A comparison of ECG and EEG metrics for in-flight monitoring of helicopter pilot workload
Bibliographic record
Abstract
There is increasing interest in understanding the cognitive and physiological state of operators in safety critical situations (e.g. pilots), specifically as it relates to task difficulty and mental workload. Herein, we evaluate the potential of electrocardiography (ECG) and electroencephalography (EEG) for detecting in-flight changes in helicopter pilot workload. Two National Research Council Canada test pilots performed a series of flight maneuvers in an NRC Bell 205 helicopter which involved a target tracking task with three levels of difficulty. Subjective ratings of pilot workload were collected using the Cooper-Harper handling quality ratings scale and pilot control activity was quantified based on cyclic control movements. ECG derived measures of heart rate and heart rate variability, as well as EEG derived measures of power in three frequency bands (theta 4-8Hz; alpha 8-13Hz; beta 13-22Hz), were computed and compared across task difficulty levels. A set of support vector machine (SVM) regressors were trained and tested to differentiate the three difficulty levels from ECG and EEG features. Differences in subjective ratings and control activity metrics confirmed the task difficulty manipulations (pECG= 0.17) performing better than the EEG-based regressor (minimum cross-validation MSEEEG= 0.29). This study provides an initial application demonstration of physiological and cognitive metrics and machine learning approaches for detecting differences in task difficulty during helicopter flight. This is the necessary first step for further development of passive brain computer interfaces for real-time in-flight monitoring of helicopter pilot workload.
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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 | 0.003 |
| 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 | 0.001 | 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".