title: EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine.
abstract: Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (Kc) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.
authors: Key Laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, People's Republic of China.
authors: Lin
authors: L
authors: Gao
authors: State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, People's Republic of China.
authors: Wei
authors: W
authors: Cheng
authors: State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, People's Republic of China.
authors: Jinhua
authors: J
authors: Zhang
authors: Key Laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, People's Republic of China.
authors: Jue
authors: J
authors: Wang
mesh_terms: D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D006225:Hand / Q000502:physiology; D006801:Humans; D000069550:Machine Learning; D008297:Male; D009043:Motor Activity / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted