A Neuromorphic Device for Detecting HighFrequency Oscillations in Human iEEG Among diagnostic biomarkers, high frequency oscillations in human iEEG are used to identify epileptogenic brain tissue during epilepsy surgery. However, current methods typically analyse the raw data offline using complex timeconsuming algorithms. We developed a compact neuromorphic sensoryprocessing systemonchip that can monitor the iEEG signals and detect high frequency oscillations in realtime using spiking neural networks. To this end, we present an integrated device with an analog frontend that can extract predefined spectral features and encode them as addressevents, and a neuromorphic processor core that implements a network of integrate and fire neurons with dynamic synapses. The device was fabricated using a standard  0.18mumathrmm  CMOS technology node. The estimated power consumption of the frontend is  6.2mumathrmW channel and the areaonchip for a single channel is 0.15 square millimetres. The SNN classifier provides 90.5% sensitivity and 67.7% specificity for detecting high frequency oscillations. This is the first feasibility study towards identifying relevant features in intracranial human data in realtime onchip using eventbase processors.

A 10 MSampleSec Digital Neural Spike Detection for a 1024 Pixels Multi Transistor Array Sensor This paper presents an FPGA implementation of a DSP performing real time spike detection on the electrical activity of an in vitro neuronal culture of rat hippocampi. The DSP enhances the Signaltonoise ratio (SNR) of samples recorded by a 1024 pixels Multi Transistor Array (MTA) at 9375 SamplesSec per pixel of  sim 6 boldsymbolmumathbfm  pitch. The implementation integrates in the same system a Time Division Multiplexing (TDM) filter and a spatiotemporal correlation algorithm, to increase the SNR up to identify spikes as low as  215 boldsymbolmumathbfV_0mathbfPEAK . The digital filter is a 2nd order high pass Infinite input response (IIR) Chebyshev filter. The spatiotemporal correlation exploits the MTA smaller pixels size and the high samplerate to compute an equivalent pixel from a group of 7 pixels and 3 consecutive frames for a total of 21 samples and it is supported by a multichannel noise power estimation. Finally, this paper shows the results achieved on the performed experiments and compares the system with others experiments using different sensors and algorithms.