Paper Info
Title
Feature recognition of multi-class imaginary movements in brain-computer interface
Abstract
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.
Year
DOI
Venue
2009
10.1109/VECIMS.2009.5068903
VECIMS
Keywords
Field
DocType
higher accuracy rate,feature recognition,brain-computer interface,bci system,multi-class imaginary movement,identification accuracy,feature data,two-dimensional time-frequency analysis,multi-channel synchronization,fisher separability analysis,imaginary movement,object recognition,brain computer interfaces,data mining,foot,image recognition,support vector machine,feature extraction,support vector machines,synchronisation,image analysis,time frequency analysis,brain computer interface,electroencephalography,satisfiability,accuracy
Synchronization,Pattern recognition,Computer science,Support vector machine,Feature recognition,Brain–computer interface,Speech recognition,Feature extraction,Time–frequency analysis,Artificial intelligence,Cognitive neuroscience of visual object recognition,Feature data
Conference
Citations 
PageRank 
References 
0
0.34
1
Authors
6
Name
Order
Citations
PageRank
Baikun Wan110416.90
Yan'gang Liu200.34
Dong Ming310551.47
Hongzhi Qi44920.61
Yizhong Wang5145.30
Rui Zhang672.04