Automatic Recognition of the Unconscious Reactions from Physiological Signals

  • Leonid Ivonin
  • Huang-Ming Chang
  • Wei Chen
  • Matthias Rauterberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7946)


While the research in affective computing has been exclusively dealing with the recognition of explicit affective and cognitive states, carefully designed psychological and neuroimaging studies indicated that a considerable part of human experiences is tied to a deeper level of a psyche and not available for conscious awareness. Nevertheless, the unconscious processes of the mind greatly influence individuals’ feelings and shape their behaviors. This paper presents an approach for automatic recognition of the unconscious experiences from physiological data. In our study we focused on primary or archetypal unconscious experiences. The subjects were stimulated with the film clips corresponding to 8 archetypal experiences. Their physiological signals including cardiovascular, electrodermal, respiratory activities, and skin temperature were monitored. The statistical analysis indicated that the induced experiences could be differentiated based on the physiological activations. Finally, a prediction model, which recognized the induced states with an accuracy of 79.5%, was constructed.


Affective computing archetypes the collective unconscious 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Picard, R.W.: Affective computing, MIT Media Laboratory Perceptual Computing Section Technical Report No. 321 (1995)Google Scholar
  2. 2.
    Fairclough, S.H.: Fundamentals of physiological computing. Interacting with Computers 21, 133–145 (2009)CrossRefGoogle Scholar
  3. 3.
    Novak, D., Mihelj, M., Munih, M.: A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing. Interacting with Computers 24, 154–172 (2012)CrossRefGoogle Scholar
  4. 4.
    Zhou, F., Qu, X., Helander, M.G., Jiao, J.(R.): Affect prediction from physiological measures via visual stimuli. International Journal of Human-Computer Studies 69, 801–819 (2011)Google Scholar
  5. 5.
    Wu, D., Courtney, C.G., Lance, B.J., Narayanan, S.S., Dawson, M.E., Oie, K.S., Parsons, T.D.: Optimal arousal identification and classification for affective computing using physiological signals: virtual reality stroop task. IEEE Transactions on Affective Computing 1, 109–118 (2010)CrossRefGoogle Scholar
  6. 6.
    Stickel, C., Ebner, M., Steinbach-Nordmann, S., Searle, G., Holzinger, A.: Emotion detection: application of the valence arousal space for rapid biological usability testing to enhance universal access. In: Stephanidis, C. (ed.) Universal Access in HCI, Part I, HCII 2009. LNCS, vol. 5614, pp. 615–624. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Nisbett, R.E., Wilson, T.D.: Telling more than we can know: verbal reports on mental processes. Psychological Review 84, 231–259 (1977)CrossRefGoogle Scholar
  8. 8.
    Van Gaal, S., Lamme, V.A.F.: Unconscious high-level information processing: implication for neurobiological theories of consciousness. The Neuroscientist 18, 287–301 (2012)CrossRefGoogle Scholar
  9. 9.
    Bargh, J.A., Morsella, E.: The unconscious mind. Perspectives on Psychological Science 3, 73–79 (2008)CrossRefGoogle Scholar
  10. 10.
    Rauterberg, M.: Emotions: The voice of the unconscious. In: Yang, H.S., Malaka, R., Hoshino, J., Han, J.H. (eds.) ICEC 2010. LNCS, vol. 6243, pp. 205–215. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Sally, W.: Algorithms and archetypes: evolutionary psychology and Carl Jung’s theory of the collective unconscious. Journal of Social and Evolutionary Systems 17, 287–306 (1994)CrossRefGoogle Scholar
  12. 12.
    Jung, C.G.: The archetypes and the collective unconscious. Princeton University Press, Princeton (1981)Google Scholar
  13. 13.
    Jung, C.G.: Man and his symbols. Doubleday, Garden City (1964)Google Scholar
  14. 14.
    Miller, N.E.: Some examples of psychophysiology and the unconscious. Applied Psychophysiology and Biofeedback 17, 3–16 (1992)Google Scholar
  15. 15.
    Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): affective ratings of pictures and instruction manual. Technical Report A-8, Gainesville, FL, USA (2008)Google Scholar
  16. 16.
    Bradley, M.M., Lang, P.J.: International affective digitized sounds (IADS): stimuli, instruction manual and affective ratings (Tech. Rep. No. B-2), Gainesville, FL, USA (1999)Google Scholar
  17. 17.
    Eich, E., Ng, J.T.W., Macaulay, D., Percy, A.D., Grebneva, I.: Combining music with thought to change mood. In: Coan, J.A., Allen, J.J.B. (eds.) The Handbook of Emotion Elicitation and Assessment, pp. 124–136. Oxford University Press, New York (2007)Google Scholar
  18. 18.
    Gross, J.J., Levenson, R.W.: Emotion elicitation using films. Cognition & Emotion 9, 87–108 (1995)CrossRefGoogle Scholar
  19. 19.
    Rottenberg, J., Ray, R.D., Gross, J.J.: Emotion elicitation using films. In: Coan, J.A., Allen, J.J.B. (eds.) Handbook of Emotion Elicitation and Assessment, pp. 9–28. Oxford University Press, New York (2007)Google Scholar
  20. 20.
    Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology: Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 93, 1043–1065 (1996)Google Scholar
  21. 21.
    Russell, J.A.: A circumplex model of affect. Journal of Personality and Social Psychology 39, 1161–1178 (1980)CrossRefGoogle Scholar
  22. 22.
    Ivonin, L., Chang, H.-M., Chen, W., Rauterberg, M.: A new representation of emotion in affective computing. In: Proceeding of 2012 International Conference on Affective Computing and Intelligent Interaction (ICACII 2012), Taipei, Taiwan. Lecture Notes in Information Technology, pp. 337–343 (2012)Google Scholar
  23. 23.
    Lang, P.J., Greenwald, M.K., Bradley, M.M., Hamm, A.O.: Looking at pictures: affective, facial, visceral, and behavioral reactions. Psychophysiology 30, 261–273 (1993)CrossRefGoogle Scholar
  24. 24.
    Soleymani, M., Pantic, M., Pun, T.: Multimodal emotion recognition in response to videos. IEEE Transactions on Affective Computing 3, 211–223 (2011)CrossRefGoogle Scholar
  25. 25.
    Faber, M.A., Mayer, J.D.: Resonance to archetypes in media: there is some accounting for taste. Journal of Research in Personality 43, 307–322 (2009)CrossRefGoogle Scholar
  26. 26.
    Hannan, D.: Coral sea dreaming: awaken. Roadshow Entertainment (2010)Google Scholar
  27. 27.
    Demme, J.: The silence of the lambs. Orion Pictures (1991)Google Scholar
  28. 28.
    Atkinson, R., Curtis, R.: Mr. Bean (season 1, episode 1). Pearson Television International (1990)Google Scholar
  29. 29.
    Allers, R., Minkoff, R.: The Lion King. Walt Disney Pictures (1994)Google Scholar
  30. 30.
    Zemeckis, R.: Forrest Gump. Paramount Pictures (1994)Google Scholar
  31. 31.
    Mendes, S.: American beauty. DreamWorks Pictures (1999)Google Scholar
  32. 32.
    Gibson, M.: Braveheart. 20th Century Fox (1995)Google Scholar
  33. 33.
    Aronofsky, D.: Black swan. Fox Searchlight Pictures (2010)Google Scholar
  34. 34.
    Hooper, T.: The king’s speech. The Weinstein Company (2010)Google Scholar
  35. 35.
    Almodóvar, P.: All about my mother. Warner Sogefilms (1999)Google Scholar
  36. 36.
    Fincher, D.: Fight club. 20th Century Fox (1999)Google Scholar
  37. 37.
    Campbell, J.: The hero with a thousand faces. New World Library, Novato (2008)Google Scholar
  38. 38.
    Maloney, A.: Preference ratings of images representing archetypal themes: an empirical study of the concept of archetypes. Journal of Analytical Psychology 44, 101–116 (2002)CrossRefGoogle Scholar
  39. 39.
    Gronning, T., Sohl, P., Singer, T.: ARAS: archetypal symbolism and images. Visual Resources 23, 245–267 (2007)CrossRefGoogle Scholar
  40. 40.
    Figner, B., Murphy, R.O.: Using skin conductance in judgment and decision making research. In: Schulte-Mecklenbeck, M., Kuehberger, A., Ranyard, R. (eds.) A Handbook of Process Tracking Methods for Decision Research, pp. 163–184. Psychology Press, New York (2011)Google Scholar
  41. 41.
    Piferi, R.L., Kline, K.A., Younger, J., Lawler, K.A.: An alternative approach for achieving cardiovascular baseline: viewing an aquatic video. International Journal of Psychophysiology 37, 207–217 (2000)CrossRefGoogle Scholar
  42. 42.
    Neuman, M.R.: Vital signs: heart rate. IEEE Pulse 1, 51–55 (2010)MathSciNetCrossRefGoogle Scholar
  43. 43.
    Afonso, V.X., Tompkins, W.J., Nguyen, T.Q.: ECG beat detection using filter banks. IEEE Transactions on Biomedical Engineering 46, 192–202 (1999)CrossRefGoogle Scholar
  44. 44.
    Kreibig, S.D.: Autonomic nervous system activity in emotion: a review. Biological Psychology 84, 394–421 (2010)CrossRefGoogle Scholar
  45. 45.
    Ramshur, J.T.: Design, evaluation, and application of heart rate variability software (HRVAS). Master’s thesis, The University of Memphis, Memphis, TN (2010)Google Scholar
  46. 46.
    Fairclough, S.H., Venables, L.: Prediction of subjective states from psychophysiology: a multivariate approach. Biological Psychology 71, 100–110 (2006)CrossRefGoogle Scholar
  47. 47.
    Boiten, F.A.: The effects of emotional behaviour on components of the respiratory cycle. Biological Psychology 49, 29–51 (1998)CrossRefGoogle Scholar
  48. 48.
    Kim, K.H., Bang, S.W., Kim, S.R.: Emotion recognition system using short-term monitoring of physiological signals. Medical & Biological Engineering & Computing 42, 419–427 (2004)CrossRefGoogle Scholar
  49. 49.
    Ekman, P., Levenson, R., Friesen, W.: Autonomic nervous system activity distinguishes among emotions. Science 221, 1208–1210 (1983)CrossRefGoogle Scholar
  50. 50.
    O’Brien, R.G., Kaiser, M.K.: MANOVA method for analyzing repeated measures designs: an extensive primer. Psychological Bulletin 97, 316–333 (1985)CrossRefGoogle Scholar
  51. 51.
    West, B.T., Welch, K.B., Galecki, A.T.: Linear mixed models: a practical guide using statistical software. Chapman and Hall/CRC, Boca Raton (2006)Google Scholar
  52. 52.
    Cnaan, A., Laird, N.M., Slasor, P.: Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Statistics in Medicine 16, 2349–2380 (1997)CrossRefGoogle Scholar
  53. 53.
    Xing, Z., Pei, J., Keogh, E.: A brief survey on sequence classification. ACM SIGKDD Explorations Newsletter 12, 40 (2010)CrossRefGoogle Scholar
  54. 54.
    Holzinger, A., Stocker, C., Bruschi, M., Auinger, A., Silva, H., Gamboa, H., Fred, A.: On applying approximate entropy to ECG signals for knowledge discovery on the example of big sensor data. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds.) AMT 2012. LNCS, vol. 7669, pp. 646–657. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  55. 55.
    Kadous, M.W., Sammut, C.: Classification of multivariate time series and structured data using constructive induction. Machine Learning 58, 179–216 (2005)CrossRefGoogle Scholar
  56. 56.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55, 119–139 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  57. 57.
    Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  58. 58.
    Chan, F.K.: Haar wavelets for efficient similarity search of time-series: with and without time warping. IEEE Transactions on Knowledge and Data Engineering 15, 686–705 (2003)CrossRefGoogle Scholar
  59. 59.
    Geurts, P.: Pattern extraction for time series classification. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 115–127. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  60. 60.
    Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 228–233 (2001)CrossRefGoogle Scholar
  61. 61.
    Ivonin, L., Chang, H.-M., Chen, W., Rauterberg, M.: Unconscious emotions: quantifying and logging something we are not aware of. Personal and Ubiquitous Computing 17, 663–673 (2013)CrossRefGoogle Scholar
  62. 62.
    Healey, J.A., Picard, R.W.: Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems 6, 156–166 (2005)CrossRefGoogle Scholar
  63. 63.
    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, 1175–1191 (2001)CrossRefGoogle Scholar
  64. 64.
    Sakr, G.E., Elhajj, I.H., Huijer, H.A.-S.: Support vector machines to define and detect agitation transition. IEEE Transactions on Affective Computing 1, 98–108 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Leonid Ivonin
    • 1
  • Huang-Ming Chang
    • 1
  • Wei Chen
    • 1
  • Matthias Rauterberg
    • 1
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands

Personalised recommendations