Modular Flexible 80-dB-DR Artifact-Resilient EEG Headset with Distributed Pulse-Based Feature Extraction and Multiplier-Less Neuromorphic Boosted Seizure Classifier
Bibliographic record
Abstract
Wearable EEG headsets have shown potential to transform outpatient diagnostics by providing real-time insights into brain neurological activity, allowing for more accurate treatment plans. For most diagnostic applications, energy-efficient design is crucial due to the need for long-term recording. Diagnostic headsets typically consist of multiple active electrodes (AE) with embedded electronics for amplification and/or quantization, connected to a central back-end (BE) unit responsible for data processing and, if necessary, wireless transmission. As shown in Fig. 1 (top, left), a review of the state of the art reveals that in systems with a sufficiently-high dynamic range (DR) analog front-end (AFE) [1] and a data-driven classifier (e.g., a nonlinear support vector machine (NL-SVM) [2]) for seizure detection, power consumption is mainly dominated by the AFE (47.6%), AE-to-BE data communication (26.5%), and signal processing for seizure detection (20.6%). This emphasizes the need for a holistic approach to enhance the efficiency of all these major components for an overall energy-efficient design.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".