A Neurophysiological Sensor-Equipped Head-Mounted Display for Instrumental QoE Assessment of Immersive Multimedia
Bibliographic record
Abstract
The last few years have seen a drastic increase in the consumption of virtual- and augmented-reality (VR and AR, respectively) applications. Ultimately, the success of any emerging technology will rely on the experience it provides the end user, and not on the technology itself. Subjective methods for quality-of-experience (QoE) assessment have as main disadvantage that converting such human factors into a quality rating is difficult, particularly for everyday users. To overcome this limitation, recent research has explored the use of objective methods to monitor neurophysiological correlates of relevant perception processes. In this paper, we describe the development of a neurophysiological sensor-equipped head-mounted display that combines a consumer off-the-shelf VR headset, a modified low-cost portable device for electroencephalogram (EEG) acquisition repurposed to simultaneously acquire EEG, electrocardiogram (ECG), and electrooculogram (EOG) signals with high-quality dry electrodes. The device was evaluated under three different scenarios, each one designed to test the different ExG modalities. Initial tests showed promising results and allowed for (1) steady-state visually evoked potentials to be accurately measured from EEG, (2) heart rate variability measurements to discriminate between different affective videos, and (3) EOG measurements to monitor gaze direction and eye blinks, all while users were mobile. Being able to accurately monitor signals from the autonomic and central nervous systems in an unobtrusive and portable manner is an important step for instrumental QoE assessment of emerging VR/AR applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".