TY - GEN
T1 - EEG based method for the decoding of complex arm motor imagery tasks
AU - Zhang, Shuailei
AU - Wang, Shuai
AU - Zheng, Dezhi
AU - Na, Rui
AU - Zhu, Kai
AU - Ma, Kang
AU - Li, Dapeng
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/14
Y1 - 2018/12/14
N2 - Brain-computer interface (BCI) is a new kind of communication and control technology, which connect the human brain to external world by converting users' intention into machine command without the cooperation of normal nerves and muscles. Recently, brain computer interface based on motor imagery (MI) has received increasing interest for its practicability and convenience. However, the short of imagery pattern makes the application of MI difficult to realize. This paper will propose a MI patterns including four novel and complex arm gestures: Clockwise and anticlockwise swing of both arms. Preliminary result shows that using support vector machine classifier and deep brain network classifier, we are able to discriminate these tasks with average classification accuracy of 54.41%, and average information transmission rate of 8.05 bits/min. Meanwhile, the result shows clockwise and anticlockwise movements of same arm (error rate: 17.33%) are not as easily to discriminate as movement of left and right arms (error rate: 15.91%).
AB - Brain-computer interface (BCI) is a new kind of communication and control technology, which connect the human brain to external world by converting users' intention into machine command without the cooperation of normal nerves and muscles. Recently, brain computer interface based on motor imagery (MI) has received increasing interest for its practicability and convenience. However, the short of imagery pattern makes the application of MI difficult to realize. This paper will propose a MI patterns including four novel and complex arm gestures: Clockwise and anticlockwise swing of both arms. Preliminary result shows that using support vector machine classifier and deep brain network classifier, we are able to discriminate these tasks with average classification accuracy of 54.41%, and average information transmission rate of 8.05 bits/min. Meanwhile, the result shows clockwise and anticlockwise movements of same arm (error rate: 17.33%) are not as easily to discriminate as movement of left and right arms (error rate: 15.91%).
KW - Brain-computer interface
KW - classification accuracy
KW - information transmission rate
KW - motor imagery
KW - source based method
UR - https://www.scopus.com/pages/publications/85060680741
U2 - 10.1109/IST.2018.8577096
DO - 10.1109/IST.2018.8577096
M3 - 会议稿件
AN - SCOPUS:85060680741
T3 - IST 2018 - IEEE International Conference on Imaging Systems and Techniques, Proceedings
BT - IST 2018 - IEEE International Conference on Imaging Systems and Techniques, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Imaging Systems and Techniques, IST 2018
Y2 - 16 October 2018 through 18 October 2018
ER -