Evaluating Pilot Mental Workload Using fNIRS-Based Functional Connectivity Features with a Deep Residual Shrinkage Network Under Emergency Flight Scenarios
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Excessive mental workload can lead to less remaining resources for pilots to perform concurrent tasks during emergency flights, affecting aviation safety. Based on a flight simulator, this study investigated 25 cadet pilots using functional near-infrared spectroscopy (fNIRS) and subjective ratings to assess their mental workload under three subtasks with different equipment failures. fNIRS data included oxyhemoglobin, deoxyhemoglobin, and total hemoglobin signals, yielding 10545 functional connectivity (FC) features from four brain regions: prefrontal, right motor, left motor, and occipital cortexes. A deep residual shrinkage network classified mental workload levels, outperforming convolutional neural network and random forest models with 89.58% accuracy after feature selection employing an interpretable machine learning algorithm. The results suggest that brain FC from three hemoglobin signals could be used to differentiate the three different levels of pilot mental workload. This study could contribute to improving pilot training and supporting the development of pilots’ competencies during emergency scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it