Advertisement

Efficient Human Stress Detection System Based on Frontal Alpha Asymmetry

  • Asma Baghdadi
  • Yassine Aribi
  • Adel M. Alimi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10637)

Abstract

EEG signals reflect the inner emotional state of a person and regarding its wealth in temporal resolution, it can be used profitably to measure mental stress. Emotional states recognition is a growing research field inasmuch to its importance in Human-machine applications in all domains, in particular psychology and psychiatry. The main goal of this study is to provide a simple method for stress detection based on Frontal Alpha Asymmetry for trials selection and time, time-frequency domain features. This approach was tested on prevalent DEAP database, and provided us with two subdatasets to be processed and classified thereafter. From the variety of features produced in the literature we chose to test Hjorth parameters and Band Power as a time-frequency feature. To enhance the classification performance, we tested the SVM classifier, K-NN and Fuzzy K-NN.

Keywords

Human stress EEG Frontal Asymmetry Hjorth Band Power FK-NN 

Notes

Acknowledgments

The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48.

References

  1. 1.
    Sulaiman, N., Taib, M.N., Lias, S., Murat, Z.H., Aris, S.A.M., Hamid, N.H.A.: Novel methods for stress features identification using EEG signals. Int. J. Simul.: Syst. Sci. Technol. 12(1), 27–33 (2011)Google Scholar
  2. 2.
    Giannakakis, G., Grigoriadis, D., Tsiknakis, M.: Detection of stress/anxiety state from EEG features during video watching. In: Conference of the IEEE Engineering in Medicine and Biology Society (2015)Google Scholar
  3. 3.
    Vanitha, V., Krishnan, P.: Real time stress detection system based on EEG signals. Biomed. Res. 27, 271–275 (2016). Special IssueGoogle Scholar
  4. 4.
    Lahane, P., Vaidya, A., Umale, C., Shirude, S., Raut, A.: Real time system to detect human stress using EEG signals. Int. J. Innovative Res. Comput. Commun. Eng. 4(4) (2016)Google Scholar
  5. 5.
    Brenner, R.P., Ulrich, R.F., Spiker, D.G., Sclabassi, R.J., Reynolds, C.F., Marin, R.S., Boller, F.: Computerized EEG spectral analysis in elderly normal, demented and depressed subjects. Electroencephalogr. Clin. Neurophysiol. 64(6), 483–492 (1986)CrossRefGoogle Scholar
  6. 6.
    Pollock, V.E., Schneider, L.S.: Topographic electroencephalographic alpha in recovered depressed elderly. J. Abnorm. Psychol. 98(3), 268–273 (1989)CrossRefGoogle Scholar
  7. 7.
    Gray, J.A.: The psychophysiological basis of introversion-extraversion. Behav. Res. Ther. 8(3), 249–266 (1970)CrossRefGoogle Scholar
  8. 8.
    Coan, J.A., Allen, J.J.: Frontal EEG asymmetry and the behavioral activation and inhibition systems. Psychophysiology 40(1), 106–114 (2003)CrossRefGoogle Scholar
  9. 9.
    Sutton, S.K., Davidson, R.J.: Prefrontal brain asymmetry: a biological substrate of the behavioral approach and inhibition systems. Psychol. Sci. 8(3), 204–210 (1997)CrossRefGoogle Scholar
  10. 10.
    Tomarken, A.J., Davidson, R.J., Wheeler, R.E., Doss, R.C.: Individual differences in anterior brain asymmetry and fundamental dimensions of emotion. J. Pers. Soc. Psychol. 62(4), 676–687 (1992)CrossRefGoogle Scholar
  11. 11.
    Dhahri, H., Alimi, A.M.: The modified differential evolution and the RBF (MDE-RBF) neural network for time series prediction. In: IEEE International Conference on Neural Networks - Conference Proceedings, p. 2938 (2006)Google Scholar
  12. 12.
    Tomarken, A.J., Davidson, R.J., Henriques, J.B.: Resting frontal brain asymmetry predicts affective responses to films. J. Pers. Soc. Psychol. 59(4), 791–801 (1990)CrossRefGoogle Scholar
  13. 13.
    Dharmawan, Z.: Analysis of computer games player stress level using EEG data. Master of Science Thesis report, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Netherlands (2007)Google Scholar
  14. 14.
    Interactive Productline IP AB-Mindball. http://www.mindball.se/index.html
  15. 15.
    Novák, D.: EEG and VEP signal processing. Technical report. Czech Technical University in Prague, Department of Cybernetics (2004)Google Scholar
  16. 16.
    Horlings, R.: Emotion recognition using brain activity. In: Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for Ph.D. Students in Computing, Gabrovo, Bulgaria, p. II.1-1 (2008)Google Scholar
  17. 17.
    Morilak, D.A.: Role of brain norepinephrine in the behavioral response to stress. Prog. Neuro-psychopharmacol. Biol. Psychiatry 29(8), 1214–1224 (2005)CrossRefGoogle Scholar
  18. 18.
    Hoffmann, E.: Brain training against stress: theory methods and results from an outcome study. Stress Rep. 4 (2005)Google Scholar
  19. 19.
    Lin, T., John, L.: Quantifying mental relaxation with EEG for use in computer games. In: International Conference on Internet Computing, Las Vegas, Nevada, USA, pp. 409–415 (2006)Google Scholar
  20. 20.
    Alimi, A.M.: Evolutionary computation for the recognition of on-line cursive handwriting. IETE J. Res. 48(5), 385–396 (2002). SPECCrossRefGoogle Scholar
  21. 21.
    Fuchs, E., Uno, H., Fluegge, G.: Chronic psychosocial stress induces morphological alterations in hippocampal pyramidal neurons of the tree shrew. Brain Res. 673, 275–282 (1995)CrossRefGoogle Scholar
  22. 22.
    Bezine, H., Alimi, A.M., Derbel, N.: Handwriting trajectory movements controlled by a beta-elliptic model. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, p. 1228 (2003)Google Scholar
  23. 23.
    Hughes, J.W., Stoney, C.M.: Depressed mood is related to high-frequency heart rate variability during stressors. Psychosom. Med. 62, 796–803 (2000)CrossRefGoogle Scholar
  24. 24.
    Baghdadi, A., Aribi, Y., Alimi, A.M.: A survey of methods and performances for EEG-based emotion recognition. In: Abraham, A., Haqiq, A., Alimi, A.M., Mezzour, G., Rokbani, N., Muda, A.K. (eds.) HIS 2016. AISC, vol. 552, pp. 164–174. Springer, Cham (2017). doi: 10.1007/978-3-319-52941-7_17 CrossRefGoogle Scholar
  25. 25.
    Lawrence, D.A., Kim, D.: Central/peripheral nervous system and immune responses. Toxicology 142, 189–201 (2000)CrossRefGoogle Scholar
  26. 26.
    NIOSH, Stress at Work, NIOSH Publication Number 99-101 (1999)Google Scholar
  27. 27.
    Cooper, C.: Stress in the workplace. Br. J. Hosp. Med. 55, 559–563 (1996)Google Scholar
  28. 28.
    Manning, M., Jackson, C., Fusilier, M.: Occupational stress, social support, and the costs of health care. Acad. Manag. J. 39, 738–750 (1996)CrossRefGoogle Scholar
  29. 29.
    Ansari-asl, K., Chanel, G., Pun, T.: A channel selection method for EEG classification in emotion assessment based on synchronization likelihood. In: Proceedings of 15th European Signal Processing Conference, pp. 1241–1245 (2007)Google Scholar
  30. 30.
    Hjorth, B.: EEG analysis based on time domain properties. Electroencephalogr. Clin. Neurophysiol. 29(3), 306–310 (1970)CrossRefGoogle Scholar
  31. 31.
    Horlings, R., Datcu, D., Rothkrantz, L.: Emotion recognition using brain activity. In: Proceedings of International Conference on Computer Systems and Technologies, p. II.116 (2008)Google Scholar
  32. 32.
    Bastos-Filho, T.F., Ferreira, A., Atencio, A.C.: Evaluation of feature extraction techniques in emotional state recognition. In: IEEE Proceedings of 4th International Conference on Intelligent Human Computer Interaction, Kharagpur, India, 27–29 December 2012Google Scholar
  33. 33.
    Hosseini, S.A., Khalilzadeh, M., Changiz, S.: Emotional stress recognition system for affective computer based on bio-signals. J. Biol. Syst. 18, 101–114 (2010). Special IssueCrossRefGoogle Scholar
  34. 34.
    García-Martínez, B., Martínez-Rodrigo, A., Cantabrana, R.Z., García, J.M.P., Martínez, R.A.: Application of entropy-based metrics to identify emotional distress from electroencephalographic recordings. Entropy 18, 221 (2016)CrossRefMathSciNetGoogle Scholar
  35. 35.
    García-Martínez, B., Martínez-Rodrigo, A., Zangróniz, R., García, J.M.P., Alcaraz, R.: Symbolic analysis of brain dynamics detects negative stress. Entropy 18, 221 (2017)CrossRefGoogle Scholar
  36. 36.
    Elbaati, A., Boubaker, H., Kherallah, M., Alimi, A.M., Ennaji, A., Abed, H.E.: Arabic handwriting recognition using restored stroke chronology. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, p. 411 (2009)Google Scholar
  37. 37.
    Aribi, Y., Wali, A., Alimi, A.M.: Automated fast marching method for segmentation and tracking of region of interest in scintigraphic images sequences. In: Azzopardi, G., Petkov, N. (eds.) CAIP 2015. LNCS, vol. 9257, pp. 725–736. Springer, Cham (2015). doi: 10.1007/978-3-319-23117-4_62 CrossRefGoogle Scholar
  38. 38.
    Aribi, Y., Wali, A., Hamza, F., Alimi, A.M., Guermazi, F.: ARG: a semiautomatic system for ROI detection on Renal Scintigraphic images. In: Proceedings of the 14th International Conference on Hybrid Intelligent Systems (HIS 2014), Kuwait, December 2014Google Scholar
  39. 39.
    Aribi, Y., Wali, A., Alimi, A.M.: An intelligent system for renal segmentation. In: Proceedings of the 15th International Conference on e-Health Networking - Healthcom 2013, Lisbon, Portugal, pp. 1–6, October 2013Google Scholar
  40. 40.
    Aribi, Y., Wali, A., Chakroun, M., Alimi, A.M.: Automatic definition of regions of interest on renal scintigraphic images. In: Proceedings of the Conference on Intelligent Systems and Control, Vancouver, Canada, AASRI Procedia, vol. 4, pp. 37–42 (2013)Google Scholar
  41. 41.
    Aribi, Y., Wali, A., Alimi, A.M.: A system based on the fast marching method for analysis and processing DICOM images: the case of renal scintigraphy dynamic. In: Proceedings of the International Conference on Computer Medical Applications (ICCMA 2013), Sousse, Tunisia, pp. 1–6, January 2013Google Scholar
  42. 42.
    Aribi, Y., Wali, A., Hamza, F., Alimi, A.M., Guermazi, F.: Analysis of scintigraphic renal dynamic studies: an image processing tool for the clinician and researcher. In: Hassanien, A.E., Salem, A.-B.M., Ramadan, R., Kim, T. (eds.) AMLTA 2012. CCIS, vol. 322, pp. 267–275. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-35326-0_27 CrossRefGoogle Scholar
  43. 43.
    Aribi, Y., Hamza, F., Wali, A., Alimi, A.M., Guermazi, F.: An automated system for the segmentation of dynamic scintigraphic images. Appl. Med. Inform. 34(2), 1–12 (2014)Google Scholar
  44. 44.
    DEAP dataset, a dataset for emotion analysis using EEG, physiological and video signals. http://www.eecs.qmul.ac.uk/mmv/datasets/deap/

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.REGIM: REsearch Groups in Intelligent Machines, National School of EngineersUniversity of SfaxSfaxTunisia

Personalised recommendations