High accuracy classification of EEG signal
Conference Proceeding Article
Improving classification accuracy is a key issue to advancing brain computer interface (BCI) research from laboratory to real world applications. This article presents a high accuracy EEC signal classification method using single trial EEC signal to detect left and right finger movement. We apply an optimal temporal filter to remove irrelevant signal and subsequently extract key features from spatial patterns of EEG signal to perform classification. Specifically, the proposed method transforms the original EEG signal into a spatial pattern and applies the RBF feature selection method to generate robust feature. Classification is performed by the SVM and our experimental result shows that the classification accuracy of the proposed method reaches 90% as compared to the current reported best accuracy of 84%.
Graphics and Human Computer Interfaces
Data Management and Analytics
ICPR '04 Proceedings of the 17th International Conference on Pattern Recognition
City or Country
New York, USA
XU, Wenjie; GUAN, Cuitai; SIONG, Chng Eng; RANGANATHA, S.; M. THULASIDAS; and Wu Jiankang.
High accuracy classification of EEG signal. (2004). ICPR '04 Proceedings of the 17th International Conference on Pattern Recognition. 2, 391-394. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3496
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