Beamforming for Powerline Interference in Large Sensor Arrays
Bibliographic record
Abstract
This paper shows how to use beamforming to remove the power-line interference (PLI) in large surface electromyography (sEMG) sensor array or high-density sEMG. The method exploits the highly correlated nature of the different sources of interference, being part of the same electrical grid, and their narrow frequency bands. The idea is to use a very narrow pass-band filter around 50 or 60 Hz to get signals with high PLI content before applying a spatial filtering by principal component analysis (PCA). This way, beamforming are done on the frequency bands where PLI are presents. Also, it ensures that even if the PLI has a smaller overall power than the desired signal, it will be easily found as the most powerful component of the decomposition. The PLI can then be removed from the signal. With trivial modification, harmonics of the PLI can also be removed. The approach was used in the context of muscle behavior analyses of low back pain patients using a sEMG array of 64 sensors. The performances of the filter are studied by experimental and semi-empirical methods. Compared to the usual notch filter, an improvement of up 10 dB is found.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".