Human Auditory Steady-State Responses: The Effects of Recording Technique and State of Arousal
Bibliographic record
Abstract
UNLABELLED: There is some controversy in the literature about whether auditory steady-state responses (ASSRs) can be reliably recorded in all subjects and whether these responses consistently decrease in amplitude during drowsiness. In 10 subjects, 40-Hz ASSRs became significantly different from background electroencephalogram activity with a probability of P < 0.01 and an average time of 22 s (range, 2-92 s), provided that the responses were analyzed with time-domain averaging rather than spectral averaging. In a second experiment with 10 subjects, 40-Hz ASSRs recorded between the vertex and posterior neck consistently decreased in amplitude during drowsiness and sleep. Findings that the ASSR may occasionally increase during drowsiness may be explained by postauricular muscle responses recorded from a mastoid reference. These may occur during drowsiness in association with rolling-eye movements. ASSRs recorded between the vertex and posterior neck are not distorted by these reflexes. These findings combine with previous literature on the effects of general anesthetics on the ASSR to confirm that the ASSR is a valid option for monitoring the hypnotic effects of general anesthetics. IMPLICATIONS: Auditory steady-state responses to stimuli presented at rates near 40 Hz can be used to monitor anesthesia. These responses can be quickly and reliably recorded during both sleep and wakefulness, provided that appropriate averaging techniques are used.
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.
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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".