Modular Flexible 80-dB-DR Artifact-Resilient EEG Headset with Distributed Pulse-Based Feature Extraction and Multiplier-Less Neuromorphic Boosted Seizure Classifier
Why this work is in the frame
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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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ). 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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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it