A novel correlation-driven cross-term compression polynomial network for classifying motion sickness levels

Virtual reality (VR) motion sickness remains a major barrier to widespread adoption. While hardware improvements are common, efficient and accurate algorithmic monitoring solutions are still lacking. Deep learning methods achieve high accuracy but suffer from high complexity and poor interpretability, whereas traditional polynomial models become inefficient due to the combinatorial explosion of cross-terms. To address these limitations, we propose the Correlation-Driven Cross-Term Compression Polynomial Network (CDCTC-PolyNet), a lightweight polynomial model designed for real-time cybersickness recognition. The key innovation is correlation-driven cross-term compression: pairwise feature relationships are quantified using Pearson correlation coefficients, and cross-terms of highly correlated features are approximated by univariate higher-order terms. This strategy significantly reduces model complexity while preserving predictive accuracy. CDCTC-PolyNet is evaluated on ElectroEncephaloGraphy (EEG) data from 23 subjects using 1-s epochs, with cybersickness labeled as high or low based on the median Simulator Sickness Questionnaire (SSQ) score. Under a subject-mixed data split, the model achieves a classification accuracy of 97. 98%, with 10% lower computational complexity and 20% faster training compared to baseline methods. Although this study focuses on methodological validation rather than cross-subject generalization, the results demonstrate the feasibility of CDCTC-PolyNet as an interpretable and ultra-lightweight framework for EEG-based motion sickness monitoring. Beyond VR gaming, the proposed framework can be deployed in low-power Augmented Reality (AR) glasses or vehicle infotainment systems to provide real-time alerts before motion sickness affects driving safety or passenger comfort. The code is available at https: //github. com/YingYan2024/CDCTC-PolyNet.

Authors

• Ying Yan
• Haoran Li Institute of Automation, Chinese Academy of Sciences & School of Artificial Intelligence, University of Chinese Academy of Sciences
• Jun Cai Nanjing University
• Guanting Liu
• Edmond Qi Wu
• Hao Wang Monash University
• Chengcheng Hua
• Yaowen Yu
• Aiguo Song

Keywords

• Correlation-driven cross-term compression polynomial network
• Motion sickness
• Virtual reality
• Classification
• Electroencephalography