Analog-based Compressive Sensing of Multichannel Neural Signals: Systematic Design Approaches
Bibliographic record
Abstract
Compressive Sensing (CS) is an emerging data compression method in the neurorecording application to decrease the transferring data rate as well as the power consumption. It can be implemented in both analog and digital domain, but analog has the potential of reducing the power more due to decreasing the sampling frequency in frontend. In analog, CS is usually achieved by switched capacitor circuits. Non-ideal specifications of Operational Transconductance Amplifier (OTA) of CS integrator such as finite gain, bandwidth, slew rate and output swing induce error and reduce the total SNR. In this paper, we simulate these non-idealities in Matlab and Simulink with the assumption that all other elements in this system are ideal. The results demonstrate that the SNR of the whole system is very sensitive to the gain, bandwidth and output swing of OTA. In other words, the bottleneck to achieve a high SNR in the neurorecording system is the CS encoder. For neurorecording implant applications, CS is mainly achieved with reasonable reported SNR between 8 and 24 dB. However, we can obtain an improved CS recording performance using main blocks, i.e. LNA and ADC with much relaxed specifications in order to reduce the power consumption as well as the silicon area.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".