Diurnal variations in the waking EEG: comparisons with sleep latencies and subjective alertness
Bibliographic record
Abstract
Daytime measures of sleep latency and subjective alertness do not correlate with one another, suggesting that they assess different aspects of alertness. In addition, their typical diurnal variations show very different time courses. Quantitative analysis of the waking electroencephalogram (EEG) has been proposed as an objective measure of alertness, but it is not clear how it compares with other measures. In this study, the waking EEG was measured in the daytime to determine the presence of diurnal variations in the activity of standard frequency bands and to compare these variations with the temporal patterns typical of sleep propensity and subjective alertness. Alertness was evaluated in four men and 12 women, aged 19-33 y. Assessments were conducted every 2 h, from 10.00 to 24.00, in the following order: a visual analogue scale of alertness, a waking EEG recording and a sleep latency test. The waking EEG was recorded with eyes open. For each recording session, 32-60 s of artefact-free signals were selected from the C3/A2 derivation, then subjected to amplitude spectral analysis. Four EEG frequency bands showed significant diurnal variations: delta, theta, sigma and beta1. None of these variations showed a significant correlation with the temporal patterns of sleep latencies or subjective alertness. At the individual level, however, theta band activity increased when subjective alertness decreased, suggesting that the theta band can be used to monitor variations in alertness in a given individual, even at the moderate levels of sleepiness experienced during the daytime.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".