Measuring workload using a combination of electroencephalography and near infrared spectroscopy
Why this work is in the frame
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Bibliographic record
Abstract
The ability to continuously monitor workload in a real-world environment would have important implications for the offline design of human machine interfaces as well as the real-time improvement of interaction between humans and machines. We explored the usefulness of features derived from electroencephalography (EEG) spectra, near infrared spectroscopy (NIRS) hemoglobin concentration, and their combination, under data acquisition and processing conditions that could be applied to real-time usage. We simultaneously recorded from eight EEG and three NIRS channels during different workload conditions of the N-back task (N = 0, 1, 2). EEG and NIRS data were classified independently, and in combination. EEG could be used to reliably classify workload condition for most subjects and NIRS for half of them. NIRS tended to contribute to classification accuracy when combined with EEG in some subjects. We discuss implications and future directions.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it