An Investigation of Electrovestibulography and Vestibular Field Potentials
Bibliographic record
Abstract
An emerging technique called Electrovestibulography (EVestG) shows promise for the diagnosis of various balance and mood disorders. This technique records the electrical signal from the external ear during evoked vestibular responses. In order to extract the vestibular signal from a recording which contains many other signals (muscle artifacts, cochlear responses, and environmental noise), a signal processing technique called the Neural Event Extraction Routine (NEER) is used [1]. NEER currently consists of four major operations. In the first step is an adaptive filter is applied to the signal to reduce noise and artifacts as much as possible. The second step is to separate the signal into segments corresponding to the direction of acceleration applied to the subject. The third step involves filtering the signal using a set of wavelets, each tuned to a different frequency. The final step is to use a set of heuristics on the phase and magnitude of the wavelet responses to identify possible evoked field potentials. These possible field potentials are then averaged to obtain a typical field potential, which can be used to identify disorders [2]. So far, a number of refinements have been made to the original algorithm. A rewrite for computational efficiency has allowed much faster processing of results. Also, changes to the segmentation process have been investigated. Numerous tests have been done to measure the reproducibility of EVestG data under various experimental conditions. Additionally, initial work has been done on a procedure for objectively evaluating the performance of the NEER algorithm using simulated signals. Such a procedure will allow us to compare the current algorithm with other techniques. Future work will use the characterization of the evoked vestibular field potential from animal studies to guide improvements to the NEER algorithm. A possible alternative algorithm will be investigated which will use matched filters based on expected field potential shapes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".