  To achieve seizure freedom, epilepsy surgery requires the complete resection
of the epileptogenic brain tissue. In intraoperative ECoG recordings, high
frequency oscillations (HFOs) generated by epileptogenic tissue can be used to
tailor the resection margin. However, automatic detection of HFOs in real-time
remains an open challenge. Here we present a spiking neural network (SNN) for
automatic HFO detection that is optimally suited for neuromorphic hardware
implementation. We trained the SNN to detect HFO signals measured from
intraoperative ECoG on-line, using an independently labeled dataset. We
targeted the detection of HFOs in the fast ripple frequency range (250-500 Hz)
and compared the network results with the labeled HFO data. We endowed the SNN
with a novel artifact rejection mechanism to suppress sharp transients and
demonstrate its effectiveness on the ECoG dataset. The HFO rates (median 6.6
HFO/min in pre-resection recordings) detected by this SNN are comparable to
those published in the dataset (58 min, 16 recordings). The postsurgical
seizure outcome was "predicted" with 100% accuracy for all 8 patients. These
results provide a further step towards the construction of a real-time portable
battery-operated HFO detection system that can be used during epilepsy surgery
to guide the resection of the epileptogenic zone.
