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A Novel Method for Stress Measuring Using EEG Signals

  • Vinayak Bairagi
  • Sanket Kulkarni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 887)

Abstract

Stress is one of the major contributing factors which lead to various diseases including cardiovascular diseases. To avoid this, stress monitoring is very essential for clinical intervention and disease prevention. In present study, the feasibility of exploiting Electroencephalography (EEG) signals to monitor stress in mental arithmetic tasks is investigated. This paper presents a novel hardware system along with software system which provides a method for determining stress level with the help of a Theta sub-band of EEG signals. The proposed system performs a signal-processing of EEG signals, which recognizes the peaks of the Theta sub-band above a certain threshold value. It finds the first order difference information to identify the peak. This proposed method of EEG based stress detection can be used as quick, noninvasive, portable and handheld tool for determining the stress level of a person.

Keywords

Mental stress Electroencephalography (EEG) Theta sub-band 

Notes

Acknowledgment

The authors would like to thank the BIRAC GYTI for financial supporting this work under research grant for researchers and the SKN General Hospital, Pune for their valuable help and support. The author would like to thank all authors of the references which have been used, as well as reviewers of the paper. The authors would like to thank the SERB-ITS for its travel grant support.

References

  1. 1.
    Mcewen, B.S.: Central effects of stress hormones in health and disease: understanding the protective and damaging effects of stress and stress mediators. Eur. J. Pharmacol. 583(2–3), 174–185 (2008)CrossRefGoogle Scholar
  2. 2.
    Cohen, S., Janicki-Deverts, D., Miller, G.E.: Psychological stress and disease. J. Am. Med. Assoc. 298(14), 1685–1687 (2007)CrossRefGoogle Scholar
  3. 3.
    Steptoe, A., Kivimaki, M.: Stress and cardiovascular disease. Nat. Rev. Cardiol. 9(1), 360–370 (2012)CrossRefGoogle Scholar
  4. 4.
    Berka, C., Levendowski, D.J., Cvetinovic, M.M., Petrovic, M.M., Davis, G., Lumicao, M.N., Zivkovic, V.T., Popovic, M.V., Olmstead, R.: Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset. Int. J. Hum. Comput. Interact. 17(2), 151–170 (2004)CrossRefGoogle Scholar
  5. 5.
    Kulkarni, N.N., Bairagi, V.K.: Extracting salient features for EEG-based diagnosis of Alzheimer’s disease using support vector machine classifier. IETE J. Res. 63(1), 11–22 (2016)CrossRefGoogle Scholar
  6. 6.
    Kulkarni, N.N., Bairagi, V.K.: Electroencephalogram based diagnosis of Alzheimer Disease. In: 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO), pp 1–6 (2015)Google Scholar
  7. 7.
    Wang, X., Nie, D., Lu, B.: Emotional state classification from EEG data using machine learning approach. Neurocomputing 129, 94–106 (2014)CrossRefGoogle Scholar
  8. 8.
    Petrantonakis, P.C., Hadjileontiadis, L.J.: Emotion recognition from brain signals using hybrid adaptive filtering and higher order crossings analysis. IEEE Trans. Affect. Comput. 1(2), 81–97 (2010)CrossRefGoogle Scholar
  9. 9.
    Tatum, W.O.: Handbook of EEG Interpretation, pp. 28–34. Demos Medical Publishing, New York (2014)CrossRefGoogle Scholar
  10. 10.
    Gayakwad, R.A.: Op-amps and Linear Integrated Circuits, pp. 249–298. Prentice-Hall, Englewood Cliffs (1988)Google Scholar
  11. 11.
    Taelman, J., Vandeput, S., Spaepen, A., Huffel, S.: Influence of mental stress on heart rate and heart rate variability. In: 4th Springer European Conference of the International Federation for Medical and Biological Engineering, IFMBE Proceedings, vol. 22, pp. 1366–1369 (2008)Google Scholar
  12. 12.
    Patil, K., Singh, M., Singh, G., Anjali, S.N.: Mental stress evaluation using heart rate variability analysis: a review. Int. J. Public Ment. Health Neurosci. 2(1), 10–16 (2015)Google Scholar
  13. 13.
    Abouelenien, M., Burzo, M., Mihalcea, R.: Human acute stress detection via integration of physiological signals and thermal imaging. In: Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments, p. 32. ACM (2016)Google Scholar
  14. 14.
    Vanitha, L., Suresh, G.R.: Hierarchical SVM to detect mental stress in human beings using heart rate variability. In: 2nd International Conference on Devices, Circuits and Systems (ICDCS), pp. 1–5 (2014)Google Scholar
  15. 15.
    Sun, F., Kuo, C., Cheng, H., Buthpitiya, S., Collins, P., Griss, M.: Activity-aware mental stress detection using physiological sensors. In: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Mobile Computing, Applications, and Services, pp. 211–230 (2012)Google Scholar
  16. 16.
    Siegler, J.C., Rehman, S., Bhumireddy, G.P., Abdula, R., Klem, I., Brener, S.J., Heitner, J.F.: The accuracy of the electrocardiogram during exercise stress test based on heart size. PLoS ONE 6(8), e23044 (2011)CrossRefGoogle Scholar
  17. 17.
    Hou, X., Liu, Y., Sourina, O., Mueller-Wittig, W.: CogniMeter: EEG-based emotion, mental workload and stress visual monitoring. In: International Conference on Cyberworlds (CW), pp 153–160 (2015)Google Scholar
  18. 18.
    Jun, G., Smitha, K.G.: EEG based stress level identification. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 3270–3274 (2016)Google Scholar
  19. 19.
    Vanitha, V., Krishnan, P.: Real time stress detection system based on EEG signals. Biomed. Res. 1(1), 1–5 (2016)Google Scholar
  20. 20.
    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. Biomed. Opt. Express 7(10), 3882–3898 (2016)CrossRefGoogle Scholar
  21. 21.
    Salai, M., Vassanyi, I., Kosa, I.: Stress detection using low cost heart rate sensors. J. Healthc. Eng. 2016, 1–13 (2016)CrossRefGoogle Scholar
  22. 22.
    Toth, V.: Measurement of stress intensity using EEG. Unpublished master’s thesis. Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics 2015Google Scholar
  23. 23.
    Rodriguez, M.: Mental stress detection using multimodal sensing in a wireless body area network. In: Informatiktage, pp. 163–166 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of E&TCAISSMS’s Institute of Information TechnologyPuneIndia

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