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In this work, we present a neuromorphic system that combines for the firsttime a neural recording headstage with a signal-to-spike conversion circuit anda multi-core spiking neural network (SNN) architecture on the same die forrecording, processing, and detecting High Frequency Oscillations (HFO), whichare biomarkers for the epileptogenic zone. The device was fabricated using astandard 0.18$\mu$m CMOS technology node and has a total area of 99mm$^{2}$. Wedemonstrate its application to HFO detection in the iEEG recorded from 9patients with temporal lobe epilepsy who subsequently underwent epilepsysurgery. The total average power consumption of the chip during the detectiontask was 614.3$\mu$W. We show how the neuromorphic system can reliably detectHFOs: the system predicts postsurgical seizure outcome with state-of-the-artaccuracy, specificity and sensitivity (78%, 100%, and 33% respectively). Thisis the first feasibility study towards identifying relevant features inintracranial human data in real-time, on-chip, using event-based processors andspiking neural networks. By providing "neuromorphic intelligence" to neuralrecording circuits the approach proposed will pave the way for the developmentof systems that can detect HFO areas directly in the operation room and improvethe seizure outcome of epilepsy surgery.

An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG