[1]潘赛虎,叶志雄,糜超,等.八通道视觉诱发脑电采集系统设计及实现[J].常州大学学报(自然科学版),2016,(05):69-73.[doi:10.3969/j.issn.2095-0411.2016.05.012]
　PAN Saihu,YE Zhixiong,MI Chao,et al.Design and Implementation of Eight Channels Visual Evoked EEG Signals Acquisition System[J].Journal of Changzhou University(Natural Science Edition),2016,(05):69-73.[doi:10.3969/j.issn.2095-0411.2016.05.012]
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八通道视觉诱发脑电采集系统设计及实现()
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常州大学学报(自然科学版)[ISSN:2095-0411/CN:32-1822/N]

卷:
期数:
2016年05期
页码:
69-73
栏目:
计算机与信息工程
出版日期:
2016-09-30

文章信息/Info

Title:
Design and Implementation of Eight Channels Visual Evoked EEG Signals Acquisition System
作者:
潘赛虎; 叶志雄; 糜超; 陈悦
常州大学 信息科学与工程学院,江苏 常州 213164
Author(s):
PAN Saihu;  YE Zhixiong;  MI Chao;  CHEN Yue
School of Information Science and Engineering, Changzhou University, Changzhou 213164, China
关键词:
脑电采集;  模拟前端;  运动想象脑电范式;  信号处理
Keywords:
EEG acquisition;  analog front end;  motor imagery EEG paradigm;  signal processing
分类号:
TP 274.2
DOI:
10.3969/j.issn.2095-0411.2016.05.012
文献标志码:
A
摘要:
针对脑电信号的低信噪比和脑电采集系统的发展要求,设计了基于TI公司ADS1299的脑电信号采集系统,能便捷有效地采集视觉诱发脑电信号。主要通过24位高精度模拟前端ADS1299将脑电信号转为数字信号,最后经USB转串口模块传至上位机; 上位机使用JAVA编程对脑电信号进行实时显示并存储; 设计了左右手运动想象脑电范式程序,能记录刺激呈现时间、刺激类型及行为数据。对采集到的左右手运动想象视觉诱发脑电信号进行特征提取及分类研究,采用BP神经网络的分类率可以达到70%。
Abstract:
In order to solve the problem of low SNR in EEG and meet the requirement of the development of EEG acquisition system, a system is designed to acquire visual evoked EEG signals, which is based on chip ADS1299 of TI Company. Original signals are filtered and conditioned through preprocessing circuit, and then the preprocessed EEG signals are converted to digital signals through 24-bit high-precision chip ADS1299, finally, the digital signals are transmitted to PC via a USB to serial module; The PC software is programmed by Java to realize the real-time display and storage of EEG signals; Left and right hand motor imagery paradigm is designed to record presentation time, simulation type and behavioral data. Feature extraction and classification methods are used on the left and right hand motor imagery visual evoked EEG signals. A BP neural network classifier is used in this paper and the classification rate is as high as 70%.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2015-05-19。基金项目:国家自然科学基金资助项目(61201096); 常州市科技资助项目(CE20145055); 江苏省“青蓝工程”资助项目(苏教师[2014]23号)。作者简介:潘赛虎(1974—),男,江苏常州人,硕士,讲师,主要从事电子信息工程、自动控制研究。
更新日期/Last Update: 2016-10-10