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