Journal of Biomimetics Biomaterials and Tissue Engineering
Open Access

Research Article

Improving Classification by Feature Discretization and Optimization for fNIRS-based BCI

¹State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, P. R. China

²University of Chinese Academy of Sciences, Beijing 100049, P. R. China

³School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming 650500, P. R. China

⁴Department of Advanced Robotics, Chiba Institute of Technology, Chiba 2750016, Japan

⁵School of Mechanical Engineering & Automation, Northeastern University, Shenyang, China

Corresponding Author:

Baolei Xu
State Key Laboratory of Robotics
Shenyang Institute of Automation (SIA)
Chinese Academy of Sciences (CAS)
Shenyang 110016, P. R. China
Tel: +86-15002426919
E-mail: blxu@sia.cn

Received date: September 28, 2013; Accepted date: November 18, 2013; Published date: December 26, 2013

Citation: Xu B, Fu Y, Shi G, Yin X, Wang Z, et al. (2014) Improving Classification by Feature Discretization and Optimization for fNIRS-based BCI. J Biomim Biomater Tissue Eng 19:119. doi:10.4172/1662-100X.1000119

Copyright: © 2013 Xu B, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author andsource are credited.

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.

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