Please wait a minute...
J4  2013, Vol. 47 Issue (8): 1431-1436    DOI: 10.3785/j.issn.1008-973X.2013.08.016
电气工程     
脑机接口中基于BISVM的EEG分类
杨帮华, 何美燕, 刘丽, 陆文宇
上海大学 机电工程与自动化学院自动化系; 上海市电站自动化技术重点实验室,上海 200072
EEG classification based on batch incremental SVM in
brain computer interfaces
YANG Bang-hua, HE Mei-yan, LIU Li, LU Wen-yu
Department of Automation, School of Mechatronics Engineering and Automation; Shanghai Key Laboratory of Power Station Automation Technology; Shanghai University, Shanghai 200072
 全文: PDF  HTML
摘要:

针对脑电信号(EEG)分类问题,提出基于批处理增量式支持向量机(BISVM)的分类方法.将所有数据通过批处理进行分组,采用第1组数据在SVM中建立初始分类器模型,将剩余组内数据顺序作为新增样本,对满足卡罗需-库恩-塔克(KKT)条件的样本进行增量学习和减量去学习,不断判断KKT条件并更新参数,丢弃错误样本,对初始分类器模型进行更新.对2008年脑机接口竞赛数据及本实验室采集数据,用小波包分解(WPD)结合共空间模式(CSP)进行特征提取,SVM、ISVM及BISVM分类.结果表明,BISVM的平均分类准确率相对SVM及ISVM分别提高了3.3%及0.3%,BISVM平均训练时间相对ISVM从1.076 s减少到0.793 s.BISVM为改善计算机对大脑的适应性,实现快速实时在线的脑机接口系统奠定基础.

Abstract:

Aiming at the Electroencephalogram (EEG) classification, a classification method based on batch incremental support vector machine (BISVM) was proposed in brain computer interfaces (BCIs). All training data were grouped by batch processing and the initial SVM classifier model was set up using the first group. The remaining groups of data were added into the first group orderly as new samples. An incremental learning and decremental learning process was adopted to samples which meet the KKT condition. The initial SVM classifier model was updated by continuously estimating KKT condition, updating the parameters, discarding the error samples. Based on the 2008 BCI Competition Dataset and our experimental EEG data, features were extracted by wavelet packet decomposition (WPD) and common spatial patterns (CSP). The SVM, ISVM and BISVM were used to classify these features. Experimental results show that the average classification accuracy of the BISVM is 3.3% and 0.3% higher than the SVM and ISVM respectively. The average training time of the BISVM is shortened from 1.076 s seconds to 0.073 s seconds compared to the ISVM. The BISVM can not only improve the adaptability of a computer to human brain but also lay the foundation to the realization of a fast, real-time and online BCI system.

出版日期: 2013-08-01
:  TP 391.4  
基金资助:

 国家自然基金项目资助项目(31100709, 60975079);上海市教育委员会创新资助项目(11YZ19,12ZZ099).

作者简介: 杨帮华(1971—),女,副教授,主要从事脑机接口,生物医学信号处理,模式识别与智能系统等研究. E-mail: yangbanghua@shu.edu.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  

引用本文:

杨帮华, 何美燕, 刘丽, 陆文宇. 脑机接口中基于BISVM的EEG分类[J]. J4, 2013, 47(8): 1431-1436.

YANG Bang-hua, HE Mei-yan, LIU Li, LU Wen-yu. EEG classification based on batch incremental SVM in
brain computer interfaces. J4, 2013, 47(8): 1431-1436.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2013.08.016        http://www.zjujournals.com/eng/CN/Y2013/V47/I8/1431

[1] XAVIER A, IMRAN KHAN N, MARIE FRANCOISE L, et al. Accuracy of a BCI based on movement-related and error potentials [C]∥ 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Boston: IEEE, 2011: 3688-3691.

[2] MOLINA G G. BCI Adaptation using incremental SVM learning [C]∥ 3rd International IEEE/EMBS Conference on Neural Engineering. Hawaii: IEEE, 2007: 337-341.

[3] ZHANG Ying-chun, HU Guo-sheng, ZHU Feng-Feng, et al. A new incremental learning support vector machine [C]∥ Artificial Intelligence and Computational Intelligence. Shanghai: Lecture Notes in Computer Science, 2009, 7-10.


[4] XIAO Huai-tie, SUN Fa-sheng, LIANG Yong-sheng. A fast incremental learning algorithm for SVM Based on K nearest neighbors [C]∥ Artificial Intelligence and Computational Intelligence. Nanjing: Lecture Notes in Computer Science, 2010, 413-416.

[5] QIN Jian-zhao, LI Yuan-qing. An improved semi-supervised support vector machine based translation algorithm for BCI systems [C]∥ The 18th International Conference on Pattern Recognition. Hong Kong: IEEE, 2006, 1240-1243.

[6] BLANKERTZ B. BCI Competitions. [EB/OL]. (2008-06-24) [2011-11-06]. http:∥www.bbci.de/competition

[7] NOVI Q, GUAN C, DAT T H, et al. Sub-band common spatial pattern (SBCSP) for brain-computer interface [C]∥ 3rdInternational IEEE/ EMBS Conference on Neural Engineering. Hawaii: IEEE, 2007: 204-207.

[8] LI Yuan-qing, GUAN Cun-tai. A semi-supervised SVM learning algorithm for joint deature extraction and classification in brain computer interfaces [C]∥ 28th IEEE EMBS Annual International Conference. New York: IEEE, 2006: 2570-2573.

[9] MASSIMILIANO P, ALESSANDRO V, [J]. Neural Computation, 1998, 10(4): 955-974.

[10] 吴慧.新的支持向量机增量学习算法[D].西安:西安电子科技大学, 2009.

WU Hui. New incremental learning algorithms for support vector machines [D]. Xi’an: Xidian University, 2009.

[11] ZHU Fa, YE Ning, XU Sheng, et al. Support vectors classification and incremental learning [C]∥ Information Technology and Artificial Intelligence Conference. Chongqing: IEEE, 2011: 206-210.

[12] CHRISTOPHER P D, GERT C. SVM Incremental learning, adaptation and optimization [C]∥ International Symposium on Neural Networks. Oregon : IEEE, 2003: 2685-2690.

[13] KARASUYAMA, M, TAKEUCHI, I. Multiple incremental decremental learning of support vector Machines [J]. IEEE Computational Intelligence Society, 2010, 21(7): 1048-1059.

[1] 徐嵩,孙秀霞,何衍. 利用直线段成像特性的摄像机畸变迭代标定方法[J]. J4, 2014, 48(3): 404-413.
[2] 谢天, 解利军, 宋广华, 郑耀. 基于平面颜色分布的增强现实自然特征注册算法[J]. J4, 2013, 47(12): 2243-2252.
[3] 杨冰, 许端清, 杨鑫, 赵磊, 唐大伟. 基于艺术风格相似性规则的绘画图像分类[J]. J4, 2013, 47(8): 1486-1492.
[4] 楼晓俊, 孙雨轩, 刘海涛. 聚类边界过采样不平衡数据分类方法[J]. J4, 2013, 47(6): 944-950.
[5] 孟子博, 姜虹, 陈婧, 袁波, 王立强. 基于特征剪裁的AdaBoost算法及在人脸检测中的应用[J]. J4, 2013, 47(5): 906-911.
[6] 何智翔, 丁晓青, 方驰, 文迪. 基于LBP和CCS-AdaBoost的多视角人脸检测[J]. J4, 2013, 47(4): 622-629.
[7] 刘晓芳,叶修梓,张三元,张引. 并行磁共振图像的非二次正则化保边性重建[J]. J4, 2012, 46(11): 2035-2043.
[8] 张远辉,韦巍. 在线角速度估计的乒乓球机器人视觉测量方法[J]. J4, 2012, 46(7): 1320-1326.
[9] 施锦河, 沈继忠, 王攀. 四类运动想象脑电信号特征提取与分类算法[J]. J4, 2012, 46(2): 338-344.
[10] 张大尉, 朱善安. 基于核邻域保持判别嵌入的人脸识别[J]. J4, 2011, 45(10): 1842-1847.
[11] 舒振宇, 汪国昭. 基于张量投票的快速网格分割算法[J]. J4, 2011, 45(6): 999-1005.
[12] 徐舒畅, 张三元, 张引. 基于彩色图像的皮肤色素浓度提取算法[J]. J4, 2011, 45(2): 253-258.
[13] 佘青山, 孟明, 罗志增, 马玉良. 基于多核学习的下肢肌电信号动作识别[J]. J4, 2010, 44(7): 1292-1297.
[14] 薛凌云, 段会龙, 向学勤, 范影乐. 基于FitzHughNaguno神经元随机共振机制的图像复原[J]. J4, 2010, 44(6): 1103-1107.
[15] 张远辉, 韦巍, 虞旦. 基于实时图像的乒乓机器人Kalman跟踪算法[J]. J4, 2009, 43(09): 1580-1584.