. International Journal of Psychophysiology. 141, 93-100. DOI: 10.1016/j.ijpsycho.2019.04.001.

[44]. 44.Goodale S.E., Ahmed N., Zhao C. et al. (2021). fMRI-based detection of alertness predicts behavioral response variability. eLife. 10. DOI: 10.7554/eLife.62376.

[45]. 45.Cheremushkin E.A., Petrenkoa N.E., Alipov N.N., Sergeeva O.V. (2022). Changes in the power characteristics of the alpha-rhythm in students with different levels of anxiety in normal sleep and partial deprivation when solving the Go/NoGo problem. Human Physiology. 48(2), 79-85. DOI: 10.31857/S0131164622020047.

[46]. 46.Härpfer K., Spychalski D., Kathmann N., Riesel A. (2021). Diverging patterns of EEG alpha asymmetry in anxious apprehension and anxious arousal. Biological Psychology. 162. DOI: 10.1016/j.biopsycho.2021.108111.

[47]. 47.Prentice A., Barreiros A.R., van der Vinne N. (2023). Rostral Anterior Cingulate Cortex Oscillatory Power Indexes Treatment-Resistance to Multiple Therapies in Major Depressive Disorder. Neuropsychobiology. 82(6), 373–383. DOI: 10.1159/000533853.

[48]. 48.Van der Vinne N., Vollebregt M.A., van Putten M.J.A.M. (2017). Frontal alpha asymmetry as a diagnostic marker in depression: Fact or fiction? A meta-analysis. NeuroImage: Clinical. 16, 79-87. DOI: 10.1016/j.nicl.2017.07.006.

[49]. 49.Yatsenko M.V., Kaigorodova N.Z., Kazin E.M., Fedorov A.I. (2018). EEG Correlation of the Influence of Endogenous and Exogenous Factorson Mental Work Capacity in Students. Human Physiology. 44(6), 36-48. DOI: 10.1134/S0131164618060152.

[50]. 50. Guo C.C., Sturm V.E., Zhou J., Gennatas E.D., Trujillo A.J., Hua A.Y., Crawford R., Stables L., Kramer J.H., Rankin K., Levenson R.W., Rosen H.J., Miller B.L., Seeley W.W. (2016). Dominant hemisphere lateralization of cortical parasympathetic control as revealed by frontotemporal dementia. Proceedings of the National Academy of Sciences USA. 113(17), 2430-2439. DOI: 10.1073/pnas.1509184113.



21 Woodlands Close #02-10, Primz Bizhub,Postal 737854, Singapore

Email:editorial_office@as-pub.com