Hardware Efficient Automatic Thresholding for NEO-Based Neural Spike Detection

The nonlinear energy operator (NEO) algorithm has been commonly implemented in hardware for neural spike detection. However, the traditional method to set the threshold is sensitive to the spike firing rate. In this paper, a new approach is presented to automatically set the threshold, in real time, in a manner that is robust to the spike firing rate and suitable for a neural implant. The presented threshold calculation method statistically analyzes the neural signal standard deviation and root-mean-square frequency and can update the threshold of each channel sequentially every few seconds. Hardware efficient architectures to estimate the threshold calculation statistical parameters are also presented. This automatic thresholding method for NEO spike detection shows robust performance for firing rates from 10 to 100, occupies only 0.021 mm2 in 130 nm CMOS, and consumes only 50 nW in simulations with a 20-kHz clock.

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