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 mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".