Stochastic Signal Processing Based Stimulation Artifact Cancellation in ΔΣ Neural Frontend.

Gayas Mohiuddin Sayed, Armin Bartels, Daniel De Dorigo, Tim Fleiner, Nicole Rosskothen-Kuhl, Matthias Kuhl
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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.

基于随机信号处理的ΔΣ神经前端刺激伪影消除。
本文提出了一种采用随机域自适应LMS滤波器消除电刺激伪影的神经记录器前端。记录系统包括低噪声模拟前端和一阶ΔΣ调制器。一个节能的随机信号处理器,占用0.12 mm2的面积,处理ΔΣ调制器输出比特流,以学习和补偿并发电刺激引起的伪影。该方法在180nm CMOS工艺的ASIC原型上得到验证,总功耗为6.83 μW,随机信号处理器功耗仅为0.51 μW。实验结果表明,该系统在10 kHz带宽内有效抑制200 mV的峰对峰刺激伪影约33 dB,使其成为一种新型的最先进的实时伪影消除系统。此外,双相和单相刺激的体外验证证实了其有效性,双相刺激产生的74.3 mVpp伪影被减弱了25 dB。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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