Descriptor: Multimodal Dataset for Player Engagement Analysis in Video Games (MultiPENG)
Bibliographic record
Abstract
Player engagement is crucial for understanding and optimizing gaming experiences, yet the research community lacks comprehensive multimodal datasets with reliable engagement annotations. We present a dataset combining six synchronized data streams—EEG, eye tracking, heart rate, user inputs, webcam footage, and gameplay frames—collected from 39 participants playing popular games across varying difficulty levels. Our dataset's distinctive feature lies in its temporal precision, achieved through strategic integration of engagement surveys during natural game pauses, minimizing both recall bias and gameplay disruption. The dataset includes 900 annotated gameplay sessions with four psychological metrics (engagement, interest, stress, and excitement). Initial analyses revealed surprising findings: human judges achieved only 0.48 F1-score in engagement assessment from webcam footage, while a flow theory-based model reached 0.60 F1-score using difficulty and player experience. Our multimodal neural model combining EEG, eye tracking, and facial features demonstrated the dataset's potential with a 0.51 F1-score despite class imbalance. This comprehensive dataset enables various research directions in engagement measurement and modeling, supporting the development of more robust real-time engagement detection systems.IEEE SOCIETY/COUNCILInstrumentation and Measurement Society (IMS)DATA TYPE/LOCATIONVideos, Keystrokes, Physiological SignalsDATA DOI/PID10.34740/kaggle/ds/6552328
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.017 |
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".