EEG-based Emotion Recognition for Game Difficulty Control

Abstract

Balance design taking game difficulty into account has an important role in game design. In recent years, a number of studies have tried to adjust difficulty by using various player dependent difficulty detection algorithms. But most of these methods need customizing its algorithm for each game. In this paper, we investigate the way to find adaptive game difficulty levels according to player’s emotion by analyzing electroencephalogram (EEG) signals for improving player’s emersion. A player’s EEG signals during playing a rhythm game which has three different difficulty levels were analyzed by using PAD model. We focus on the states of emotion from players EEG signals.