Wavelet Transforms Dedicated to Compress Recorded ENGs from Multichannel Implants: Comparative Architectural Study
Bibliographic record
Abstract
Bandwidth of wireless multichannel neural recording systems is one of the most significant limitation to increase the number of channels monitored. Data compression is being efficiently used to process multichannel recordings. This paper explores discrete wavelet transform (DWT) processor architectures suited to compress ENGs and so, increase the number of channels. Low power consumption, low silicon area and specificity of multichannel neural recording systems are considered for this investigation. Six architectures were implemented and compared. All of them implement a 3 level Daubechies-4 wavelet decomposition. This comparative study allows to conclude that an excellent trade-off between power consumption and silicon area is obtained through a DWT polyphase structure using a careful balance of parallelism and folding. Also, it arises that multiplexing several channels toward a shared DWT processor provides the best savings for both, power and 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".