Adaptive stimulus artifact cancellation in biological signals using neural networks
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Bibliographic record
Abstract
The recording of somatosensory evoked potentials (SEP) is very important today in diagnostic and intraoperative procedures. Ensemble averaging improves the signal-to noise ratio (SNR) by reducing the random interference. Ensemble averaging does not reduce the stimulus artifact which tends to mask or at least distort the SEP. The artifact is a result of the relatively large voltage applied to the body in order to elicit a nervous response, and is thus synchronized with the SEP. Several adaptive cancellation techniques have been used to reduce the stimulus artifact, but these techniques have typically assumed linearity between the primary and reference channels. Neural networks offer the advantage of being able to model nonlinearities. A neural network structure called Pi-Sigma is presented and the resulting cancellation of stimulus artifact in SEP data is shown. The results are compared to cancellation obtained using linear filters and a nonlinear RLS filter.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it