[2512.20620] Uncovering Patterns of Brain Activity from EEG Data Consistently Associated with Cybersickness Using Neural Network Interpretability Maps

Computer Science > Human-Computer Interaction arXiv:2512.20620 (cs) [Submitted on 3 Nov 2025 (v1), last revised 27 Mar 2026 (this version, v2)] Title:Uncovering Patterns of Brain Activity from EEG Data Consistently Associated with Cybersickness Using Neural Network Interpretability Maps Authors:Jacqueline Yau, Katherine J. Mimnaugh, Evan G. Center, Timo Ojala, Steven M. LaValle, Wenzhen Yuan, Nancy Amato, Minje Kim, Kara D. Federmeier View a PDF of the paper titled Uncovering Patterns of Brain Activity from EEG Data Consistently Associated with Cybersickness Using Neural Network Interpretability Maps, by Jacqueline Yau and 8 other authors View PDF HTML (experimental) Abstract:Cybersickness poses a serious challenge for users of virtual reality (VR) technology. Consequently, there has been significant effort to track its occurrence during VR use with passive measures like brain activity recorded through electroencephalogram (EEG). To classify cybersickness accurately, including in real time, machine learning algorithms which can extract meaningful signals from the rest of the brain data will be required. However, EEG datasets are typically very small and very high in variability between participants, which makes building effective models extremely challenging. To address these concerns, we first introduce a framework for neural networks which has subject-adaptive training with calibration and interpretation for classification given limited and imbalanced EEG data. Which fea...