Advertisement

Emotion Recognition Using the Emotiv EPOC Device

  • Trung Duy Pham
  • Dat Tran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7667)

Abstract

Emotion plays an important role in the interaction between humans as emotion is fundamental to human experience, influencing cognition, perception, learning communication, and even rational decision-making. Therefore, studying emotion is indispensable. This paper aims at finding the relationships between EEG signals and human emotions based on emotion recognition experiments that are conducted using the commercial Emotiv EPOC headset to record EEG signals while participants are watching emotional movies. Alpha, beta, delta and theta bands filtered from the recorded EEG signals are used to train and evaluate classifiers with different learning techniques including Support Vector Machine, k-Nearest Neighbour, Naïve Bayes and AdaBoost.M1. Our experimental results show that we can use the Emotiv headset for emotion recognition and that the AdaBoost.M1 technique and the theta band provide the highest recognition rates.

Keywords

EEG Emotion recognition Emotiv EPOC headset AdaBoost.M1 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anderson, K., McOwan, W.P.: A real-time automated system for the recognition of human facial expressions. IEEE Trans. System, Man, and Cybernetics, Part B: Cybernetics 36(1), 96–105 (2006)CrossRefGoogle Scholar
  2. 2.
    Anttonen, J., Surakka, V.: Emotions and Heart Rate while Sitting on a Chair. In: CHI 2005: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 491–499 (2005)Google Scholar
  3. 3.
    Leng, H., Lin, Y., Zanzi, L.A.: An Experimental Study on Physiological Parameters Toward Driver Emotion Recognition. In: Dainoff, M.J. (ed.) HCII 2007 and EHAWC 2007. LNCS, vol. 4566, pp. 237–246. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Petrantonakis, C.P., Hadjileontiadis, J.L.: Emotion Recognition From EEG Using Higher Order Crossings. IEEE Transactions on Information Technology in Biomedicine 14(2), 186–197 (2010)CrossRefGoogle Scholar
  6. 6.
    Lin, Y.: EEG-Based emotion recognition in music listening. IEEE Transactions 57(7), 1798–1806 (2010)Google Scholar
  7. 7.
    Murugappan, M.: Human emotion classification using wavelet transform and KNN. In: International Conference on Pattern Analysis and Intelligent Robotics (ICPAIR), vol. 1, pp. 148–153 (2011)Google Scholar
  8. 8.
    Lotte, F., Congedo, M., Lécuyer, A.: F. Lamarche and B. Arnaldi, A review of classification algorithms for EEG-based brain–computer interfaces. Journal of Neural Engineering (2007)Google Scholar
  9. 9.
    Emotiv EPOC headset, http://www.emotiv.com/
  10. 10.
    Experiment Wizard software tool, http://code.google.com/p/experiment-wizard/
  11. 11.
  12. 12.
    Li, M., Lu, B.L.: Emotion classification based on gamma-band EEG. In: IEEE International Conference Engineering in Medicine and Biology Society, Minneapolis, pp. 1223–1226 (2009)Google Scholar
  13. 13.
    Nie, D., Xiao, W.W., Li, C.S., Baoliang, L.: EEG-based emotion recognition during watching movies. In: 5th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 667–670 (2011)Google Scholar
  14. 14.
  15. 15.
    Zeng, Z., Pantic, M., Roisman, I.G., Huang, S.T.: A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans. Pattern Analysis and Machine Intelligence 31(1), 39–58 (2009)CrossRefGoogle Scholar
  16. 16.
    Westermann, R., Spies, K., Stahl, G., Hesse, F.W.: Relative effectiveness and validity of mood induction procedures: A meta-analysis. European Journal of Social Psychology 26, 557–580 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Trung Duy Pham
    • 1
  • Dat Tran
    • 1
  1. 1.Faculty of Information Sciences and EngineeringUniversity of CanberraAustralia

Personalised recommendations