Feasibility of an EEG-based dynamic suboptimal cognitive monitoring for field neuroergonomics
Bibliographic record
Abstract
Suboptimal cognitive states among construction workers significantly impact safety and productivity, with mental workload playing a key role in triggering these states.Determining if the mental workload fluctuation is leading to an error is challenging as the relationship between mental workload and suboptimal cognitive states is complex and non-linear, with traditional theories failing to map their fluctuations effectively.Recently, a two-dimensional space has been introduced to theoretically map mental workload fluctuations and suboptimal cognitive states using task engagement and arousal.However, there is currently no framework in place to continuously apply this theoretical knowledge in practical settings.To address this gap, this study investigates the feasibility of EEG-based frameworks for classifying four different cognitive states, namely comfort zone, mind wandering, effort withdrawal, and inattentional blindness, based on mental workload fluctuations.EEG signals were collected from 10 participants using a headset with dry electrodes, processed to extract relevant features, and classified using Support Vector Machine (SVM) and Artificial Neural Network (ANN) models.The ANN achieved superior performance in k-fold and leave one period out validation methods, though accuracy declined in leave one subject out validation.These findings underscore the potential of EEG-based differentiation of cognitive suboptimalities to enhance safety and productivity in construction by providing crucial information about when construction workers are most likely to make cognitive errors, which is essential for timely and appropriate interventions.Also, the low subject independent accuracy emphasizes the need to address individual differences in EEG signals for broader applicability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".