Fractal-Based Brain State Recognition from EEG in Human Computer Interaction

  • Olga Sourina
  • Qiang Wang
  • Yisi Liu
  • Minh Khoa Nguyen
Part of the Communications in Computer and Information Science book series (CCIS, volume 273)


Real-time brain states recognition from Electroencephalogram (EEG) could add a new dimension in an immersive human-computer interaction. As EEG signal is considered to have a fractal nature, we proposed and developed a general fractal based spatio-temporal approach to brain states recognition including the concentration level, stress level, and emotion recognition. Our hypothesis is that changes of fractal dimension values of EEG over time correspond to the brain states changes. Overall brain state recognition algorithms were proposed and described. Fractal dimension values were calculated by the implemented Higuchi and Box-counting methods. Real-time subject-dependent classification algorithms based on threshold FD values calculated during a short training session were proposed and implemented. Based on the proposed real-time algorithms, neurofeedback games for concentration and stress management training such as “Brain Chi”, “Dancing Robot”, “Escape”, and “Apples”, and emotion-enabled applications such as emotion-enabled avatar, music therapy, and emotion-based search were designed and implemented.


BCI Neurofeedback Emotion recognition Fractal dimension EEG Real-time applications 


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  1. 1.
    Nunez, P.L., Srinivasan, R.: Electric Fields of the Brain. Oxford University Press (2006)Google Scholar
  2. 2.
    Fuchs, T., Birbaumer, N., Lutzenberger, W., Gruzelier, J.H., Kaiser, J.: Neurofeedback treatment for attention-deficit/hyperactivity disorder in children: A comparison with methylphenidate. Applied Psychophysiology Biofeedback 28, 1–12 (2003)CrossRefGoogle Scholar
  3. 3.
    Gevensleben, H., Holl, B., Albrecht, B., Schlamp, D., Kratz, O., Studer, P., Wangler, S., Rothenberger, A., Moll, G.H., Heinrich, H.: Distinct eeg effects related to neurofeedback training in children with adhd: A randomized controlled trial. International Journal of Psychophysiology 74, 149–157 (2009)CrossRefGoogle Scholar
  4. 4.
    Thompson, L., Thompson, M., Reid, A.: Neurofeedback outcomes in clients with asperger’s syndrome. Applied Psychophysiology Biofeedback 35, 63–81 (2010)CrossRefGoogle Scholar
  5. 5.
    Kouijzer, M.E.J., van Schie, H.T., de Moor, J.M.H., Gerrits, B.J.L., Buitelaar, J.K.: Neurofeedback treatment in autism. preliminary findings in behavioral, cognitive, and neurophysiological functioning. Research in Autism Spectrum Disorders 4, 386–399 (2010)CrossRefGoogle Scholar
  6. 6.
    Saxby, E., Peniston, E.G.: Alpha-theta brainwave neurofeedback training: An effective treatment for male and female alcoholics with depressive symptoms. Journal of Clinical Psychology 51, 685–693 (1995)CrossRefGoogle Scholar
  7. 7.
    Vernon, D., Egner, T., Cooper, N., Compton, T., Neilands, C., Sheri, A., Gruzelier, J.: The effect of training distinct neurofeedback protocols on aspects of cognitive performance. International Journal of Psychophysiology 47, 75–85 (2003)CrossRefGoogle Scholar
  8. 8.
    Hanslmayr, S., Sauseng, P., Doppelmayr, M., Schabus, M., Klimesch, W.: Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects. Applied Psychophysiology Biofeedback 30, 1–10 (2005)CrossRefGoogle Scholar
  9. 9.
    Heinrich, H., Gevensleben, H., Strehl, U.: Annotation: Neurofeedback - train your brain to train behaviour. Journal of Child Psychology and Psychiatry and Allied Disciplines 48, 3–16 (2007)CrossRefGoogle Scholar
  10. 10.
    Davidson, P.R., Jones, R.D., Peiris, M.T.R.: Eeg-based lapse detection with high temporal resolution. IEEE Transactions on Biomedical Engineering 54, 832–839 (2007)CrossRefGoogle Scholar
  11. 11.
    Lin, C.T., Wu, R.C., Jung, T.P., Liang, S.F., Huang, T.Y.: Estimating driving performance based on eeg spectrum analysis. Eurasip Journal on Applied Signal Processing, 3165–3174 (2005)Google Scholar
  12. 12.
    Huang, R.S., Jung, T.P., Makeig, S.: Multi-scale eeg brain dynamics during sustained attention tasks. In: Proc. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, pp. IV1173–IV1176 (2007)Google Scholar
  13. 13.
    Lutsyuk, N.V., Eismont, E.V., Pavlenko, V.B.: Modulation of attention in healthy children using a course of eeg-feedback sessions. Neurophysiology 38, 389–395 (2006)CrossRefGoogle Scholar
  14. 14.
    Pop-Jordanov, J., Pop-Jordanova, N.: Neurophysical substrates of arousal and attention. Cognitive Processing 10, 71–79 (2009)CrossRefGoogle Scholar
  15. 15.
    Schier, M.A.: Changes in eeg alpha power during simulated driving: A demonstration. International Journal of Psychophysiology 37, 155–162 (2000)CrossRefGoogle Scholar
  16. 16.
    Wang, Q., Sourina, O., Nguyen, M.K.: Fractal dimension based algorithm for neurofeedback games. In: Proc. CGI 2010, p. SP25 (2010)Google Scholar
  17. 17.
    Wang, Q., Sourina, O., Nguyen, M.K.: EEG-based ”serious” games design for medical applications. In: Proc. 2010 Int. Conf. on Cyberworlds, Singapore, pp. 270–276 (2010)Google Scholar
  18. 18.
    Block, A., Von Bloh, W., Schellnhuber, H.J.: Efficient box-counting determination of generalized fractal dimensions. Physical Review A 42, 1869–1874 (1990)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Higuchi, T.: Approach to an irregular time series on the basis of the fractal theory. Physica D: Nonlinear Phenomena 31, 277–283 (1988)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Ishino, K., Hagiwara, M.: A feeling estimation system using a simple electroencephalograph. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 5, pp. 4204–4209 (2003)Google Scholar
  21. 21.
    Petrantonakis, P.C., Hadjileontiadis, L.J.: Emotion recognition from EEG using higher order crossings. IEEE Transactions on Information Technology in Biomedicine 14, 186–197 (2010)CrossRefGoogle Scholar
  22. 22.
    Schaaff, K.: EEG-based emotion recognition. PhD thesis, Universitat Karlsruhe, TH (2008)Google Scholar
  23. 23.
    Takahashi, K.: Remarks on emotion recognition from multi-modal bio-potential signals. In: IEEE International Conference on Industrial Technology, IEEE ICIT 2004, vol. 3, pp. 1138–1143 (2004)Google Scholar
  24. 24.
    Zhang, Q., Lee, M.: Analysis of positive and negative emotions in natural scene using brain activity and gist. Neurocomputing 72, 1302–1306 (2009)CrossRefGoogle Scholar
  25. 25.
    Chanel, G., Kronegg, J., Grandjean, D., Pun, T.: Emotion assessment: Arousal evaluation using EEG’s and peripheral physiological signals (2006)Google Scholar
  26. 26.
    Chanel, G., Kierkels, J.J.M., Soleymani, M., Pun, T.: Short-term emotion assessment in a recall paradigm. International Journal of Human Computer Studies 67, 607–627 (2009)CrossRefGoogle Scholar
  27. 27.
    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: Proceedings of the ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, pp. 489–492 (2009)Google Scholar
  28. 28.
    Schaaff, K., Schultz, T.: Towards emotion recognition from electroencephalographic signals. In: 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009, pp. 1–6 (2009)Google Scholar
  29. 29.
    Khalili, Z., Moradi, M.H.: Emotion recognition system using brain and peripheral signals: Using correlation dimension to improve the results of eeg. In: Proceedings of the International Joint Conference on Neural Networks, pp. 1571–1575 (2009)Google Scholar
  30. 30.
    Liu, Y., Sourina, O., Nguyen, M.K.: Real-time EEG-based human emotion recognition and visualization. In: Proc. 2010 Int. Conf. on Cyberworlds, Singapore, pp. 262–269 (2010)Google Scholar
  31. 31.
    Blinn, J.F.: A generalization of algebraic surface drawing. SIGGRAPH Comput. Graph. 16, 273 (1982)CrossRefGoogle Scholar
  32. 32.
    Wyvill, G., McPheeters, C., Wyvill, B.: Data structure for soft objects. The Visual Computer 2, 227–234 (1986)CrossRefGoogle Scholar
  33. 33.
    Pasko, A., Adzhiev, V., Sourin, A., Savchenko, V.: Function representation in geometric modeling: concepts, implementation and applications. The Visual Computer 11 (1995)Google Scholar
  34. 34.
    Kulish, V., Sourin, A., Sourina, O.: Analysis and visualization of human electroencephalograms seen as fractal time series. Journal of Mechanics in Medicine and Biology 26, 175–188 (2006)CrossRefGoogle Scholar
  35. 35.
    Sourina, O., Sourin, A., Kulish, V.: EEG Data Driven Animation and Its Application. In: Gagalowicz, A., Philips, W. (eds.) MIRAGE 2009. LNCS, vol. 5496, pp. 380–388. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  36. 36.
    Hentschel, H.G.E., Procaccia, I.: The infinite number of generalized dimensions of fractals and strange attractors. Physica D: Nonlinear Phenomena 8, 435–444 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  37. 37.
    Renyi, A.: On a new axiomatic theory of probability. Acta Mathematica Academiae Scientiarum Hungaricae 6, 285–335 (1955)MathSciNetzbMATHCrossRefGoogle Scholar
  38. 38.
    Shannon, C.: The mathematical theory of communication. M.D. Computing 14 (1997)Google Scholar
  39. 39.
    Kulish, V., Sourin, A., Sourina, O.: Human electroencephalograms seen as fractal time series: Mathematical analysis and visualization. Computers in Biology and Medicine 36, 291–302 (2006)CrossRefGoogle Scholar
  40. 40.
    Pawelzik, K., Schuster, H.G.: Generalized dimensions and entropies from a measured time series. Physical Review A 35, 481–484 (1987)CrossRefGoogle Scholar
  41. 41.
    Phothisonothai, M., Nakagawa, M.: Fractal-based eeg data analysis of body parts movement imagery tasks. Journal of Physiological Sciences 57, 217–226 (2007)CrossRefGoogle Scholar
  42. 42.
    Sanei, S., Chambers, J.: EEG signal processing. John Wiley & Sons, Chichester (2007)Google Scholar
  43. 43.
    Wang, Q., Sourina, O., Nguyen, M.K.: Fractal dimension based neurofeedback. Visual Computer 27, 299–309 (2011)CrossRefGoogle Scholar
  44. 44.
    AES: American electroencephalographic society guidelines for standard electrode position nomenclature. Journal of Clinical Neurophysiology 8, 200–202 (1991)Google Scholar
  45. 45.
    Sourina, O., Kulish, V.V., Sourin, A.: Novel tools for quantification of brain responses to music stimuli. In: Proc. of 13th International Conference on Biomedical Engineering, ICBME 2008 (2008)Google Scholar
  46. 46.
    Haptek: Haptek (2010)Google Scholar
  47. 47.
    IDM-Project: Emotion-based personalized digital media experience in co-spaces (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Olga Sourina
    • 1
  • Qiang Wang
    • 1
  • Yisi Liu
    • 1
  • Minh Khoa Nguyen
    • 1
  1. 1.Nanyang Technological UniversitySingapore

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