A Neurocognitive Approach to Evaluate Mobile Game Player’s Experience Using EEG

In recent years, the gaming industry has witnessed exponential growth, with an increasing focus on enhancing player experience and engagement. To achieve this, we propose a method that recognizes game experience traits of players using Electroencephalography (EEG). The proposed method aims to comprehensively assess player engagement with neural measurements from EEG recordings. The MUSE EEG headband is used to capture data while playing a game. Data is preprocessed to minimize the unwanted noise in the EEG data. Different frequency domain features are extracted and Three different classifiers (Random Forest, K-nearest neighbour and Naive Bayes) are used to detect the existence (High/Low) of two gaming experience traits: tension and challenge. A highest accuracy of 86.6% and 88.8% is achieved for challenge and tension game trait recognition respectively using Random Forest classifier. Notably, the study unveiled that different game traits can be recognized using EEG, suggesting the potential for personalized game design.