title: NIRS-based classification of clench force and speed motor imagery with the use of empirical mode decomposition for BCI.
abstract: Near-infrared spectroscopy (NIRS) is a non-invasive optical technique used for brain-computer interface (BCI). This study aims to investigate the brain hemodynamic responses of clench force and speed motor imagery and extract task-relevant features to obtain better classification performance. Given the non-stationary characteristics of real hemodynamic measurements, empirical mode decomposition (EMD) was applied to reduce the physiological noise overwhelmed in the task-relevant NIRS signals. Compared with continuous wavelet decomposition, EMD does not require a pre-determined basis function. EMD decomposes the original signals into a set of intrinsic mode functions (IMFs). In this study, joint mutual information was applied to select the optimal features, and support vector machine was used as a classifier. Offline and pseudo-online analyses showed that the most feasible classification accuracy can be obtained using IMFs as input features. Accordingly, an alternative feature is provided to develop the NIRS-BCI system.
authors: State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China. Electronic address: yinxuxian@sia.cn.
authors: Xuxian
authors: X
authors: Yin
authors: State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, PR China. Electronic address: blxu@sina.cn.
authors: Baolei
authors: B
authors: Xu
authors: Key Laboratory of Motor and Brain imaging, Capital Institute of Physical Education, Beijing 100088, China. Electronic address: jiangchanghao@cupes.edu.cn.
authors: Changhao
authors: C
authors: Jiang
authors: School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming 650500, PR China. Electronic address: fyf@ynu.edu.cn.
authors: Yunfa
authors: Y
authors: Fu
authors: State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, PR China; Department of Advanced Robotics, Chiba Institute of Technology, Chiba 2750016, Japan. Electronic address: zhidong.wang@it-chiba.ac.jp.
authors: Zhidong
authors: Z
authors: Wang
authors: State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, PR China; School of Mechanical Engineering & Automation, Northeastern University, Shenyang, China. Electronic address: hli@sia.cn.
authors: Hongyi
authors: H
authors: Li
authors: State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, PR China. Electronic address: sg0105@sia.cn.
authors: Gang
authors: G
authors: Shi
mesh_terms: D000465:Algorithms; D001921:Brain / Q000098:blood supply; D062207:Brain-Computer Interfaces; D006225:Hand / Q000502:physiology*; D006439:Hemodynamics; D006801:Humans; D009068:Movement; D012815:Signal Processing, Computer-Assisted; D019265:Spectroscopy, Near-Infrared
keywords: Brain–computer interface (BCI); Empirical mode decomposition (EMD); Joint mutual information (JMI); Near-infrared spectroscopy (NIRS)