Advanced International Journal for Research

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Cybersickness Risk Prediction Using EEG And Symbolic Explainable Machine Learning

Author(s) Mr. Nikhil Souri Yalamati, Prof. Dr. Sobhan Babu Y, Prof. Dr. Venkatesh D
Country India
Abstract Cybersickness is still a major problem in Virtual Reality applications, leading to discomfort for users and poor usability of the system. This work proposes a Neuro-Symbolic method that can predict the risk of cybersickness from brain activity data recorded through electroencephalogram (EEG) with symbolic machine learning models that are interpretable. The pipeline that is proposed includes several steps: ingestion of multi-dataset EEG signals, statistical feature extraction based on windows, individual subject validation, and model interpretability using the methods of SHAP, LIME, and rule-based reasoning. The features that were extracted include the power of different brain frequencies, the asymmetry of the brain hemispheres, the connectivity between brain regions, the complexity of the spectrum, and the dynamics in time. The performance of the models was assessed using publicly available VR-EEG data sets, where it was found that Random Forest, XGBoost, and LightGBM, which are tree-based models, achieved high discriminative performance while maintaining complete interpretability through human-readable decision rules. Moreover, the system outputs statistical cross-fold results that are validated, calibrated risk scores, global and local feature importance, and ranked symbolic rules to elucidate the causes of cybersickness risk. The results suggest that it is possible to predict the risk of EEG-based cybersickness reliably in real-time while providing transparent decision paths that are suitable for academic evaluation and practical deployment.
Keywords Cybersickness Prediction, EEG Feature Engineering, Symbolic Machine Learning, Model Explainability, Virtual Reality Analytics
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 6, November-December 2025
Published On 2025-12-29
DOI https://doi.org/10.63363/aijfr.2025.v06i06.2763
Short DOI https://doi.org/hbhk36
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