Beyond Subjectivity: Continuous Cybersickness Detection Using EEG-based Multitaper Spectrum Estimation

2025/03/27 by Berken Utku Demirel, Adnan Harun Dogan, Demirel, Berken Utku +7 · 1 voice · 1 citation
Computer Science · Engineering · Psychology · #Emotion and Mood Recognition #FOS: Computer and information sciences #FOS: Electrical engineering #Human-Computer Interaction (cs.HC) #Signal Processing (eess.SP) #Social Robot Interaction and HRI #Virtual Reality Applications and Impacts #cs.HC #eess.SP #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2503.22024

openalex publication_date 2025/03/27 · arxiv published 2025/03/27 · arxiv updated 2025/06/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Virtual reality (VR) presents immersive opportunities across many applications, yet the inherent risk of developing cybersickness during interaction can severely reduce enjoyment and platform adoption. Cybersickness is marked by symptoms such as dizziness and nausea, which previous work primarily assessed via subjective post-immersion questionnaires and motion-restricted controlled setups. In this paper, we investigate the dynamic nature of cybersickness while users experience and freely interact in VR. We propose a novel method to continuously identify and quantitatively gauge cybersickness levels from users' passively monitored electroencephalography (EEG) and head motion signals. Our method estimates multitaper spectrums from EEG, integrating specialized EEG processing techniques to counter motion artifacts, and, thus, tracks cybersickness levels in real-time. Unlike previous approaches, our method requires no user-specific calibration or personalization for detecting cybersickness. Our work addresses the considerable challenge of reproducibility and subjectivity in cybersickness research.

Cited by

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

Discussions

• Using continuous EEG monitoring to detect cybersickness in VR: researchers have developed a real-time method using multitaper spectrum estimation from EEG signals, enabling dynamic tracking during fre [bsky, 4 points, 0 comments]

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