A low power Dirac basis compressed sensing framework for EEG using a Meyer wavelet function dictionary
Bibliographic record
Abstract
The energy required for Electroencephalography (EEG) transmission at the sensor node, in Wireless Body Area Networks (WBANs), is high relative to the sensor's battery size. The power consumption is dominated by sensing, processing and data transmission of the EEG. Many successful solutions based on Compressed Sensing (CS) and other techniques have been proposed. The transmission of a lower number of bits elongates the battery life of the sensor but results in high errors in the reconstructed signals at the receiver side. In this paper, we use CS to reduce the energy at the sensor. We also acquire the signal using a random sampling circuit (simulating a Dirac sensing matrix) which eliminates the use of a microcontroller at the sensor node. At the receiver end, we propose to recover the signal using a basis dictionary, which has a multiple band-pass Meyer wavelet. This is shown to result in low signal recovery error. The proposed dictionary promotes optimal sparse basis and optimal incoherence with the Dirac sensing Matrix. The proposed technique is shown to achieve low decompression errors at high compression rates such as 10:1. It also achieves ultra-low power consumption as the Meyer wavelet is incoherent with the random Dirac basis used for sampling. We show an analysis that indicates significant power consumption reduction.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".