Modeling and Analysis of Environmental Electromagnetic Interference in Multiple-Channel Neural Recording Systems for High Common-Mode Interference Rejection Performance

Biosensors Pub Date : 2024-07-15 DOI:10.3390/bios14070343
Gang Wang, Chang-Seok You, Chengcong Feng, Wenliang Yao, Zhengtuo Zhao, Ning Xue, Lei Yao
{"title":"Modeling and Analysis of Environmental Electromagnetic Interference in Multiple-Channel Neural Recording Systems for High Common-Mode Interference Rejection Performance","authors":"Gang Wang, Chang-Seok You, Chengcong Feng, Wenliang Yao, Zhengtuo Zhao, Ning Xue, Lei Yao","doi":"10.3390/bios14070343","DOIUrl":null,"url":null,"abstract":"Environmental electromagnetic interference (EMI) has always been a major interference source for multiple-channel neural recording systems, and little theoretical work has been attempted to address it. In this paper, equivalent circuit models are proposed to model both electromagnetic interference sources and neural signals in such systems, and analysis has been performed to generate the design guidelines for neural probes and the subsequent recording circuit towards higher common-mode interference (CMI) rejection performance while maintaining the recorded neural action potential (AP) signal quality. In vivo animal experiments with a configurable 32-channel neural recording system are carried out to validate the proposed models and design guidelines. The results show the power spectral density (PSD) of environmental 50 Hz EMI interference is reduced by three orders from 4.43 × 10−3 V2/Hz to 4.04 × 10−6 V2/Hz without affecting the recorded AP signal quality in an unshielded experiment environment.","PeriodicalId":100185,"journal":{"name":"Biosensors","volume":"54 19","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosensors","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.3390/bios14070343","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Environmental electromagnetic interference (EMI) has always been a major interference source for multiple-channel neural recording systems, and little theoretical work has been attempted to address it. In this paper, equivalent circuit models are proposed to model both electromagnetic interference sources and neural signals in such systems, and analysis has been performed to generate the design guidelines for neural probes and the subsequent recording circuit towards higher common-mode interference (CMI) rejection performance while maintaining the recorded neural action potential (AP) signal quality. In vivo animal experiments with a configurable 32-channel neural recording system are carried out to validate the proposed models and design guidelines. The results show the power spectral density (PSD) of environmental 50 Hz EMI interference is reduced by three orders from 4.43 × 10−3 V2/Hz to 4.04 × 10−6 V2/Hz without affecting the recorded AP signal quality in an unshielded experiment environment.
多通道神经记录系统中环境电磁干扰的建模与分析,实现高共模干扰抑制性能
环境电磁干扰(EMI)一直是多通道神经记录系统的主要干扰源,而针对这一问题的理论研究却少之又少。本文提出了等效电路模型来模拟此类系统中的电磁干扰源和神经信号,并通过分析为神经探针和后续记录电路制定了设计指南,以在保持记录的神经动作电位(AP)信号质量的同时,实现更高的共模干扰(CMI)抑制性能。使用可配置的 32 通道神经记录系统进行了活体动物实验,以验证所提出的模型和设计指南。结果表明,在无屏蔽实验环境中,环境 50 Hz EMI 干扰的功率谱密度(PSD)降低了三个数量级,从 4.43 × 10-3 V2/Hz 降至 4.04 × 10-6 V2/Hz,而不会影响记录的 AP 信号质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信