Using Physiological Signals to Evolve Art

  • Tristan Basa
  • Christian Anthony Go
  • Kil-Sang Yoo
  • Won-Hyung Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


Human subjectivity have always posed a problem when it comes to judging designs. The line that divides what is interesting or not is blurred by the different interpretations as varied as the individuals themselves. Some approaches have made use of novelty in determining interestingness. However, computational measures of novelty such as the Euclidean distance are mere approximations to what the human brain finds interesting. In this paper, we explore the possibility of determining interestingness in a more direct method by using learning techniques such as Support Vector Machines to identify emotions from physiological signals, and then use genetic algorithms to evolve artworks that resulted in positive emotional signals.


Genetic Algorithm Support Vector Machine Basic Emotion Human Preference Facial Action Code System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Saunders, R.: Curious Design Agents and Artificial Creativity. In: Proceedings of the 4th conference on Creativity & cognition, Loughborough, UK, pp. 80–87 (2002)Google Scholar
  2. 2.
    Collete, C., Vernet-Maury, E., Delhomme, G., Dittmar, A.: Autonomic Nervous System Response Patterns Specificity to Basic Emotions. Journal of the Autonomic Nervous System 62, 45–57 (1997)CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Mitchell, T.: Machine Learning. McGraw-Hill Companies Inc., Singapore (1997)MATHGoogle Scholar
  5. 5.
    Sims, K.: Artificial Evolution for Computer Graphics. Computer Graphics (Siggraph 1991 proceedings) 25(4), 319–328 (1991)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Christianini, N., Taylor, J.S.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, UK (2000)Google Scholar
  7. 7.
    Ekman, P., Friesen, W.V.: The Facial Action Coding System. Consulting Psychologists Press, Paolo Alto (1978)Google Scholar
  8. 8.
    Levenson, R.W., Ekman, P., Friesen, W.V.: Voluntary facial action generates emotions specific autonomous nervous system activity. Psychophysiol 21 (1990)Google Scholar
  9. 9.
    Hubert, W., De Jong Meyer, R.: Psychophysiological response patterns to positive and negative film stimuli. Biol. Psychol., 73–93 (1990)Google Scholar
  10. 10.
    Hinrich, H., Machleidt, W.: Basic Emotions Reflected in EEG-coherences. International Journal of Phsychophysiology 13, 225–232 (1992)CrossRefGoogle Scholar
  11. 11.
    Fridlung, A.J., Schwartz, G.E., Fowler, S.C.: Pattern Recognition of Self- Reported Emotional state from multiple-site facial EMG activity during affective imagery. Psyhophysiol. 21 (1984)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tristan Basa
    • 1
  • Christian Anthony Go
    • 1
  • Kil-Sang Yoo
    • 1
  • Won-Hyung Lee
    • 1
  1. 1.Graduate School of Advanced Imaging Science, Multimedia and Film, Department of Image EngineeringChung-Ang UniversitySeoulKorea

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