Stochastic Signal Processing Based Stimulation Artifact Cancellation in ΔΣ 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 1st-order ΔΣ modulator. A power-efficient stochastic signal processor, occupying an area of 0.12 mm2, processes the ΔΣ 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 μW, with the stochastic signal processor consuming only 0.51 μW. 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.