联合稀疏表示的双次诱发电位提取算法

doi:10.3969/j.issn.0372-2112.2014.05.004

电子学报

联合稀疏表示的双次诱发电位提取算法

余南南, 刘海宽, 王晓燕

江苏师范大学电气工程及自动化学院,江苏徐州 221116

收稿日期:2013-05-07 修回日期:2013-08-05 出版日期:2014-05-25
- 作者简介:
- 余南南 女，博士，讲师，1981年出生于黑龙江省齐齐哈尔，江苏师范大学电气工程及自动化学院教师，主要从事生物医学信息处理方面研究. E-mail:yunannan1981@126.com刘海宽男,1962年生于黑龙江省双鸭山,江苏师范大学电气工程及自动化学院教授,院长,研究方向为智能信息处理与控制等. E-mail：iuhaikuan1962@163.com
- 基金资助:
- 江苏省高校自然科学基金 (No.13KJB510010); 江苏省自然科学基金 (No.BK20130230)

Double-Trial Extraction of Evoked Potentials with Joint Sparse Representation

YU Nan-nan, LIU Hai-kuan, WANG Xiao-yan

School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, Jiangsu 221116, China

Received:2013-05-07 Revised:2013-08-05 Online:2014-05-25 Published:2014-05-25

摘要/Abstract

摘要： 诱发电位少次提取对于研究大脑活动规律以及临床诊断等具有重要意义.根据脑电信号的特点，本文提出一种基于联合稀疏表示的双次诱发电位信号估计算法.利用诱发电位信号的准周期性和自发脑电信号的随机性，该算法将脑电信号看作为相似成分和相异成分的叠加.神经系统通过相同刺激产生的诱发电位主要在潜伏期和波幅两方面发生变化，因此该算法利用平均诱发电位进行建模，得到稀疏字典，通过联合稀疏表示算法实现双次诱发电位信号的提取.实验结果表明，该算法和其他算法相比获得了更好的效果.

关键词: 诱发电位双次提取, 联合稀疏表示, 字典构造, 脑电信号

Abstract: The few-trial extraction of evoked potentials is very meaningful to the study of brain and many clinical applications.According to the characteristics of Electroencephalogram signal,this paper presents a novel algorithm for double-trial extracting evoked potentials based on joint sparse representation.Taking advantage of the quasi-periodic structure of evoked potentials and randomness of ongoing spontaneous Electroencephalogram,the observations of evoked potentials are considered as the superposition of the similar components and the different components.Evoked potential obtained by same stimulation of the nerves changes only in latency and scale parameters.Our method uses the average evoked potentials to model and construct the sparse dictionary,so the double-trial extraction of evoked potentials can be achieved with joint sparse representation.Experiment results show that the performance of the proposed method is better than that of other methods.

Key words: double-trial extraction of evoked potentials, joint sparse representation, construction of dictionary, electroencephalogram

中图分类号:

TN911.7

余南南, 刘海宽, 王晓燕. 联合稀疏表示的双次诱发电位提取算法[J]. 电子学报, DOI: 10.3969/j.issn.0372-2112.2014.05.004.

YU Nan-nan, LIU Hai-kuan, WANG Xiao-yan. Double-Trial Extraction of Evoked Potentials with Joint Sparse Representation[J]. Acta Electronica Sinica, DOI: 10.3969/j.issn.0372-2112.2014.05.004.

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联合稀疏表示的双次诱发电位提取算法

Double-Trial Extraction of Evoked Potentials with Joint Sparse Representation

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