Neural mechanisms of intermuscular coherence: implications for the rectification of surface electromyography
Bibliographic record
Abstract
Oscillatory activity plays a crucial role in corticospinal control of muscle synergies and is widely investigated using corticospinal and intermuscular synchronization. However, the neurophysiological mechanisms that translate these rhythmic patterns into surface electromyography (EMG) are not well understood. This is underscored by the ongoing debate on the rectification of surface EMG before spectral analysis. Whereas empirical studies commonly rectify surface EMG, computational approaches have argued against it. In the present study, we employ a computational model to investigate the role of the motor unit action potential (MAUP) on the translation of oscillatory activity. That is, diverse MUAP shapes may distort the transfer of common input into surface EMG. We test this in a computational model consisting of two motor unit pools receiving common input and compare it to empirical results of intermuscular coherence between bilateral leg muscles. The shape of the MUAP was parametrically varied, and power and coherence spectra were investigated with and without rectification. The model shows that the effect of EMG rectification depends on the uniformity of MUAP shapes. When output spikes of different motor units are convolved with identical MUAPs, oscillatory input is evident in both rectified and nonrectified EMG. In contrast, a heterogeneous MAUP distribution distorts common input and oscillatory components are only manifest as periodic amplitude modulations, i.e., in rectified EMG. The experimental data showed that intermuscular coherence was mainly discernable in rectified EMG, hence providing empirical support for a heterogeneous distribution of MUAPs. These findings implicate that the shape of MUAPs is an essential parameter to reconcile experimental and computational approaches.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".