[1]赵丽,薛仲林,王宣方. 基于SSVEP的高传输速率脑机拨号系统[J].计算机技术与发展,2017,27(10):185-188.
　ZHAO Li,XUE Zhong-lin,WANG Xuan-fang. A High ITR BCI Dial System Based on SSVEP[J].,2017,27(10):185-188.
点击复制

 基于SSVEP的高传输速率脑机拨号系统()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年10期
页码:
185-188
栏目:
应用开发研究
出版日期:
2017-10-10

文章信息/Info

Title:
 A High ITR BCI Dial System Based on SSVEP
文章编号:
1673-629X（2017)10-0185-04
作者:
 赵丽; 薛仲林; 王宣方
 天津市信息传感与智能控制重点实验室
Author(s):
 ZHAO Li; XUE Zhong-lin; WANG Xuan-fang
关键词:
 ; 典型相关分析; 稳态视觉诱发电位; 传输速率; 脑机接口系统
Keywords:
 ; canonical correlation analysis; steady-state visual evoked potentials; information transfer rate; brain computer interaction
分类号:
TP302
文献标志码:
A
摘要:
 随着科技的发展,BCI技术已有长足进步,但是其识别率和传输率低,是阻碍其走出实验室、走向实践应用一个重要原因.SSVEP即当人的眼睛的视网膜受到固定频率的刺激时,大脑视觉皮层也会产生一个相同频率的脑电信号或者是倍数频率的信号.为此,提出了基于CCA算法的快速脑机拨号系统.该系统引入用于识别和分析SSVEP信号的空间滤波器,同时将数据分析结果通过Matlab串口直接发送给下位机SIM900拨打电话.该系统不仅可以实现拨打电话功能,而且还可以实现电话的实时接听,以帮助有运动障碍的残障人士,有效保证了残障人士生活的便利.实验结果表明,基于CCA算法所构建的快速脑机拨号系统能够提取脑电信号的特征,实现准确的模式分类,其准确率达95%,ITR达到154 bit/min,为脑机接口系统推向应用提供一种新的思路和方法.
Abstract:
 With the development of science and technology,BCI technology has made great progress,but its recognition rate and transmis-sion rate are low which constrain its application in practice. SSVEP is the phenomenon that when the retina of the human eye is stimulated by a fixed frequency,the visual cortex of the brain produces a signal of the same frequency,or multiples of the frequency. Therefore,a quick brain computer dialing system based on CCA is proposed which is used for spatial filter for identification and analysis of SSVEP signals and sends the results of data analysis through Matlab serial port to the client SIM900 call. It can not only own the function of call-ing the telephone but also have real-time answering of the telephone so as to help the disabled person with the movement disorder and ef-fectively ensure the convenience of the disabled. The experimental results show that it can extract the characteristics of EEG signal for re-alization of accurate pattern classification with accuracy rate by 95% and ITR by 154 bit/min,and has provided a new ideas and method to establish brain computer interface system for applications.

相似文献/References:

[1]查春生 倪志伟 倪丽萍 公维峰.基于相空间重构的股价时间序列相关性分析[J].计算机技术与发展,2010,(08):17.
　ZHA Chun-sheng,NI Zhi-wei,NI Li-ping,et al.Correlations Analysis Between Stock Index Time Serials Based on Reconstructed Phase Space[J].,2010,(10):17.
[2]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(10):1.
[3]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(10):5.
[4]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(10):13.
[5]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(10):21.
[6]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(10):25.
[7]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(10):29.
[8]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(10):34.
[9]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(10):38.
[10]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(10):43.
[11]笪铖璐,陈志阳,黄丽亚. 基于CCA的SSVEP性能研究[J].计算机技术与发展,2015,25(05):52.
　DA Cheng-lu,CHEN Zhi-yang,HUANG Li-ya. Study on Performance of SSVEP Based on CCA[J].,2015,25(10):52.

更新日期/Last Update: 2017-11-24