A Software Module for the Adaptive Estimation of Steady State Auditory Evoked Potentials
Bibliographic record
Abstract
A graphical user interface (GUI) implementing a novel technique of fast estimation of steady state auditory evoked potentials (SSAEPs) for rapid assessment of the functionality of the human auditory nervous system is presented. The proposed signal estimator has shown great promise in the fast extraction of weak signals buried under large amounts of noise such as the case with SSAEP signals. Currently, the main technical impediment in the widespread clinical use of the SSAEP testing for hearing assessment is the excessively long measurement time needed for the estimation process due to the presence of large amounts of background noise. The presented software module that is publicly disseminated through the Internet is meant to facilitate the use of an efficient signal processing technique by the hearing researchers. The software environment allows for loading previously recorded SSAEP signals into the workspace for analysis. Moreover, it enables the user to add simulated SSAEP signals to the background EEG for the purpose of testing the capability of the underlying signal processing algorithm.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.031 |
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".