The Neural Footprint of Play: Classifying Gamer Expertise via Raw EEG and Brain Topography

Ricardo Emmanuel García Manzo; Ricardo Antonio  Salido Ruiz; Eduardo  Mendez Palos; Víctor Ernesto  Moreno González; Fernanda J.  Miranda Peral; Marco Antonio  Perez Cisneros; Alma Yolanda  Alanis Garcia

Autores/as

Ricardo Emmanuel García Manzo 📩 Universidad de Guadalajara, México https://orcid.org/0000-0002-1210-3459
Ricardo Antonio Salido Ruiz Universidad de Guadalajara, México https://orcid.org/0000-0001-6135-9306
Eduardo Mendez Palos Universidad de Guadalajara, México
Víctor Ernesto Moreno González Universidad de Guadalajara, México
Fernanda J. Miranda Peral Universidad de Zamora, De Hidalgo, Mexico https://orcid.org/0009-0006-2555-3017
Marco Antonio Perez Cisneros Universidad de Guadalajara, México
Alma Yolanda Alanis Garcia Universidad de Guadalajara, México

Palabras clave:

EEG, EEGVideogames, VideogamesNeurogaming, Neural Networks, Expertise classification

Resumen

This study explores the feasibility of classifying videogame expertise using raw electroencephalographic (EEG) signals recorded dur-ing gameplay of Temple Run®. EEG data from ten subjects were pro-cessed and analyzed through three complementary approaches. First, a Multilayer Perceptron (MLP) neural network was trained with im-balanced data, followed by a second experiment using Synthetic Mi-nority 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 infor-mation without requiring feature extraction. A third experiment involved topographic analysis, revealing distinct spatial activation patterns be-tween video gamers and non-gamers. Novice players exhibited greater parieto-occipital activation, while experienced players showed more fo-cused frontal engagement, reflecting enhanced visuomotor efficiency. To-gether, these results highlight the potential of raw EEG for real-time ex-pertise classification and its broader applications in neurogaming, adap-tive systems, and digital health.

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