Isolating Visual Evoked Responses—Comparing Signal Identification Algorithms
Bibliographic record
Abstract
PURPOSE: To compare signal identification algorithms for recording visual evoked potentials (VEP). METHODS: VEPs were recorded both in the presence and absence of a stimulus. Four algorithms were designed to estimate the probability that a recording contains a stimulus evoked signal, and to assign weights for use in a weighted average to isolate a final VEP. Algorithms were compared on their ability to identify trials containing VEPs; the signal-to-noise (SNR) ratios of the final VEP, and the number of trials required to isolate a VEP that was significantly different from background noise. RESULTS: All the algorithms isolated VEPs that did not differ significantly in timing or amplitude from those extracted using traditional ensemble averaging. All the studied algorithms were capable of identifying and assigning a significantly greater weight to trials containing visually evoked signals compared with trials containing only noise potentials (P < 0.01). The best performing algorithm produced a ninefold increase in the signal-to-noise of the extracted waveform. DISCUSSION: The present investigation provides empirical confirmation that computational signal identification algorithms can improve the detection of VEP signal embedded in noise. When combined with weighted averaging they can reduce the number of trials required for evaluation.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".