Signal Quality Assessment in Low-Density and Single Channel Surface Electromyography
Bibliographic record
Abstract
Electrode placement for surface electromyography (EMG) acquisition can be a difficult and time-consuming process. The use of electrode arrays and signal quality assessment for automatic channel selection has been proposed to facilitate EMG acquisition. Previous work examined signal quality assessment for high-density EMG (HD-EMG) arrays, but HD-EMG necessitates higher cost and complexity. This paper investigates automated signal quality assessment in low-density EMG (LD-EMG) arrays and single-channel EMG (SC-EMG). Machine learning regression models were trained to assess channel quality on a 3-point scale (Adequate, Good, Excellent), using signal quality ratings from two expert human raters as a ground truth. Performance was high for arrays with an inter-electrode distance ≤ 20 mm$(r_{s}$≥ 0.89, ICC (3, k) ≥ 0.93). Performance decreased for inter-electrode distance ≥ 30 mm, with random forest regression providing the best overall performance$(r_{s}$≥ 0.73, ICC (3, k) ≥ 0.61). Signal quality quantification of SC-EMG showed limited results$(r_{s}$≥ 0.51, ICC (3, k) ≥ 0.70); however, there was potential to distinguish between Adequate and Excellent quality channels, with a support vector classifier identifying Adequate channels with 84.7% precision and 72.1% recall.
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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 | 0.007 | 0.025 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 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".