Dynamic Game Difficulty Control by Using EEG-based Emotion Recognition

Abstract

Computer games are steadily one of major way to enjoy leisure time. Main goal of game entertainment is to keep the player's immersion. Thus, balance design taking game difficulty into account has an important role in game design. In recent years, a number of studies tried to adaptive difficulty by using various algorithms which detect player dependent difficulty. Most of these algorithms need customizing itself for each game. Measuring features which determine game difficulty is the major problem of these ways of game balancing. But, it can be easy by using the player's immersion during playing games. And, player's immersion is detected by analyzing electroencephalogram (EEG) signals. In this paper, we propose a dynamic game difficulty control system, by using this emotion recognition technique with PAD model by using players' EEG signals during playing a rhythm game which has three different difficulty levels. © 2014 SERSC.