A Neural-ADC-Compatible Fully-Dynamic Lossless Adaptive Resolution Compression Technique for Energy-Constrained Bio-Signal Recording
Bibliographic record
Abstract
This paper presents a lossless adaptive-resolution compression technique intended for energy-constrained implantable neural interface microsystems. The proposed method seamlessly integrates with the widely-adopted ADC-direct front-end architectures with minimal overhead and no need for architectural modification, making it universally-compatible. It maintains an always-Nyquist sampling rate and employs a single-bit quantizer to overcome the limitations of previously-reported adaptive sampling and adaptive resolution methods (e.g., missing short-time events, nonlinearity, power/area overdesign). It also features continuous DC variation calculation and adjustable hysteresis bands. The method is optimized and validated through MATLAB simulations, followed by synthesis and implementation in a standard 130nm CMOS process, resulting in an active area of 0.0529 mm2and total power consumption of 1.9µW @ 128kHz. Post-layout verification was conducted using prerecorded EEG and ECG datasets, demonstrating a minimum compression ratio of 83.75% of the theoretical maximum (i.e., $\frac{{{N_H}}}{{{N_L}}}$), highlighting its effectiveness for implantable neural recording devices.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".