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Improving Classification by Feature Discretization and Optimization for fNIRS-based BCI
Xu BL(徐保磊); Fu YF(伏云发); Shi G(石刚); Yin XX(尹旭贤); Wang ZD(王志东); Li HY(李洪谊)
作者部门机器人学研究室
关键词Brain Computer Interface Bci Fnirs Feature Discretization Feature Selection
发表期刊Journal of Biomimetics Biomaterials and Tissue Engineering
ISSN1662-100X
2014
卷号19期号:1页码:1-5
产权排序1
摘要

In this paper, we present a signal discretization and feature selection method to improve classification accuracy for fNIRS based brain computer interface (BCI) system, which can classifiy right hand clench force motor imagery and clench speed motor imagery at an accuracy of 69%-81% through 5 fold cross validation in 6 subjects. Difference between oxyhemoglobin and deoxyhemoglobin (we abbreviate this difference as HbD) is proposed as a new feature type and shows outstanding performance in some subjects. Linear kernal support vector machine (SVM) classification between clench force motor imagery and clench speed motor imagery using four concentration feature types (oxyhemoglobin, deoxyhemoglobin, totalhemoglobin, and HbD) is implemented. Our results demonstrate that feature discretization using Chi2 algorighm and feature optimization using ‘MIFS’ (Mutual Information Feature Selection) criterion can improve the classification accuracy by more than 35%. Except total hemoglobin, all the other three feature types can be used as the optimum feature for different subjects. The results of this paper can also be used in online BCI applications.

语种英语
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/15470
专题机器人学研究室
通讯作者Xu BL(徐保磊)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, P. R. China
2.University of Chinese Academy of Sciences, Beijing 100049, P. R. China
3.School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming 650500, P. R. China
4.Department of Advanced Robotics, Chiba Institute of Technology, Chiba 2750016, Japan
5.School of Mechanical Engineering & Automation, Northeastern University, Shenyang, China
推荐引用方式
GB/T 7714
Xu BL,Fu YF,Shi G,et al. Improving Classification by Feature Discretization and Optimization for fNIRS-based BCI[J]. Journal of Biomimetics Biomaterials and Tissue Engineering,2014,19(1):1-5.
APA Xu BL,Fu YF,Shi G,Yin XX,Wang ZD,&Li HY.(2014).Improving Classification by Feature Discretization and Optimization for fNIRS-based BCI.Journal of Biomimetics Biomaterials and Tissue Engineering,19(1),1-5.
MLA Xu BL,et al."Improving Classification by Feature Discretization and Optimization for fNIRS-based BCI".Journal of Biomimetics Biomaterials and Tissue Engineering 19.1(2014):1-5.
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