Adaptable Game Experience Based on Player's Performance and EEG

For high skilled players, an easy game might become boring and for low skilled players, a difficult game might become frustrating. The purpose of this research was to create new and better ways to offer players with different skills, an appropriate experience. We focused on adapting the difficulty levels of a simple 2D platform game, designing and building levels automatically. The proposed method consists of Dynamic Difficulty Adjustment and Rhythm-Group Theory (a procedural content generation method), combined with levels of attention obtained from EEG data. Experiments were designed in the way that players had to clear five different levels that were created automatically using the player's performance and EEG data obtained from a biosensor while playing. Results showed that the method successfully adapts the level difficulty according to the player's status. In addition, the designed method calculates difficulty using values calculated in real time to decide how the level should be created. We consider that this new method can be implemented not only in platformers but also in other genres, also, it could be used by game developers as a tool of playtesting when designing new levels for their games.