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%.

The work presented in this paper aims at assessing human emotions using peripheral as well as electroencephalographic (EEG) physiological signals on shorttime periods. Three specific areas of the valence-arousal emotional space are defined, corresponding to negatively excited, positively excited, and calm-neutral states. An acquisition protocol based on the recall of past emotional life episodes has been designed to acquire data from both peripheral and EEG signals. Pattern classification is used to distinguish between the three areas of the valence-arousal space. The performance of several classifiers has been evaluated on ten participants and different feature sets: peripheral features, EEG time-frequency features, EEG pairwise mutual information features. Comparison of results obtained using either peripheral or EEG signals confirms the interest of using EEG's to assess valence and arousal in emotion recall conditions. The obtained accuracy for the three emotional classes is 63% using EEG time-frequency features which is better than the results obtained from previous studies using EEG and similar classes. Fusion of the different feature sets at the decision level using a summation rule also showed to improve accuracy to 70%. Furthermore, the rejection of non confident samples finally led to a classification accuracy of 80% for the three classes.

This paper reviews the psychophysiological method in game research. The use of psychophysiological measurements provides an objective, continuous, real-time, non-invasive, precise, and sensitive way to assess the game experience, but for best results it requires carefully controlled experiments, large participant samples and specialized equipment. We briefly explain the theory behind the method and present the most useful measures. We review previous studies that have used psychophysiological measures in game research, and provide future directions.

Affective states, moods and emotions, are an integral part of human nature: they shape our thoughts, govern the behavior of the individual, and influence our interpersonal relationships. The last decades have seen a growing interest in the automatic detection of such states from voice, facial expression, and physiological signals, primarily with the goal of enhancing human-computer interaction with an affective component. With the advent of brain-computer interface research, the idea of affective brain-computer interfaces (aBCI), enabling affect detection from brain signals, arose. In this article, we set out to survey the field of neurophysiology-based affect detection. We outline possible applications of aBCI in a general taxonomy of brain-computer interface approaches and introduce the core concepts of affect and their neurophysiological fundamentals. We show that there is a growing body of literature that evidences the capabilities, but also the limitations and challenges of affect detection from neurophysiological activity

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