Estimating the Properties of the Single-Trial Speech Auditory Brainstem Response Using an Accurate AR Model
Bibliographic record
Abstract
The human speech Auditory Brainstem Response (sABR) is an electrophysiological response with potentially important clinical and practical applications. However, because of the very low SNR of the signal, long recording times are usually needed over which the responses from a large number of trials are coherently averaged. Therefore, it is important to understand the properties of the single trial sABR, as this can help in developing methods to detect this response using a smaller number of trials. This paper presents a parametric model of averaged human sABR that is used to estimate the properties of a single-trial response. The Autoregression (AR) method is followed to model the sABR at four different signal qualities, based on recorded data coherently averaged over different numbers of trials. The properties of the modeled sABR are compared with the recorded ones in the time and frequency domains. This model is also used to estimate a single-trial sABR. The results show that the properties of the modeled responses (statistical distribution, SNR, noise power) are similar to the recorded sABRs. Moreover, coherent averaging based on the estimated single-trial sABR produces a comparable theoretical SNR increase, similar exponential relation between the SNR and number of averaged trials, and a similar power spectrum to the recorded sABR.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".