The State Key Laboratory of Bioelectronics, Jiangsu Key Lab of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
Zhiwei Wei
The State Key Laboratory of Bioelectronics, Jiangsu Key Lab of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
The State Key Laboratory of Bioelectronics, Jiangsu Key Lab of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
Changcheng Wu
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Dalin Zhang
The State Key Laboratory of Bioelectronics, Jiangsu Key Lab of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
Wenlong Li
The State Key Laboratory of Bioelectronics, Jiangsu Key Lab of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
Guozheng Xu
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China
Huijun Li
The State Key Laboratory of Bioelectronics, Jiangsu Key Lab of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
Hong Zeng
The State Key Laboratory of Bioelectronics, Jiangsu Key Lab of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
The classification of motor imagery Electroencephalogram (EEG) of the same limb is important for natural control of neuroprosthesis. Due to the close spatial representations on the motor cortex area of the brain, the discrimination of the different motor imagery tasks is challenging. In this paper, phase synchronization information was proposed to classify motor imagery EEG within the same limb. In addition, non-portable was compared with portable EEG acquisition equipment for the purpose of making the brain computer interface (BCI) system more practical. In the non-portable case, the average accuracy of the binary classification and 3-class classification was 60.6% and 42.7%. In the portable case, the average EEG decoding accuracy of 58.5% and 39.9% was achieved for the two and three tasks. Furthermore, in both two cases, different sets of electrode pairs got the similar results. Moreover, we found that the proposed phase information based method was less sensitive to the number of EEG channels and had less performance degradation in portable EEG equipment. These results show it is possible to use phase synchronization information to discriminate different motor imagery tasks within the same limb. Eventually, this will potentially make the control of neuroprosthesis or other rehabilitation device more natural and intuitive.
