Predicting Focused and Mind-Wandering States from EEG Signals Recorded During Driving
Bibliographic record
Abstract
Driving is a demanding physical and cognitive task. It requires significant mental focus and coordinated movements to ensure safety. However, long periods of driving can cause the mind to wander, which impairs focus and raises the possibility of accidents. As a result, identifying accurate methods to detect such states could lead to safer roads for everyone. Previous studies have built on that direction by using machine learning models to predict fatigue and non-focused states. However, these works have mainly relied on machine learning models, relegating the capacity of deep learning models. Moreover, these works have been tested on datasets comprising at most ten subjects. To address these limitations, this study presents a methodology that combines signal processing techniques and deep learning models to predict focused and mind-wandering states from EEG signals collected from 21 subjects while driving. The best model of our approach, a Transformer, achieved an accuracy, macro F1 score, weighted F1 score, and ROC-AUC of $88 \%, 87 \%, 89 \%$, and $94.4 \%$, respectively. Our results suggest that transformer models are the most suitable for detecting focused states, providing a foundation for developing EEG-based applications to monitor brain states during driving.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".