Electroencephalogram and Physiological Signal Analysis for Assessing Flow in Games

Abstract

Passive brain-computer interaction (BCI) can provide useful information to understand a user's state and anticipate intentions, which is needed to support adaptivity and personalization. Given the huge variety of audiences, a game's capability of adapting to different user profiles-in particular to keep the player in flow-is crucial to make it ever more enjoyable and satisfying. We have performed a user experiment exploiting a four-electrode electroencephalogram (EEG) tool similar to the ones that are soon likely to appear in the market for game control. We have performed a spectral characterization of the video-gaming experience, also in comparison with other tasks. Results show that the most informative frequency bands for discriminating among gaming conditions are around low beta. Simple signals from the peripheral nervous system add marginal information. Classification of three levels of user states is possible, with good accuracy, using a support vector machine (SVM) classifier. A user-independent classification performs worse than a user-dependent approach (50.1% versus 66.4% rate). Personalized SVM training and validation time is reasonable (7-8 min). Thus, we argue that a personalized system could be implemented in a consumer context and research should aim at improving classifiers that can be trained online by end users.