Advertisement

Emotion Assessment Based on EEG Brain Signals

  • Sali IssaEmail author
  • Qinmu Peng
  • Xinge You
  • Wahab Ali Shah
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

This paper presents an emotion assessment method that classifies five emotions (happy, sad, angry, fear, and disgust) using EEG brain signals. Public DEAP database is chosen for the proposed system evaluation, Fz channel electrode is selected for the feature extraction process. Then a Continuous Wavelet Transform (CWT) is used to extract the proposed Standard Deviation Vector (SDV) feature which describes brain voltage variation in both time and frequency domains. Finally, several machine learning classifiers are used for the classification stage. Experiment results show that the proposed SDV feature with SVM Classifier produce robust system with high accuracy result of about 91%.

References

  1. 1.
    Busso, C., Deng, Z., Yildirim, S., Bulut, M., Lee, C.M., Kazemzadeh, A., Lee, S., Neumann, U., Narayanan, S.: Analysis of emotion recognition using facial expressions, speech and multimodal information. In: Proceedings of the 6th International Conference on Multimodal Interfaces, pp. 205–211. ICML, State College, Pennsylvania, USA (2004)Google Scholar
  2. 2.
    Emerich, S., Lupu, E., Apatean, A.: Emotions recognition by speechand facial expressions analysis. In: 17th European on Signal Processing Conference, Glasgow, UK, pp. 1617–1621. IEEE (2009)Google Scholar
  3. 3.
    Arnold, M.B.: Emotion and Personality: Psychological Aspects. Columbia University Press, New York (1960)Google Scholar
  4. 4.
    Frijda, N.H.: The Emotions. Cambridge University Press, UK (1986)Google Scholar
  5. 5.
    Canon, W.: The James Lange theory of emotion: a critical examination and an alternative theory. Am. J. Psychol. 39, 106–124 (1927)CrossRefGoogle Scholar
  6. 6.
    LeDoux, J.E.: Brain mechanisms of emotion and emotional learning. Curr. Opin. Neurobiol. 2(2), 191–197 (1992)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: DEAP: a database for emotion analysis using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)CrossRefGoogle Scholar
  8. 8.
    Chanel, G., Rebetez, C., Btrancourt, M., Pun, T.: Emotion assessment from physiological signals for adaptation of game difficulty. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 41(6), 1052–1063 (2011)CrossRefGoogle Scholar
  9. 9.
    Atkinson, J., Campos, D.: Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Expert. Syst. Appl. 47, 35–41 (2016)CrossRefGoogle Scholar
  10. 10.
    Soleymani, M., Lichtenauer, J., Pun, T., Pantic, M.: A multimodal database for affect recognition and implicit tagging. IEEE Trans. Affect. Comput. 3(1), 42–55 (2012)CrossRefGoogle Scholar
  11. 11.
    Stikic, M., Johnson, R.R., Tan, V., Berka, C.: EEG-based classification of positive and negative affective states. Brain-Comput. Interfaces 1(2), 99–112 (2014)CrossRefGoogle Scholar
  12. 12.
    Murugappan, M., Juhari, M.R.B.M., Ramachandran, N., Yaacob, S.: An investigation on visual and audiovisual stimulus based emotion recognition using EEG. Int. J. Med. Eng. Inform. 1(3), 342–356 (2009)CrossRefGoogle Scholar
  13. 13.
    Nasehi, S., Pourghassem, H.: An optimal EEG-based emotion recognition algorithm using Gabor features. WSEAS Trans. Signal Process. 8(3), 87–99 (2012)Google Scholar
  14. 14.
    Wang, S., Zhu, Y., Yue, L., Ji, Q.: Emotion recognition with the help of privileged information. IEEE Trans. Auton. Ment. Dev. 7(3), 189–200 (2015)CrossRefGoogle Scholar
  15. 15.
    Atkinson, J., Campos, D.: Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Expert Syst. Appl. 47(C), 35–41 (2016)CrossRefGoogle Scholar
  16. 16.
    Kumar, N., Khaund, K., Hazarika, S.M.: Bispectral analysis of EEG for emotion recognition. In: 7th International conference on Intelligent Human Computer Interaction, IHCI 2015, pp. 31–35, IIIT-Allahabad, India (2015). Procedia Comput. Sci.CrossRefGoogle Scholar
  17. 17.
    Mehmood, R.M., Du, R., Lee, H.J.: Optimal feature selection and deep learning ensembles method for emotion recognition from human brain EEG sensors. IEEE Access 5, 14797–14806 (2017)CrossRefGoogle Scholar
  18. 18.
    Alarcao, Soraia M., Fonseca, Manuel J.: Emotions recognition using EEG signals: a survey. IEEE Trans. Affect. Comput. PP(99), 1–20 (2017)CrossRefGoogle Scholar
  19. 19.
    Trans Cranial Technologies. https://www.trans-cranial.com
  20. 20.
  21. 21.
    Polikar, R.: The wavelet tutorial. Rowan University, Glassboro, Camden, Stratford, New Jersey, U.S (1999)Google Scholar
  22. 22.
    Vialatte, F.B., Solé-Casals, J., Dauwels, J., Maurice, M., Cichocki, A.: Bump time frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals. BMC Neurosci. 10, 1186 (2009)CrossRefGoogle Scholar
  23. 23.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sali Issa
    • 1
    Email author
  • Qinmu Peng
    • 1
  • Xinge You
    • 1
  • Wahab Ali Shah
    • 1
  1. 1.Huazhong University of Science and TechnologyWuhanChina

Personalised recommendations