Modular DR- and CMR-Boosted Artifact-Resilient EEG Headset With Distributed Pulse-Based Feature Extraction and Neuro-Inspired Boosted-SVM Classifier
Bibliographic record
Abstract
This article presents the design, development, and experimental testing of a flexible, modular electroencephalography (EEG) headset for long-term epilepsy monitoring and individualized treatment. System- and circuit-level techniques are employed to improve energy efficiency while ensuring high-quality EEG recordings and accurate seizure detection. The wearable prototype includes digital active electrodes (DAEs) for high-dynamic-range (DR) recording, motion artifact removal (MAR), and feature extraction (FE), along with a central backend (BE) for patient-specific classification, wireless connectivity, and common-mode rejection (CMR) boosting. DAEs communicate through a time-shared data bus, minimizing wires and enabling flexible electrode placement, maximizing system scalability. Each DAE enhances recording quality with: 1) calibrated CMR boosting (>80-dB common-mode rejection ratio (CMRR) with 1-$M\Omega $AE-to-AE mismatch); 2) SC notch filtering for power-line noise; 3) real-time electrode-tissue impedance (ETI) measurement for MAR and dc correction; and 4) an autoranging mechanism with 17-dB DR enhancement. In-AE FE cuts AE-to-BE communication power by 99.1%, while pulse-based frequency sampling reduces FE power by 92.1%. A neuromorphic multiplier-less adaptively boosted support vector machine (SVM) maintains high detection accuracy with 97.6% less classification power than conventional designs. The chip was implemented in 180-nm CMOS, and the wearable system components (DAE, wireless, and CMR boards) were miniaturized. Experimental testing showed IIRN ($0.64~\mu V_{\text {rms}}$, 0.5–100 Hz), adjustable gain/bandwidth, DR (80 dB), SNDR (74.5 dB), and CMRR (89.2 dB without mismatch, >80 dB with mismatch). Measurement results also confirm the system’s effectiveness in motion artifact estimation and removal. In vivo measurements demonstrate the system’s efficacy and latency in detecting neurologically relevant events. Seizure detection results (96.4% sensitivity, 0.41 FPR, 1-s latency, zero SRAM usage) on prerecorded EEG data from 21 patients are also reported. The system is compared to state-of-the-art EEG recording and seizure detection systems, highlighting its advantages.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".