Bibliographic record
Abstract
High-density electromyography (HD-EMG) arrays are useful for studying muscle activation in a spatial dimension. This paper studies interpolation techniques for the reconstruction of missing or poor quality channels within an HD-EMG array. Linear, cubic, spline, and nearest-neighbour interpolation methods were evaluated for both 1D (linear) and 2D (array) electrode configurations surrounding a target electrode. The quality of interpolations was measured using the percent residual difference and correlation. For basic 2D electrode configurations, the 2D spline method provided the best results. For 1D configurations, spline interpolation with electrodes selected perpendicular to the muscle fiber was preferable. For sparse electrode arrays, obtained by using every other electrode row/column, the best interpolation method was cubic interpolation over 8 electrodes. Spline interpolation was more sensitive to differences between the EMG from the electrodes used in the interpolation and the EMG in the target electrode; differences became more pronounced in the sparse array configurations. Low interpolation quality was associated with both poor quality channels and regions of transition between high and low activity in the electrode array. Results indicate there is a potential to use interpolation to both identify and reconstruct poor channels in HD-EMG arrays.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".