Workload Analysis of Pilot Steep Turn Maneuvers Using SR20 Aircraft and EEG Data
Bibliographic record
Abstract
Objective: To compare left vs. right steep turns in terms of workload-related neurophysiological signatures using electroencephalogram (EEG) and machine learning. Methods: Thirty-seven flight cadets performed one left and one right steep turn in an SR20 desktop flight simulator while a 32-channel EEG (Emotiv EPOC Flex 32) was recorded. From 2-s sliding windows (50% overlap), 800 features per window were extracted (time-, frequency-, and non-linear domains). Six classifiers (XGBoost, LightGBM, GB, SVM, LR, and Linear SVC) were evaluated using cross-subject nested cross-validation with variance-ranked feature subsets (20%, 40%, 60%, 80%, and 100%), and an additional 10% subset was assessed to identify a more parsimonious feature set. Results Objective EEG/ML: LightGBM demonstrated superior performance across all feature proportions. Subjective: interpretation combined RF-based importance and variance-ranked top-feature analysis, showing convergent frontal/frontocentral dominance with complementary utility definitions (predictive contribution vs. signal dispersion). Physiologically, left turns were associated with relatively higher high-frequency activity/complexity, whereas right turns showed relatively stronger theta/alpha-related patterns. Interpretation: These findings support MWL-associated directional neurophysiological differences in steep turns and identify candidate EEG markers for lightweight real-time workload monitoring, facilitating optimized flight training and enhanced aviation safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".