TY - GEN
T1 - Classification of EEG Multiple Imagination Tasks Based on Independent Component Analysis and Relevant Vector Machines
AU - Zhang, Shanting
AU - Xu, Rui
AU - Belkacem, Abdelkader Nasreddine
AU - Shin, Duk
AU - Wang, Kun
AU - Wang, Zhongpeng
AU - Yu, Lu
AU - Qiao, Zhifeng
AU - Wang, Changming
AU - Chen, Chao
N1 - Funding Information:
This work was financially supported by National Key R&D Program of China(2018YFC1314500), National Natural Science Foundation of China(61806146), Natural Science Foundation of Tianjin City (17JCQNJC04200), Tianjin Key Laboratory Foundation of Complex System Control Theory and Application (TJKL-CTACS-201702) and Young and Middle-Aged Innovation Talents Cultivation Plan of Higher Institutions in Tianjin.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - To solve the problem of feature extraction in braincomputer interface (BCI), the position, size and direction of dipole are located by using dipole localization method, so as to locate the active part of advanced nerve activity and remove a series of physiological and electrical artifacts such as electro-ophthalmogram. The common space pattern and correlation vector machine are used to extract the effective components of EEG signals and classify multiple motor imagery tasks. The results show that the combination of EEG dipole localization and common spatial pattern can effectively improve the signal-to-noise ratio of EEG signals and extract more obvious features. The correlation vector machine provides better classification results and is an effective method to complete the classification and recognition of motor imagery signals.
AB - To solve the problem of feature extraction in braincomputer interface (BCI), the position, size and direction of dipole are located by using dipole localization method, so as to locate the active part of advanced nerve activity and remove a series of physiological and electrical artifacts such as electro-ophthalmogram. The common space pattern and correlation vector machine are used to extract the effective components of EEG signals and classify multiple motor imagery tasks. The results show that the combination of EEG dipole localization and common spatial pattern can effectively improve the signal-to-noise ratio of EEG signals and extract more obvious features. The correlation vector machine provides better classification results and is an effective method to complete the classification and recognition of motor imagery signals.
KW - Brain computer interface
KW - Brainwave dipole Cospatial pattern
KW - Motor imagery
KW - Relevance vector machine
UR - http://www.scopus.com/inward/record.url?scp=85070511903&partnerID=8YFLogxK
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U2 - 10.1109/IMBIOC.2019.8777887
DO - 10.1109/IMBIOC.2019.8777887
M3 - Conference contribution
AN - SCOPUS:85070511903
T3 - IEEE MTT-S 2019 International Microwave Biomedical Conference, IMBioC 2019 - Proceedings
BT - IEEE MTT-S 2019 International Microwave Biomedical Conference, IMBioC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE MTT-S International Microwave Biomedical Conference, IMBioC 2019
Y2 - 6 May 2019 through 8 May 2019
ER -