Dry EEG-based Mental Workload Prediction for Aviation
Bibliographic record
Abstract
Human errors are reputed to be one of the main causes of most accidents or incidents in aviation. Such can be explained by the fact that pilots are frequently exposed to sources of emotional and cognitive stressors, including challenges pertaining to mental workload. Real-time mental workload assessment of crew during flight could help identifying cognitive overload of the crew and then reduce aviation accidents. Electroencephalography (EEG) is a well-known tool used to infer mental states. EEG prediction of mental workload is however mostly performed in highly controlled settings whereas, in the cockpit, many types of confounds (e.g., physical movement) can induce noise into the signal. The goal of this study was to develop an EEG-based workload prediction model that could be used in aviation use cases characterized by noisy signal. To reach this goal, we used machine-learning algorithms to explore the feasibility to classify different levels of mental workload from EEG features. We used a dataset composed of noise induced by physical activity (either low, medium or high levels) collected while participants performed either low-demanding or high-demanding cognitive tasks. Using only three electrodes (Fp1, Fp2 and P3) and 15 spectral band features with the physical condition label, we generated a random forest classifier with a prediction accuracy of 76%. This model could run in real time and provide workload inference at a rate of one prediction each second. Overall, our results show the possibility for predicting real-time mental workload in operational environments using dry-electrode EEG solutions developed for ambulatory use cases.
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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) | 0.001 | 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".