Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty
This paper proposes to maintain player's engagement by adapting game difficulty according to player's emotions assessed from physiological signals. The validity of this approach was first tested by analyzing the questionnaire responses, electroencephalogram (EEG) signals, and peripheral si...
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| Published in | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 41; no. 6; pp. 1052 - 1063 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.11.2011
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| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper proposes to maintain player's engagement by adapting game difficulty according to player's emotions assessed from physiological signals. The validity of this approach was first tested by analyzing the questionnaire responses, electroencephalogram (EEG) signals, and peripheral signals of the players playing a Tetris game at three difficulty levels. This analysis confirms that the different difficulty levels correspond to distinguishable emotions, and that, playing several times at the same difficulty level gives rise to boredom. The next step was to train several classifiers to automatically detect the three emotional classes from EEG and peripheral signals in a player-independent framework. By using either type of signals, the emotional classes were successfully recovered, with EEG having a better accuracy than peripheral signals on short periods of time. After the fusion of the two signal categories, the accuracy raised up to 63%. |
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| AbstractList | This paper proposes to maintain player's engagement by adapting game difficulty according to player's emotions assessed from physiological signals. The validity of this approach was first tested by analyzing the questionnaire responses, electroencephalogram (EEG) signals, and peripheral signals of the players playing a Tetris game at three difficulty levels. This analysis confirms that the different difficulty levels correspond to distinguishable emotions, and that, playing several times at the same difficulty level gives rise to boredom. The next step was to train several classifiers to automatically detect the three emotional classes from EEG and peripheral signals in a player-independent framework. By using either type of signals, the emotional classes were successfully recovered, with EEG having a better accuracy than peripheral signals on short periods of time. After the fusion of the two signal categories, the accuracy raised up to 63%. |
| Author | Chanel, G. Pun, T. Rebetez, C. Bétrancourt, M. |
| Author_xml | – sequence: 1 givenname: G. surname: Chanel fullname: Chanel, G. email: guillaume.chanel@unige.ch organization: Comput. Sci. Dept., Univ. of Geneva, Carouge, Switzerland – sequence: 2 givenname: C. surname: Rebetez fullname: Rebetez, C. email: cyril.rebetez@unige.ch organization: Technol. de Formation et Apprentissage Lab., Univ. of Geneva, Geneva, Switzerland – sequence: 3 givenname: M. surname: Bétrancourt fullname: Bétrancourt, M. email: mireille.betrancourt@unige.ch organization: Technol. de Formation et Apprentissage Lab., Univ. of Geneva, Geneva, Switzerland – sequence: 4 givenname: T. surname: Pun fullname: Pun, T. email: thierry.pun@unige.ch organization: Comput. Sci. Dept., Univ. of Geneva, Carouge, Switzerland |
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| Snippet | This paper proposes to maintain player's engagement by adapting game difficulty according to player's emotions assessed from physiological signals. The... |
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| SubjectTerms | Adaptation Assessments Categories Cybernetics Electroencephalography emotion assessment Emotion recognition Emotions Games Human Pattern classification Physiology signal analysis Trains |
| Title | Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty |
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