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
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


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.


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.


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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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