EEG-Driven Dynamic Immersion Design for XR Gaming Experiences

Yunbing Han

2025

Abstract

This study explores the dynamic identification and design method of Extended Reality game immersion based on electroencephalography (EEG) signals. By using the EEG dataset from the Kaggle platform, the original emotion labels (Positive, Neutral, Negative) were first remapped to the corresponding immersion levels (High, Medium, Low) to construct an immersion recognition dataset suitable for classification models. The Orange platform was used for visual modelling and training, and finally achieved a very high accuracy (98.5%) and AUC value (0.999) under the Random Forest algorithm, which verified the feasibility and effectiveness of predicting the user immersion state through EEG data. Based on these results, this paper proposes the importance of real-time feedback for immersion based on physiological signals in game development and design. The process of user EEG state acquisition, immersion recognition, and content adjustment enhances the continuity and interactive depth of user experience. With the miniaturization and popularization of EEG devices, real-time feedback for immersion based on physiological signals such as EEG is expected to become a key infrastructure for designing next-generation immersive experiences.

Paper Citation

in Harvard Style

Han Y. (2025). EEG-Driven Dynamic Immersion Design for XR Gaming Experiences. In Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-792-4, SciTePress, pages 517-521. DOI: 10.5220/0014362100004718

in Bibtex Style

@conference{emiti25,
author={Yunbing Han},
title={EEG-Driven Dynamic Immersion Design for XR Gaming Experiences},
booktitle={Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2025},
pages={517-521},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014362100004718},
isbn={978-989-758-792-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - EEG-Driven Dynamic Immersion Design for XR Gaming Experiences
SN - 978-989-758-792-4
AU - Han Y.
PY - 2025
SP - 517
EP - 521
DO - 10.5220/0014362100004718
PB - SciTePress