Modular High-DR Artifact-Resilient Wearable EEG Headset with Distributed Pulse-Based Feature Extraction and Multiplier-Less Neuromorphic Boosted Seizure Detection

Wearable electroencephalography (EEG) headsets have emerged as a promising technology with the potential to revolutionize outpatient diagnostics. These devices offer real-time insights into the neurological activity of the brain, paving the way for more precise and personalized treatment strategies. One of the critical factors in the success of these diagnostic headsets is their energy-efficient design, especially for long-term recording applications.

Typically, diagnostic headsets consist of multiple active electrodes (AEs) equipped with embedded electronics for functions like amplification and quantization. These electrodes are connected to a central back-end (BE) unit responsible for data processing and, if needed, wireless transmission. A comprehensive analysis of the current state of the art in this field reveals that power consumption in systems with a sufficiently high dynamic range (DR) analog front-end (AFE) and a data-driven classifier, such as a nonlinear support vector machine (NL-SVM) for seizure detection, is mainly dominated by three main components: the AFE (48%), AE-to-BE data communication (26%), and the signal processing required for seizure detection (21%). This underscores the necessity of taking a holistic approach to enhance the efficiency of these key components for an overall energy-efficient design.

This study presents design, development and experimental characterization of a channel count- scalable modular fully-flexible platform which achieves motion-artifact-resilient 80 dBDR EEG recording and highly-accurate, energy-efficient seizure detection. The innovation lies in a series of strategies that collectively result in a remarkable 72% reduction in overall power consumption: 1) performing feature extraction (FE) in each AE (i.e., distributed) resulting in 99% reduction in AE-to-BE data communication power, 2) using an ultra-low-power pulse-based method for FE resulting in 92% reduction in its power, 3) performing classification using a neuromorphic multiplier-less boosted-SVM that achieves a comparable accuracy to a resource-intensive NL-SVM while consuming 98% less power, 4) adopting an auto-ranging scheme in the AFE that leads to >17dB DR improvement, 5) employing an AE-specific-boosted distributed feed-forward common-mode rejection (CMR) enhancement method achieving >80dB CMRR in the presence of 1MΩ AE-to-AE input impedance (Zin) mismatch (89dB without mismatch), and 6) an AE-specific electrode-tissue impedance (ETI)-correlation-based circuit for motion artifact detection and suppression.