A Pilot Study on Quantifying Signal Quality in High-Density Surface Electromyography
Bibliographic record
Abstract
Adequate electromyography (EMG) signal quality is important for obtaining correct interpretations for EMG diagnostics and EMG-based control. In this pilot study, a dataset (N = 2) of high-density recordings of the biceps and triceps was rated on a four-point scale by two expert EMG raters. The intra-rater reliability was good-to-excellent (ICC (2,1) > 0.76) and the inter-rater reliability was good (ICC (3,k) = 0.85). Four regression models were developed to label electrodes automatically: 1) linear regression, 2) random forest regression, 3) support vector regression, and 4) a combination of the models obtained via majority voting. Using the human raters as a ground truth, the random forest and support vector regression obtained a very strong correlation (rs= 0.90) and excellent reliability (ICC (3,k) = 0.95). These results demonstrate the potential for the development of an automated process to quantify EMG channel quality.
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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 | 0.011 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".