A low-cost reliable online noise level estimation for accurate spike detection in extracellular recordings

Abstract

Abstract-On-line unsupervised spike detection is a fundamental building block for autonomous neural implants. To accurately monitor neurological activity, the spike detectors should be able to operate at a wide range of firing rates. This can only be achieved by a careful estimation of instantaneous noise level in the recording. In this paper, we propose a system for estimating noise level in extracellular recordings, which is reliable across a wide range of firing rates, and thus leads to accurate spike detection.

Theoretical study and optimisation of a standard deviation estimator circuit for adaptive threshold spike detection

“…Theoretically, there are various ways of calculating a noise SD , and many of them are easily applied offline. In the context of real‐time and implantable multi‐electrode systems, this calculation must be performed continuously and in parallel for each channel; it should also present both low silicon area and power costs, not only to optimise circuit integration but also to minimise biological tissue warming .…”