TY - GEN
T1 - Study of electroencephalogram feature extraction and classification of three tasks of motor imagery
AU - Zhao, Zhiyuan
AU - Yu, Jiali
AU - Wu, Yongqiang
AU - Li, Juan
AU - Guo, Hao
AU - Zhang, Hongmiao
AU - Sun, Lining
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Brain-computer interface (BCI) instead of depending on the brain's normal output pathways, can use electroencephalogram (EEG) from the scalp as the representation of brain activity to control external devices. EEG during motor imagery (MI) provides a non-muscular communication way to control external devices and has advantage of non-invasiveness and high time resolution. However the application is still limited by time-consuming training and poor classification rate with multiple categories etc. We recorded 64-channel scalp EEG from eight healthy subjects during imagery tasks of left, right hand movements and stop. EEG was analyzed in time-frequency distribution and spatial topographies were explored too. A one versus one common spatial pattern was applied to construct feature vector and then linear discriminant analysis was used for the classification. For the purpose of real time control in the future, small training size was used and we got discrimination among three types of motor imagery at the accuracy rate about 90%.
AB - Brain-computer interface (BCI) instead of depending on the brain's normal output pathways, can use electroencephalogram (EEG) from the scalp as the representation of brain activity to control external devices. EEG during motor imagery (MI) provides a non-muscular communication way to control external devices and has advantage of non-invasiveness and high time resolution. However the application is still limited by time-consuming training and poor classification rate with multiple categories etc. We recorded 64-channel scalp EEG from eight healthy subjects during imagery tasks of left, right hand movements and stop. EEG was analyzed in time-frequency distribution and spatial topographies were explored too. A one versus one common spatial pattern was applied to construct feature vector and then linear discriminant analysis was used for the classification. For the purpose of real time control in the future, small training size was used and we got discrimination among three types of motor imagery at the accuracy rate about 90%.
KW - Brain-computer interface
KW - Electroencephalogram
KW - common spatial pattern
KW - motor imagery
UR - https://www.scopus.com/pages/publications/85050808353
U2 - 10.1109/ICARM.2017.8273212
DO - 10.1109/ICARM.2017.8273212
M3 - 会议稿件
AN - SCOPUS:85050808353
T3 - 2017 2nd International Conference on Advanced Robotics and Mechatronics, ICARM 2017
SP - 492
EP - 497
BT - 2017 2nd International Conference on Advanced Robotics and Mechatronics, ICARM 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Advanced Robotics and Mechatronics, ICARM 2017
Y2 - 27 August 2017 through 31 August 2017
ER -