Classification of bluffing behavior and affective attitude from prefrontal surface encephalogram during on-line game

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. © Springer-Verlag Berlin Heidelberg 2005.