To pre-process or not to pre-process? On the role of EEG enhancement for cybersickness characterization and the importance of amplitude modulation features
Bibliographic record
Abstract
Virtual Reality (VR) has expanded beyond the entertainment field and has become a valuable tool across different verticals, including healthcare, education, and professional training, just to name a few. Despite these advancements, widespread usage of VR systems is still limited, mostly due to motion sickness symptoms, such as dizziness, nausea, and headaches, which are collectively termed “cybersickness”. In this paper, we explore the use of electroencephalography (EEG) as a tool for real-time characterization of cybersickness. In particular, we aim to answer three research questions: (1) what neural patterns are indicative of cybersickness levels, (2) do EEG amplitude modulation features convey more important and explainable patterns, and (3) what role does EEG pre-processing play in overall cybersickness characterization. Experimental results show that minimal pre-processing retains artifacts that may be useful for cybersickness detection (e.g., head and eye movements), while more advanced methods enable the extraction of more interpretable neural patterns that may help the research community gain additional insights on the neural underpinnings of cybersickness. Our experiments show that the proposed amplitude modulation features comprise roughly 60% of the top-selected features for EEG-based cybersickness detection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".