Classification of Bluffing Behavior and Affective Attitude from Prefrontal Surface Encephalogram During On-Line Game

  • Myung Hwan Yun
  • Joo Hwan Lee
  • Hyoung-joo Lee
  • Sungzoon Cho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

The purpose of this research was to detect the pattern of player’s emotional change during on-line game. By defining data processing technique and analysis method for bio-physiological activity and player’s bluffing behavior, the classification of affective attitudes during on-line game was attempted. Bluffing behavior displayed during the game was classified into two dimensions of emotional axis based on prefrontal surface electroencephalographic data. Classified bluffing attitudes were: (1) pleasantness/unpleasantness; and (2) honesty/bluffing. A multilayer-perception neural network was used to classify the player state into four attitude categories. Resulting classifier showed moderate performance with 67.03% pleasantness/unpleasantness classification, and 77.51% for honesty/bluffing. The classifier model developed in this study was integrated to on-line game as a form of ‘emoticon’ which displays facial expression of opposing player’s emotional state.

Keywords

Linear Discriminant Analysis Affective Attitude Polygraph Test Short Training Time Control Question Test 
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.

References

  1. 1.
    Andreassi, J.L.: Psychophysiology: Human Behavior & Physiological Response, 3rd edn. Lawrence Erlbaum Associates, New Jersey (1995)Google Scholar
  2. 2.
    Bishop, C.M.: Neural Network for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
  3. 3.
    Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)MATHMathSciNetGoogle Scholar
  4. 4.
    Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  5. 5.
    Eoh, H.J., Chung, M.K., Kim, S.H.: Electroencephalographic study of drowsiness in simulated driving with sleep deprivation. International Journal of Industrial Ergonomics 35(4), 307–320 (2005)CrossRefGoogle Scholar
  6. 6.
    Gevins, A., Smith, M.E., Leong, H., Mcevoy, L., Whitfield, S., Du, R., Rush, G.: Monitoring working memory load during computer-based tasks with EEG pattern recognition methods. Human Factors 40(1), 79–91 (1998)CrossRefGoogle Scholar
  7. 7.
    Sheikh, H., McFarland, D.J., Sarnacki, W.A., Wolpaw, J.R.: Electroencephalographic (EEG)-based communication- EEG control versus system performance in humans. Neuroscience Letters 345(2), 89–92 (2003)CrossRefGoogle Scholar
  8. 8.
    Kimbrell, T.A., George, M.S., Parekh, P.I., Ketter, T.A., Podell, D.M., Danielson, A.L., Repella, J.D., Benson, B.E., Willis, M.W., Herscovitch, P., Post, R.M.: Regional brain activity during transient self-induced anxiety and anger in healthy adults. Biological Psychiatry 46(4), 454–465 (1999)CrossRefGoogle Scholar
  9. 9.
    Luan, K.: Neural correlates of telling lies: A functional magnetic resonance imaging study at 4 Tesla. Academic Radiology 12(2), 164–172 (2005)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Pfurtscheller, G., Miller, G.R., Guger, C.: Direct control of a robot by electrical signals from the brain. Proceeding EMBEC 1999, Part 2, 1354–1355 (1999)Google Scholar
  11. 11.
    Vance, V.: A quantitative review of the guilty knowledge test. Journal of applied psychology 86(4), 674–683 (2001)CrossRefGoogle Scholar
  12. 12.
    Waldstein, S.R., Kop, W.J., Schmidt, L.A.: Frontal electro cortical and cardiovascular reactivity during happiness and anger. Biological Psychology 55(1), 3–23 (2000)CrossRefGoogle Scholar
  13. 13.
    Wilson, G.F., Swain, C.R., Ullsperger, P.: EEG Power Changes during a Multiple Level Memory Retention Task. International Journal of Psychophysiology 32, 107–118 (1999)CrossRefGoogle Scholar
  14. 14.
    Ishiwaka, Y., Yokoi, H., Kakazu, Y.: EEG on-line analysis for autonomous adaptive interface. International Congress Series 1232, 271–275 (2002)CrossRefGoogle Scholar
  15. 15.
    Yun, M.H.: Development of an adaptive computer game interface based on bio-physiological signal processing technique, Ministry of Science and Technology, South Korea (2000) (unpublished research report, in Korean) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Myung Hwan Yun
    • 1
  • Joo Hwan Lee
    • 1
  • Hyoung-joo Lee
    • 1
  • Sungzoon Cho
    • 1
  1. 1.Department of Industrial EngineeringSeoul National UniversitySeoulSouth Korea

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