Uncovering EEG Patterns Consistently Associated with Cybersickness Discomfort Using Deep Learning Interpretability Maps

arXiv:2512.20620 · cs.HC, cs.LG · Submitted 2025-11-03 · Read on arXiv

cs.HC, cs.LG

Submitted: 2025-11-03

Updated: 2026-10-01

Project page: https://eeg-discomfort-nnilc.github.io

Terminology

• Interpretability — 2× in this paper · explained in 15 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Transformer Model — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Data
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Captum: A unified and generic model interpretability library for PyTorch

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