EEG-Based Emotion Recognition During Mobile Gameplay

The gaming industry, encompassing both console and mobile platforms, has experienced remarkable growth in recent years. Assessing emotional responses during gameplay presents a significant challenge. This study employs electroen-cephalography (EEG) signals to discern players' emotions while engaged in mobile gaming. Specifically, two popular mobile games, Temple Run and Subway Surfers, were chosen to represent diverse gaming environments. Each game underwent three separate trials, during which EEG data was collected using a commercially available 4-channel MUSE headband. Following each trial, players indicated their emotional state on a 9-point valence and arousal scale. Statistical analysis, including a t-test, was conducted on the valence and arousal scores for both games, revealing a significant difference in emotional responses (p 0.05). Player data was labeled using the majority voting technique based on the three trials. Features were extracted from both the frequency domain and time domain which were used to classify valence and arousal into two classes using support vector machine (SVM) and k-nearest neighbor (KNN) classifiers. The KNN classifier achieved the highest accuracy rates, with 80.55