Stochastic Signal Processing Based Stimulation Artifact Cancellation in $\Delta\Sigma$ Neural Frontend

Abstract

This paper presents a neural recorder frontend featuring electrical stimulation artifact cancellation by employing an adaptive LMS filter in the stochastic domain. The recording system comprises of a low-noise analog frontend and a 1^st-order $\Delta\Sigma$ modulator. A power-efficient stochastic signal processor, occupying an area of 0.12 mm², processes the $\Delta\Sigma$ modulator output bitstream to learn and compensate for artifacts induced by concurrent electrical stimulation. The proposed approach, validated on a prototype ASIC fabricated in 180 nm CMOS technology, has a total power consumption of 6.83 $\boldsymbol{\mu}$W, with the stochastic signal processor consuming only 0.51 $\boldsymbol{\mu}$W. Experimental results demonstrate that the system effectively suppresses peak-to-peak stimulation artifacts of 200 mV by approximately 33 dB over a 10 kHz bandwidth, establishing it as a novel state-of-the-art real-time artifact cancellation system. Furthermore, in-vitro validation for both biphasic and monophasic stimulation confirms its efficacy, with 74.3 mVpp artifacts from biphasic stimulation being attenuated by 25 dB.