[1]苏少军,方慧娟.一种运动想象异步BCI的空闲状态检测方法[J].华侨大学学报(自然科学版),2013,34(4):390-394.[doi:10.11830/ISSN.1000-5013.2013.04.0390]
　SU Shao-jun,FANG Hui-juan.A Detection Method of Idle State in Motor Image Asynchronous BCI[J].Journal of Huaqiao University(Natural Science),2013,34(4):390-394.[doi:10.11830/ISSN.1000-5013.2013.04.0390]
点击复制

一种运动想象异步BCI的空闲状态检测方法()
分享到：

《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第34卷
期数:
2013年第4期
页码:
390-394
栏目:
出版日期:
2013-07-20

文章信息/Info

Title:
A Detection Method of Idle State in Motor Image Asynchronous BCI
文章编号:
1000-5013(2013)04-0390-05
作者:
苏少军;  方慧娟
华侨大学 信息科学与工程学院, 福建 厦门 361021
Author(s):
SU Shao-jun;  FANG Hui-juan
College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
关键词:
异步脑-机接口;  运动想象;  空闲状态;  分类器;  脑电信号;  贝叶斯线性判别方法
Keywords:
asynchronous brain-computer interface;  motor imagery;  idle state;  classifier;  electroencephalogram;  Bayesian linear discriminant analysis method
分类号:
TP391.4
DOI:
10.11830/ISSN.1000-5013.2013.04.0390
文献标志码:
A
摘要:
提出一种利用特殊运动想象训练样本与有限的空闲状态训练样本进行训练的方法, 采用公共空间频率模式算法与功率谱密度算法分别提取样本的空域与频域上的特征.利用贝叶斯线性判别方法进行分类,设计出一种适用于异步脑-机接口(BCI)的具有两级分类结构的分类器.实验结果表明:该方法对包含空闲状态的脑电信号具有较好的分类效果;在利用较少电极的情况下,测试集样本的分类结果的正确率和均方误差分别为77.62%和0.495.
Abstract:
This paper proposed a method which used training samples of special motor imagery and limited idle state for training. Using common spatio-spectral pattern(CSSP)and power spectral density algorithm to extract the spatial and frequency domain features of the samples as training samples of classifier. Using Bayesian linear discriminant(BLDA)method to design an classifier included two level classification structure which could be applied in asynchronous brain-computer interface(BCI). Experimental result shows that this method has obtained better classification results in the EEG contains idle state. In the case of the use of smaller electrodes, the accuracy and mean square error of classification results of the test set samples are respectively 77.62% and 0.495.

参考文献/References:

[1] WOLOAW J R,BIRBAUMER N,HEETDERKS W J,et al.Brain-computer interface technology: A review of the first international meeting[J].IEEE Trans on Rehabilitation Engineering,2000,8(2):164-173.
[2] 刘美春,谢胜利.异步脑-机接口的空闲状态检测新方法[J].计算机应用研究,2010,27(1):151-153.
[3] 邓志东,李修全,郑宽浩,等.一种基于SSVEP的仿人机器人异步脑-机接口控制系统[J].机器人,2011,33(1):129-135.
[4] TSUI C S L,GAN J Q.Asynchronous BCI control of a robot simulator with supervised online training[C]//Proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning.Berlin: Springer-Verlag,2007:125-134.
[5] GEORGE T,BERNHARD G,PFURTSCHELLER G.Continuous EEG classification during motor imagery-simulation of an asynchronous BCI[J].IEEE Trans on Neural Systems and Rehabilitation Engineering,2004,12(2):258-265.
[6] ZHANG Dan,WANG Yi-jun,GAO Xiao-rong,et al.An algorithm for idle-state detection in motor-imagery-based brain-computer interface[J].Computational Intelligence and Neuroscience,2007(2007):1-9.
[7] 苏少军,方慧娟,王根.基于多参数公共空间频率模式算法的脑电特征提取[J].微型机与应用,2010,30(18):72-75.
[8] LEMM S,BLANKERTZ B, CURIO G,et al.Spatio spectral filters for improving the classification of single trial EEG[J].IEEE Trans on Biomedical Engineering,2005,52(9):1541-1547.
[9] 张贤达.现代信号处理[M].2版.北京:清华大学出版社,2002:65-96.
[10] 尧德中,刘铁军,雷旭,等.基于脑电的脑-机接口:关键技术和应用前景[J].电子科技大学学报,2009,38(5):550-553.
[11] HOFFMANN U,EBRAHIMI T,VESIN J M.Bayesian machine learning applied in a brain-computer interface for disabled users[D].Lausanne: Ecole Polytechnique Fédérale de Lausanne,2007:60-64.
[12] 张宇,王行愚,张建华,等.离散粒子群优化-贝叶斯线性判别分析算法用于视觉事件相关电位P300的分类[J].中国生物医学工程学报,2010,29(1):46-52.

备注/Memo

备注/Memo:
收稿日期: 2012-06-15
通信作者: 方慧娟(1979-),女,讲师,主要从事脑机接口系统的研究.E-mail:huijuan.fang@163.com.
基金项目: 福建省自然科学基金资助项目(2009J05147); 华侨大学科研基金资助项目(09BS617)
更新日期/Last Update: 2013-07-20