Title

High accuracy classification of EEG signal

Publication Type

Conference Proceeding Article

Publication Date

12-2004

Abstract

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%.

Discipline

Graphics and Human Computer Interfaces

Research Areas

Data Management and Analytics

Publication

ICPR '04 Proceedings of the 17th International Conference on Pattern Recognition

Volume

2

First Page

391

Last Page

394

ISBN

0769521282

Identifier

10.1109/ICPR.2004.1334229

Publisher

IEEE

City or Country

New York, USA

Additional URL

http://doi.org/10.1109/ICPR.2004.1334229

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