Chopped Logarithmic Programmable Gain Amplifier intended to EEG acquisition interface
Bibliographic record
Abstract
This paper concerns the design and implementation of a new fully integrated Chopped Logarithmic Programmable Gain Amplifier (CLPGA) intended for a front-end EEG acquisition interface. The proposed front-end has low-input referred noise and high-common mode rejection ratio (CMRR) compared to Instrumentation Amplifier features, and its rail-to-rail topology allows electrode offset rejection. The logarithmic amplification block is composed of three cascaded true logarithmic amplification stages. Also, a chopper stabilization technique is used to improve the noise figure. This front-end interface is followed by an analog to digital convertor, and in order to prevent EEG signal distortion, the magnitude of the later signal is controlled by implementing new programming gain approach. Post-layout simulation in 0.18 μm CMOS technology demonstrates a High CMRR of 284 dB @50/60 Hz, an input referred noise of ~0.5 mVrs on 100 Hz BW and an input common mode ranges from 0.6 to 1.12 V for 1.8 V supply. The measured power consumption is 1.2 mW and the effective CLPGA area is 0.5 mm2 including the digital part needed for programming the gain.
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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 | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".