56-channel direct-coupled chopper-stabilized EEG monitoring ASIC with digitally-assisted offset correction at the folding nodes
Bibliographic record
Abstract
We present a 56-channel neural recording interface with a chopper-stabilized DC-coupled front-end and a programmable mixed-signal DC cancelation feedback. Each recording channel has a fully-differential amplifier with 51-54dB of gain, an input-referred noise of 5μVrmsintegrated from 10Hz to 5kHz and a CMRR of 65dB. Input DC-coupling allows for a simple chopping scheme without the area overhead of large capacitors and extra non-idealities compensation circuitry. Chopping is used to reduce the integrated input-referred noise from 7.5μVrmsto 4.2μVrmsover the bandwidth of 1Hz to 1KHz. Each channel contains a digital integrator and a 16-bit delta-sigma DAC in the feedback in order to cancel input DC offsets of up to α50mV and suppress the low frequency drift. Compensating the input DC offset at the folding node of the OTA provides an input-referred noise that is independent of the DC offset value. The recorded data by the array is digitized by 8 column-parallel SAR ADCs with 8-bit resolution and ENOB of 6.6 bits. Each channel in the neural recording array occupies 0.018mm2. The 8.7mm2die, fabricated in a 0.13μm CMOS process, dissipates 1.07mW from a 1.2V supply. The integrated circuit has been validated in vivo in online intracranial EEG recording in freely moving rats.
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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 | 0.000 | 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) | 0.007 | 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".