Wearable wireless EEG acquisition system with active dry electrode sensor

HUANG Yong; GUO Xu-hong; YAO Zhao-lin; PEI Wei-hua; WANG Yi-jun; CHEN Hong-da

doi:10.13873/J.1000-9787(2017)04-0080-04

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Wearable wireless EEG acquisition system with active dry electrode sensor

更新时间：2025-09-11

- Wearable wireless EEG acquisition system with active dry electrode sensor
- Vol. 36, Issue 4, Pages: 80-83(2017)
- 作者机构：
  
  中国科学院半导体研究所集成光电子学国家重点联合实验室脑科学与智能技术卓越创新中心
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2017)04-0080-04
  CLC： R741.044;TP212
- Published：2017
- 稿件说明：
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[1]黄涌,郭旭宏,姚兆林,裴为华,王毅军,陈弘达.基于主动干电极传感器的无线可穿戴脑电采集系统[J].传感器与微系统,2017,36(04):80-83.

HUANG Yong, GUO Xu-hong, YAO Zhao-lin, et al. Wearable wireless EEG acquisition system with active dry electrode sensor[J]. 2017, 36(4): 80-83.
[1]黄涌,郭旭宏,姚兆林,裴为华,王毅军,陈弘达.基于主动干电极传感器的无线可穿戴脑电采集系统[J].传感器与微系统,2017,36(04):80-83. DOI： 10.13873/J.1000-9787(2017)04-0080-04.

HUANG Yong, GUO Xu-hong, YAO Zhao-lin, et al. Wearable wireless EEG acquisition system with active dry electrode sensor[J]. 2017, 36(4): 80-83. DOI： 10.13873/J.1000-9787(2017)04-0080-04.

摘要

为了减少脑电信号（EEG）采集系统的复杂性、实验前长时间繁琐的准备过程

以及改善采集系统的抗干扰性、可穿戴性和舒适性

设计了一种带有8通道主动干电极传感器的无线可穿戴脑电采集系统

具有24位ADC

109 d B信噪比

±500 m V/10 Hz的交流输入范围。实验结果表明:该系统和商业系统记录的脑电图波形之间的相关系数为0.89

增加了可穿戴无线采集系统的可靠性

改善穿戴的舒适性。采用该系统可实现脑电的静态记录和基于稳态视觉诱发（SSVEP）的脑—机接口（BCI）应用。

Abstract

In order to reduce complexity of Electrophysiological signal collection system and long complicated preparation process before experiment

present a wireless wearable EEG acquisition system with 8-channel

active dry electrode sensor

24-bit ADC

109 d B SNR

± 500 m V/10 Hz AC input range

which improves anti-interference performance

increases comfort of wearing

and makes it suitable for wearable applications. Experimental results show that the correlation coefficient between the EEG waveform recorded by proposed systems and that by commercial systems is 0. 89. This system can be used for BCI applications based on the EEG recording and static steady-state visual evoked（ SSVEP）.

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references

Extracting neurophysiological signals reflecting users’ emotional and affective responses to BCI use: A systematic literature review [J] . Giulia Liberati,Stefano Federici,Emanuele Pasqualotto. NeuroRehabilitation . 2015 (3)

Brain–machine interface in chronic stroke rehabilitation: A controlled study [J] . Ander Ramos‐Murguialday,Doris Broetz,Massimiliano Rea,Leonhard L?er,?zge Yilmaz,Fabricio L. Brasil,Giulia Liberati,Marco R. Curado,Eliana Garcia‐Cossio,Alexandros Vyziotis,Woosang Cho,Manuel Agostini,Ernesto Soares,Surjo Soekadar,Andrea Caria,Leonardo G. Cohen,Niels Birbaumer. Ann Neurol. . 2013 (1)

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