title: Enhanced performance by time-frequency-phase feature for EEG-based BCI systems.
abstract: We introduce a new motor parameter imagery paradigm using clench speed and clench force motor imagery. The time-frequency-phase features are extracted from mu rhythm and beta rhythms, and the features are optimized using three process methods: no-scaled feature using "MIFS" feature selection criterion, scaled feature using "MIFS" feature selection criterion, and scaled feature using "mRMR" feature selection criterion. Support vector machines (SVMs) and extreme learning machines (ELMs) are compared for classification between clench speed and clench force motor imagery using the optimized feature. Our results show that no significant difference in the classification rate between SVMs and ELMs is found. The scaled feature combinations can get higher classification accuracy than the no-scaled feature combinations at significant level of 0.01, and the "mRMR" feature selection criterion can get higher classification rate than the "MIFS" feature selection criterion at significant level of 0.01. The time-frequency-phase feature can improve the classification rate by about 20% more than the time-frequency feature, and the best classification rate between clench speed motor imagery and clench force motor imagery is 92%. In conclusion, the motor parameter imagery paradigm has the potential to increase the direct control commands for BCI control and the time-frequency-phase feature has the ability to improve BCI classification accuracy.
authors.affiliation: State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, China ; University of Chinese Academy of Sciences, Beijing 100049, China.
authors.forename: Baolei
authors.initials: B
authors.lastname: Xu
authors.affiliation: School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming 650500, China.
authors.forename: Yunfa
authors.initials: Y
authors.lastname: Fu
authors.affiliation: State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, China.
authors.forename: Gang
authors.initials: G
authors.lastname: Shi
authors.affiliation: State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, China ; University of Chinese Academy of Sciences, Beijing 100049, China.
authors.forename: Xuxian
authors.initials: X
authors.lastname: Yin
authors.affiliation: State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, China ; Department of Advanced Robotics, Chiba Institute of Technology, Chiba 2750016, Japan.
authors.forename: Zhidong
authors.initials: Z
authors.lastname: Wang
authors.affiliation: State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, China ; School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110004, China.
authors.forename: Hongyi
authors.initials: H
authors.lastname: Li
authors.affiliation: Key Laboratory of Motor and Brain Imaging, Capital Institute of Physical Education, Beijing 100088, China.
authors.forename: Changhao
authors.initials: C
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004569:Electroencephalography; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine