Automatic validation of motor unit potential trains extracted by EMG signal decomposition
Bibliographic record
Abstract
A novel method is proposed to assess the validity of motor unit potential trains (MUPTs) obtained during electromyographic (EMG) signal decomposition. The algorithm evaluates a MUPT in terms of motor unit potential (MUP) shape homogeneity, motor unit (MU) firing pattern consistency, and the estimated level of false classification errors in the MUPT. Performance metrics based on simulated EMG signals showed high accuracy for detecting invalid MUPT, similar to those expected to be created by a decomposition algorithm (>;93%), as well as valid MUPTs (98%). Compared to methods that evaluate only MUP shape consistency, the presented methods improved accuracy by 57.6% for detecting invalid MUPTs composed of MUPTs with similar-shaped MUP templates. For invalid sparse MUPTs, the accuracy of the presented method was 40% higher than that of the methods that use only MU firing pattern information.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".