Playing videogames is a process driven by both cognitive and emotional factors. Then, developing a mechanism that takes into account players’ emotional state for adapting specifc game features is a way to increase their engagement and ow during gameplay. In this paper, we present how a passive Brain Computer Interface (BCI) can be used to assess the state of mind of a player that can be used for enhancing his experience through adaptation. In particular, we collected data from EEG signals, in a horror adventure game, to learn a model of ow by monitoring the level of boredom, ow, and stress of the player. To this aim, we set an experiment and collected both subjective data about the perceived emotions and state of ow and data from the BCI that have been used to learn a classi er to recognize and assess the player’s affective state. Results are encouraging and the learned model achieves a good accuracy in distinguishing the three player’s states
