Difficulty Adjustment Using Player's Performance and Electroencephalographic Data

Abstract

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 (DDA) and Rhythm-Group Theory (a procedural content generation method), combined with levels of attention obtained from Electroencephalographic (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 computed in real time to decide the shape and structure of the levels. The method designed in this research can be implemented not only in platformers but also in other genres that involve elements of rhythm, additionally, it could be used by game developers as a tool for playtesting in order to improve the game design, receive quantitative and numerical feedback from players and create an overall better experience for their players.