基于运动想象脑机接口的AuFBCSP方法

›› 2012, Vol. 25 ›› Issue (1): 48-.

基于运动想象脑机接口的AuFBCSP方法

侯秉文,刘鹏,周广玉,何嘉全

(1.西安电子科技大学理学院,陕西西安 710071;2.西安电子科技大学生命科学技术学院,陕西西安 710071)

出版日期:2012-01-15 发布日期:2012-01-10
作者简介:侯秉文(1987—),女,硕士研究生。研究方向:脑电信号处理。
基金资助:
国家自然科学基金资助项目(81000641)

Augmented Filter Bank Common Spatial Pattern in Motor Imagery-based Brain-Computer Interface

HOU Bing-Wen, LIU Peng, ZHOU Guang-Yu, HE Jia-Quan

(1.School of Science,Xidian University,Xi'an 710071,China;2.School of Life Science,Xidian University,Xi'an 710071,China)

Online:2012-01-15 Published:2012-01-10

摘要/Abstract

摘要：

针对基于运动想象的脑机接口问题,提出了一种新的特征提取方法,即加强的滤波带宽共同空间模式方法。与传统的滤波带宽共同空间模式方法相比,文中提出的方法充分考虑了被试在进行运动想象时发生事件相关去同步时段的特异性,从而得到更多的特征,并利用这些特征分类,取得更高的准确率和kappa值。在特征的选择上,利用BCI的竞赛数据,将基于互信息的方法和基于准确率的方法进行对比,发现基于准确率的特征选择方法优于基于互信息的特征选择方法。

关键词: 脑机接口, 运动想象, 特征提取, 滤波带宽共同空间模式方法

Abstract:

This paper proposes the augmented Filter Bank Common Spatial Pattern,a novel feature extraction algorithm for the motor imagery-based brain-computer interface.Compared with the Filter Bank Common Spatial Pattern,our method takes into full consideration the specificity temporal information,in which ERD happens in motor imagery.In this way,more features are acquired,and the higher accuracy and kappa value are obtained by these features.Two kinds of feature selection algorithms are employed in the BCI competition dataset.The results show that the accuracy based method yields superior classification accuracy compared with that based on mutual information.

Key words: brain-computer interface;motor imagery;feature extraction;filter bank common spatial pattern algorithm

中图分类号:

TP242

侯秉文, 刘鹏, 周广玉, 何嘉全. 基于运动想象脑机接口的AuFBCSP方法[J]. , 2012, 25(1): 48-.

HOU Bing-Wen, LIU Peng, ZHOU Guang-Yu, HE Jia-Quan. Augmented Filter Bank Common Spatial Pattern in Motor Imagery-based Brain-Computer Interface[J]. , 2012, 25(1): 48-.

参考文献［11］

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