Input Resistance Boosting for Capacitive Biosignal Acquisition Electrodes
Bibliographic record
Abstract
Capacitive electrodes are a promising alternative to conventional wet Ag/AgCl electrodes in the acquisition of biological signals. They consist of a metallic sensing layer covered by an insulating material that contacts the human body. They have the advantage of measuring biopotentials in humans through clothing, hair, and small air gaps. The electrode capacitance creates a high-pass filter with the analog front-end’s (AFE) input resistance. Hence, the bandwidth of the system, especially the low cut-off frequency, depends on the dielectric layers and the characteristics of the body-electrode contact. Moreover, capacitive electrodes suffer from motion artifacts (MAs) that also modify the electrode capacitance. This article proposes an electrode topology with boosted input resistance and compensation for the electrode capacitance changes. To achieve such characteristics, the proposed circuit comprises a negative impedance converter (NIC) that increases the input resistance, which allows the addition of a capacitor in series with the electrode capacitance to reduce the effects of capacitance changes. The proposed electrode’s cut-off frequency was investigated in a controlled test bench. For the worst case scenario of electrode capacitance (1 pF), the proposed topology achieved a cut-off frequency of 1.5 Hz while the reference circuit had a cut-off frequency of 72 Hz. The proposed topology also outperformed the reference electrode in common-mode rejection ratio (CMRR) and through clothing ECG acquisition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".