Mismatch negativity and reorienting negativity demonstrated during EEG flight trials
Bibliographic record
Abstract
INTRODUCTION: A physiological monitoring capability was integrated into a National Research Council Canada Convair 580 research aircraft in order to investigate pilot workload and fatigue. The goal of the project was to develop an objective means of evaluating pilot workload and alertness that does not interfere with a pilot’s flight task. To this end, an electroencephalography (EEG) system was interfaced with the aircraft communication system to present auditory stimuli to the pilot’s headset so that the resultant event-related potentials (ERPs) could be analyzed. The result was a precisely-timed technique for evaluating the cognitive processing of an ignored auditory information stream in a flying pilot. METHODS: An auditory oddball task was used such that 100 ms standard (1000Hz) and deviant (1500Hz) tones were presented at a rate of one per second. Deviant tones represented 10% of the tone sequence, and were delivered at random, unpredictable times. RESULTS: Across a series of 15-minute flight segments, characteristic ERP sensory components (P1, N1, and P2) were observed in response to the standard tones. More importantly, when the event-related potentials to standard and deviant tones were compared, a signifi cant mismatch negativity (MMN) with a classic frontocentral distribution was observed. A statistically significant reorienting negativity (RON) was also observed, suggesting a reorienting to the primary flight task following distraction by the deviant tones. DISCUSSION: Mismatch and reorienting negativity are small ERP components that can be diffi cult to detect in environments that are high in electromagnetic noise. The demonstration of reliable MMN and RON effects during actual flight represents an important step in the development of pilot alertness monitoring techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".