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Real-Time Fractal-Based Valence Level Recognition from EEG

  • Conference paper
Transactions on Computational Science XVIII

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 7848))

Abstract

Emotions are important in human-computer interaction. Emotions could be classified based on 3-dimensional Valence-Arousal-Dominance model which allows defining any number of emotions even without discrete emotion labels. In this paper, we propose a real-time fractal dimension (FD) based valence level recognition algorithm from Electroencephalographic (EEG) signals. The FD-based feature is proposed as a valence dimension index in continuous emotion recognition. The thresholds are used to identify different levels of the valence dimension. The algorithm is tested on the EEG data labeled with different valence levels from the proposed and implemented experiment database and from the benchmark affective EEG database DEAP. The proposed algorithm is applied for recognition of 16 emotions defined by high/low arousal, high/low dominance and 4 levels of valence dimension. 9 levels of valence states with controlled dominance levels (high or low) can be recognized as well. The proposed algorithm can be implemented in different real-time applications such as emotional avatar and E-learning systems.

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References

  1. Emotiv, http://www.emotiv.com

  2. Haptek, http://www.haptek.com

  3. American electroencephalographic society guidelines for standard electrode position nomenclature. Journal of Clinical Neurophysiology 8(2), 200–202 (1991)

    Google Scholar 

  4. Biosemi, http://www.biosemi.com

  5. Bradley, M.M.: Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry 25(1), 49–59 (1994)

    Article  Google Scholar 

  6. Bradley, M.M., Lang, P.J.: The international affective digitized sounds (2nd edition; IADS-2): Affective ratings of sounds and instruction manual. Tech. Rep., University of Florida, Gainesville (2007)

    Google Scholar 

  7. Canli, T., Desmond, J.E., Zhao, Z., Glover, G., Gabrieli, J.D.E.: Hemispheric asymmetry for emotional stimuli detected with fMRI. NeuroReport 9(14), 3233–3239 (1998)

    Article  Google Scholar 

  8. Chanel, G., Rebetez, C., Betrancourt, M., Pun, T.: Emotion assessment from physiological signals for adaptation of game difficulty. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 41(6), 1052–1063 (2011)

    Article  Google Scholar 

  9. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm

  10. Hamann, S., Canli, T.: Individual differences in emotion processing. Current Opinion in Neurobiology 14(2), 233–238 (2004)

    Article  Google Scholar 

  11. Higuchi, T.: Approach to an irregular time series on the basis of the fractal theory. Physica D: Nonlinear Phenomena 31(2), 277–283 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  12. Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classication. Tech. Rep., National Taiwan University, Taipei (2003), http://www.csie.ntu.edu.tw/cjlin/libsvm

  13. IDM-Project: Emotion-based personalized digital media experience in co-spaces (2008), http://www3.ntu.edu.sg/home/eosourina/CHCILab/projects.html

  14. Jones, N.A., Fox, N.A.: Electroencephalogram asymmetry during emotionally evocative films and its relation to positive and negative affectivity. Brain and Cognition 20(2), 280–299 (1992)

    Article  Google Scholar 

  15. Koelstra, S., Yazdani, A., Soleymani, M., Mhl, C., Lee, J.S., Nijholt, A., Pun, T., Ebrahimi, T., Patras, I.: Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos (2010)

    Google Scholar 

  16. 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 Transactions on Affective Computing 3(1), 18–31 (2012)

    Article  Google Scholar 

  17. Koelstra, S., Muhl, C., Soleymani, M., Lee, J.S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: DEAP dataset (2012), http://www.eecs.qmul.ac.uk/mmv/datasets/deap

  18. Lin, Y.P., Wang, C.H., Wu, T.L., Jeng, S.K., Chen, J.H.: EEG-based emotion recognition in music listening: A comparison of schemes for multiclass support vector machine. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Taipei, pp. 489–492 (2009)

    Google Scholar 

  19. Liu, Y., Sourina, O., Nguyen, M.K.: Real-time EEG-based emotion recognition and its applications. In: Gavrilova, M.L., Tan, C.J.K., Sourin, A., Sourina, O. (eds.) Transactions on Computational Science XII. LNCS, vol. 6670, pp. 256–277. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  20. Liu, Y., Sourina, O.: EEG-based dominance level recognition for emotion-enabled interaction. In: The IEEE International Conference on Multimedia & Expo, ICME, pp. 1039–1044 (2012)

    Google Scholar 

  21. Liu, Y., Sourina, O.: EEG-based valence level recognition for real-time applications. In: Proc. 2012 Int. Conf. on Cyberworlds, Germany, pp. 53–60 (2012)

    Google Scholar 

  22. Liu, Y., Sourina, O., Nguyen, M.K.: Real-time EEG-based emotion recognition algorithm for adaptive games. IEEE Transactions on Computational Intelligence and AI in Games (2012) (under revision)

    Google Scholar 

  23. Mauss, I.B., Robinson, M.D.: Measures of emotion: A review. Cognition and Emotion 23(2), 209–237 (2009)

    Article  Google Scholar 

  24. Mehrabian, A.: Framework for a comprehensive description and measurement of emotional states. Genetic, Social, and General Psychology Monographs 121(3), 339–361 (1995)

    Google Scholar 

  25. Mehrabian, A.: Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament. Current Psychology 14(4), 261–292 (1996)

    Article  MathSciNet  Google Scholar 

  26. O’Regan, S., Faul, S., Marnane, W.: Automatic detection of EEG artefacts arising from head movements. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 6353–6356 (2010)

    Google Scholar 

  27. Petrantonakis, P.C., Hadjileontiadis, L.J.: Emotion recognition from EEG using higher order crossings. IEEE Transactions on Information Technology in Biomedicine 14(2), 186–197 (2010)

    Article  Google Scholar 

  28. Petrantonakis, P.C., Hadjileontiadis, L.J.: Adaptive emotional information retrieval from EEG signals in the time-frequency domain. IEEE Transactions on Signal Processing 60(5), 2604–2616 (2012)

    Article  MathSciNet  Google Scholar 

  29. Picard, R.W., Vyzas, E., Healey, J.: Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(10), 1175–1191 (2001)

    Article  Google Scholar 

  30. Ranky, G.N., Adamovich, S.: Analysis of a commercial EEG device for the control of a robot arm. In: Proceedings of the 2010 IEEE 36th Annual Northeast Bioengineering Conference, pp. 1–2 (2010)

    Google Scholar 

  31. Russell, J.A., Mehrabian, A.: Evidence for a three-factor theory of emotions. Journal of Research in Personality 11(3), 273–294 (1977)

    Article  Google Scholar 

  32. Soleymani, M., Lichtenauer, J., Pun, T., Pantic, M.: A multimodal database for affect recognition and implicit tagging. IEEE Transactions on Affective Computing 3(1), 42–55 (2012)

    Article  Google Scholar 

  33. Stytsenko, K., Jablonskis, E., Prahm, C.: Evaluation of consumer EEG device emotiv epoc. Poster Session Presented at MEi: Cog. Sci. Conference 2011, Ljubljana (2011)

    Google Scholar 

  34. Takahashi, K.: Remarks on emotion recognition from multi-modal bio-potential signals. In: 2004 IEEE International Conference on Industrial Technology, vol. 3, pp. 1138–1143 (2004)

    Google Scholar 

  35. Wang, Q., Sourina, O., Nguyen, M.K.: Fractal dimension based algorithm for neurofeedback games. In: Proc. CGI 2010, Singapore, p. SP25 (2010)

    Google Scholar 

  36. Wang, Q., Sourina, O., Nguyen, M.: Fractal dimension based neurofeedback in serious games. The Visual Computer 27(4), 299–309 (2011)

    Article  Google Scholar 

  37. Zhang, Q., Lee, M.: Analysis of positive and negative emotions in natural scene using brain activity and gist. Neurocomputing 72(4-6), 1302–1306 (2009)

    Article  Google Scholar 

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Authors and Affiliations

  1. Nanyang Technological University, Singapore

    Yisi Liu & Olga Sourina

Authors
  1. Yisi Liu
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  2. Olga Sourina
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Editors and Affiliations

  1. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  2. Exascala Ltd., Unit 9, 97 Rickman Drive, B15 2AL, Birmingham, UK

    C. J. Kenneth Tan

  3. Institut für Graphische Datenverarbeitung, Fraunhofer Gesellschaft, Fraunhoferstraße 5, 64283, Darmstadt, Germany

    Arjan Kuijper

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Liu, Y., Sourina, O. (2013). Real-Time Fractal-Based Valence Level Recognition from EEG. In: Gavrilova, M.L., Tan, C.J.K., Kuijper, A. (eds) Transactions on Computational Science XVIII. Lecture Notes in Computer Science, vol 7848. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38803-3_6

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  • DOI: https://doi.org/10.1007/978-3-642-38803-3_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38802-6

  • Online ISBN: 978-3-642-38803-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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