|本期目录/Table of Contents|

 基于Chirplet变换的变频视觉诱发电位脑-机接口研究(PDF)

《纳米技术与精密工程》[ISSN:1672-6030/CN:12-1351/O3]

期数:
2014年3期
页码:
157-161
栏目:
精密测量
出版日期:
2014-05-15

文章信息/Info

Title:
 Chirp Stimuli Visual Evoked Potential Based Brain-Computer

Interface by Chirplet Transform Algorithm

作者:
 张力新1 贾义红1 许敏鹏1 綦宏志1 赵欣1 何峰1 万柏坤1 焦学军2 明东1
 (1.天津大学精密仪器与光电子工程学院,天津300072;

2.中国航天员科研训练中心人因工程重点实验室,北京 100094)

Author(s):
 Zhang Lixin1 Jia Yihong1 Xu Minpeng1 Qi Hongzhi1 Zhao Xin1 He Feng1

Wan Baikun1 Jiao Xuejun2 Ming Dong1

 (1.School of Precision Instrument and OptoElectronics Engineering, Tianjin University, Tianjin 300072,China;

2.National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing 100094, China)

关键词:
 脑-机接口 变频视觉诱发电位 Chirplet变换 支持向量机
Keywords:
 brian-computer interfacechirp stimuli visual evoked potentialchirplet transformsupport vector machine
分类号:
TP334.7
DOI:
10.13494/j.npe.20140001
文献标识码:
A
摘要:
 脑-机接口(braincomputer interface,BCI)是在大脑与外部设备间建立一个直接的信息交流通路,它无须依赖外周神经肌肉系统而仅通过脑电信号特征提取与模式识别来实现思维表达或指令操作.变频视觉诱发电位(chirp stimuli visual evoked potential,ChirpVEP)是最近提出的一种脑电诱发新模式,可作为BCI控制信号,极富应用潜力.然而Chirp-VEP的诱发条件、信号处理、特征提取方法等都缺乏充分研究.本文采用不同起始频率和chirp调频率进行了Chirp-VEP诱发实验,利用Chirplet变换(chirplet transform,CT)等4种时频分析方法提取了Chirp-VEP信号特征.研究结果表明,相较于其他时频分析方法,CT可获得更高的VEP信噪比与正确识别率.在8名受试者参加的在线BCI测试中,Chirp-VEP的总平均正确识别率高达97.8%,进一步验证了Chirp-VEP应用于BCI控制的潜力.
Abstract:
 A brain-computer interface (BCI) is to set up a direct communication pathway between the brain and an external device. It realizes the human thinking expression or command operation through feature extraction and pattern recognition of electroencephalography(EEG) rather than relying on the perpheral nerve muscle system. The newly proposed chirp stimuli visual evoked potential(Chirp-VEP) can be used as a most promising BCI control signal. However, the inducing conditions, signal processing, feature extraction methods of Chirp-VEP need comprehensive study. Different initial frequencies and chirp rates were adopted to evoke the Chirp-VEP in this paper. Four kinds of time-frequency analysis methods, such as chirplet transform(CT) and so on were used to extract the timefrequency characteristics of ChirpVEP signals. The results show that compared with the other three methods, CT can obtain higher VEP signal-to-noise ratio and accuracy recognition rate. Eight subjects participated in the online BCI testing, the total average accuracy recognition rate was up to 97.8%. It further verifies the potential of Chirp-VEP application in BCI control.

参考文献/References

-

备注/Memo

备注/Memo:
收稿日期: 2014-03-18.
基金项目: 国家自然科学基金资助项目 (81222021, 31271062, 61172008, 81171423, 51007063); 国家科技支撑计划资助项目 (2012BAI34B02);教育部新世纪优秀人才支持计划资助项目(NCET-10-0618).
作者简介: 张力新(1963— ),男,研究员,lxzhang@tju.edu.cn
通讯作者: 綦宏志,qhz@tju.edu.cn.
更新日期/Last Update: 2014-08-14