Feature recognition of multi-class imaginary movements in brain-computer interface

• Published in: 2009 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurements Systems pp. 250 - 254
• Main Authors: Baikun Wan, Yan'gang Liu, Dong Ming, Hongzhi Qi, Yizhong Wang, Rui Zhang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2009
• Subjects: Brain computer interfaces; brain-computer interface; Data mining; Electroencephalography; event related desynchronization; Feature extraction; fisher analysis; Foot; Image analysis; Image recognition; multi-class imaginary movements; support vector machine; Support vector machine classification; Support vector machines; Tongue
• ISBN: 142443808X; 9781424438082
• ISSN: 1944-9410
• DOI: 10.1109/VECIMS.2009.5068903

Feature recognition of multi-class imaginary movements is an important subject of brain-computer interface based on imaginary movement. In this paper, using the method of two-dimensional time-frequency analysis combined with Fisher separability analysis to study multi-channel synchronization, multi-class imaginary movements potential information of typical subjects. Also we have extracted the feature data of event related resynchronization/synchronization that could be used to identify different classes, and then use the support vector machine to establish classifiers, and have completed a higher accuracy rate of classification for multi-motor patterns. The result shows that the identification accuracy could basically satisfy the requirements of BCI systems under the circumstances that the subjects are better trained.