The design of 8-channel CMOS area-efficient low-power current-mode analog front-end amplifier for EEG signal recording
Bibliographic record
Abstract
In this paper, an 8-channel area-efficient low-power current-mode analog front-end amplifier (AFEA) is designed for EEG signal recording. The AFEA is composed of eight capacitive coupled transconductors (CCGMs), current-mode band-pass filters (CMBPFs), and programmable current-gain amplifiers (PCGAs) with a multiplexer (MUX), a transimpedance amplifier (TIA), and an offset current cancellation loop (OCCL). The AFEA employs CCGM with only 2pF input capacitance to eliminate the electrode dc offset (EDO). The current-mode topology is adopted in the design of CCGMs, CMBPF s, and PCGAs to reduce the power consumption. The shared OCCL is designed to eliminate the output offset of CCGM, CMBPF and PCGA. The AFEA is designed and fabricated in 180-nm CMOS technology and the core area occupies only 1mm2. The measured maximum gain is 82 dB. The measured input-referred noise is 3.34μVrms within the bandwidth of 0.5-100 Hz. The measured maximum power consumption is 7.85 μW per channel under power supply of 1.2 V. The fabricated AFEA is applied to record the human EEG signal successfully.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".