Audio’s Impact on Deep Learning Models: A Comparative Study of EEG-Based Concentration Detection in VR Games
Journal
Inventions
ISSN
2411-5134
Publisher
MDPI AG
Date Issued
2025-10-29
Author(s)
Francisco R. Castillo-Soria
Roilhi F. Ibarra-Hernández
Leonardo Betancur Agudelo
Type
journal-article
Abstract
This study investigates the impact of audio feedback on cognitive performance during VR puzzle games using EEG analysis. Thirty participants played three different VR puzzle games under two conditions (with and without audio) while their brain activity was recorded. To analyze concentration levels and neural engagement patterns, we employed spectral analysis combined with a preprocessing algorithm and an optimized Deep Neural Network (DNN) model. The proposed processing stage integrates feature normalization, automatic labeling based on Principal Component Analysis (PCA), and Gamma band feature extraction, transforming concentration detection into a supervised classification problem. Experimental validation was conducted under the two gaming conditions in order to evaluate the impact of multisensory stimulation on model performance. The results show that the proposed approach significantly outperforms traditional machine learning classifiers (SVM, LR) and baseline deep learning models (DNN, DGCNN), achieving a 97% accuracy in the audio scenario and 83% without audio. These findings confirm that auditory stimulation reinforces neural coherence and improves the discriminability of EEG patterns, while the proposed method maintains a robust performance under less stimulating conditions.
License
Acceso Abierto.
URL License
How to cite
GomezRomero-Borquez, J., Del-Valle-Soto, C., Del-Puerto-Flores, J. A., López-Pimentel, J.-C., Castillo-Soria, F. R., Ibarra-Hernández, R. F., & Betancur Agudelo, L. (2025). Audio’s Impact on Deep Learning Models: A Comparative Study of EEG-Based Concentration Detection in VR Games. Inventions, 10(6), 97. https://doi.org/10.3390/inventions10060097
Table of contents
1. Introduction -- 2. Materials and Methods -- 3. Results -- 4. Discussion -- 5. Conclusions.