Bibliographic record
Abstract
Techniques for monitoring human performance traditionally rely on subjective responses and task-specific scoring, yet research suggests EEG could offer multiple performance metrics with high temporal resolution and accuracy that could be leveraged for human–computer interaction purposes. The objective of the presented work is to investigate which EEG responses correlate with task performance and evaluate whether combinations of these produce effective predictive models, facilitating further understanding of the psychological link to performance. A user study was conducted with 32 participants required to negotiate a driving course with the ambition of learning and improving ability on the course during an EEG recording session. EEG was filtered and post-processed to find power spectral density in alpha ($\alpha$), beta ($\beta$), delta ($\delta$), and theta ($\theta$) frequency bands, as well as frontal alpha asymmetry (FAA). The initial laps were considered a baseline and an average performance improvement was calculated over the remaining laps in terms of percentage improvement in duration of track traversal. Results demonstrate event related desynchronization with increased task performance in the alpha (p=. 000), delta (p=. 000), and theta (p=. 000) bands, as well as evidence of a relationship between overall change in FAA and task efficiency. A full electrode analysis identifies$\delta _{{\rm F}_{4}}$as the optimal for predicting collisions, with efficiency best predicted by a combination of$\beta _{{\rm O}_{\rm z}}$and$\delta _{{\rm F}_{4}}$.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".