Low noise and high CMRR front-end amplifier dedicated to portable EEG acquisition system
Bibliographic record
Abstract
This paper concerns the design and implementation of a fully integrated low noise and high CMRR rail-to-rail preamplifier dedicated to EEG acquisition channel. The preamplification technique is based on two complementary CMOS True Logarithmic Amplifier (TLA) stages connected in parallel. The TLA largely amplifies small amplitude of EEG signals, and moderately the large amplitude ones created during epileptic. A chopper stabilization technique is used to filter the 1/ƒ noise and the DC offset voltage of the input CMOS transistors and to increase the common-mode rejection ratio (CMRR). Due to the TLA structure, a high CMRR and high power supply rejection ratio are achieved and the signal-to-noise ratio (of the channel is better enhanced). To snugly fit the ADC input window to the EEG signal magnitude a new programming gain approach is implemented. Also, a chopper spike filter is used to cancel the spike voltages generated by the charge injections of modulator/demodulator switches. The proposed preamplifier is implemented in 0.18 µm CMOS technology. Post-layout simulation results exhibit 253 dB @50/60 Hz as CMRR, 500 nVrms @100 Hz as input-referred noise while consuming 55 µA from a 1.8 V supply.
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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) | 0.009 | 0.004 |
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".