EEG-Based Engagement Monitoring in Cognitive Games
Bibliographic record
Abstract
Cognitive decline and dementia prevention are global priorities, with cognitive rehabilitation games showing potential to delay their onset or progression. However, these games require sufficient user engagement to be effective. Assessing the engagement through questionnaires is challenging for the individuals suffering from cognitive decline due to age or dementia. This study aims to explore the relationship between game difficulty levels, three EEG engagement indices (β/(θ + α), β/α, 1/α), and the self-reported flow state scale score during video gameplay, and to develop an accurate machine learning algorithm for the classification of user states into high- and low-engagement. Twenty-seven participants (nine older adults) played a stunt plane video game while their EEG signals were recorded using EPOCX. They also completed the flow state scale for occupational tasks questionnaire after the easy, optimal, and hard levels of gameplay. Self-reported engagement scores significantly varied across the difficulty levels (p = 0.027), with the optimal level yielding the highest scores. Combining the three EEG indices achieved the best performance, with F1 scores of 89% (within-subject) and 81% (cross-subject). Engagement classification F1 scores were 90% for young adults and 85% for older adults. The findings provide preliminary data that supports using EEG data for engagement analysis in adults and older adults.
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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".