The Neural Footprint of Play: Classifying Gamer Expertise via Raw EEG and Brain Topography
Keywords:
EEG, Videogames, Neurogaming, Serious Games, Neural Networks, Expertise classificationAbstract
This study explores the feasibility of classifying videogame expertise using raw electroencephalographic (EEG) signals recorded during gameplay of Temple Run. EEG data from ten subjects were processed and analyzed through three complementary approaches. First, a Multilayer Perceptron (MLP) neural network was trained with imbalanced data, followed by a second experiment using Synthetic Minority Oversampling Technique (SMOTE) to correct class imbalance. The models achieved precision above 90%, with the highest accuracy of 92.89% obtained using balanced data and intermediate hidden-layer sizes, demonstrating that raw EEG signals contain discriminative information without requiring feature extraction. A third experiment involved topographic analysis, revealing distinct spatial activation patterns between video gamers and non-gamers. Novice players exhibited greater parieto-occipital activation, while experienced players showed more focused frontal engagement, reflecting enhanced visuomotor efficiency. Together, these results highlight the potential of raw EEG for real-time expertise classification and its broader applications in neurogaming, adaptive systems, and digital health.References
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