The Contribution of Motor Unit Pairs to the Correlation Functions Computed From Surface Myoelectric Signals
Bibliographic record
Abstract
The contribution of motor unit action potential trains (MUAPT) of distinct motor units (MU) to the crosscorrelation function between myoelectric signals (MES) recorded at the skin surface is studied. In specific, the significance of the correlation between the firing activity of concurrently active MUs (which results in cross-terms in the overall correlation function) is compared to the representation obtained using the contributions of single MUs at each recording site (auto-terms). A model for the generation of surface MUAPs is combined with the generation of MU firing statistics in order to obtain surface MUAPTs. MU firing statistics are simulated to incorporate MU synchronization levels reported in the literature. Alternatively, experimental firing statistics are fed to the model generating the MUAPTs. The contribution of individual MU pairs to the global myoelectric signal correlation function is assessed. Results indicate that the cross-terms from different MUs decrease steadily contributing very little to the overall correlation for record lengths as short as 30 s. Thus, the error expected when computing the crosscorrelation function between two channels of MES as the superposition of the auto-terms contributed by single MUs (i.e., ignoring the cross-terms from different MUs) is shown to be very small.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".