Monitored Versus Non-Monitored Stimuli in Brain-Computer-Interface Methods for Classifying Workload States During Piloting Tasks
Bibliographic record
Abstract
Applying wireless sensors for measurement of pilot mental workload using electroencephalography (EEG) in passive brain-computer interfaces (pBCI) has proven difficult due to weak workload signatures in artifact-laden environments. While active monitoring of task-irrelevant stimuli is used with pBCI designs to improve the signal to noise ratio (SNR), this method introduces non-piloting tasks, and thus adds to pilot workload. A fully implicit non-monitoring approach, where stimuli are presented but ignored decreases superfluous artifacts associated with monitoring and responding to the stimuli, but also paradoxically weakens the workload signal of interest. We compared the misclassification rates for a variety of pBCI paradigms in monitoring versus non-monitoring conditions where auditory stimuli were used in the classification of moderate versus high workload conditions. The pBCI models incorporated spectral features of the EEG recorded wirelessly during a virtual reality flight simulation with non-pilots. Classification of workload states, using an open source BCI toolbox, found similar misclassification rates between the monitoring versus non-monitoring datasets (Bayes Factors between 2.1 and 2.9 showed there was no effect of group on classification errors). The results show that reasonable measurement of adjacent workload states (moderate versus high) can be undertaken using EEG data from wireless sensors using non-monitoring pBCI methods.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".