Brain-computer interface design using relative wavelet energy

Author

Haibin, Zhao ; Xu, Wang ; Hong, Wang

Author_Institution

Sch. of Inf. Sci. & Eng., Northeastem Univ., Shenyang

fYear

2008

fDate

2-4 July 2008

Firstpage

3558

Lastpage

3561

Abstract

A brain-computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other devices. In this paper, we used a new method: relative wavelet energy (RWE) for feature selection in BCI design. Linear discriminant analysis (LDA) and support vector machine (SVM) were utilized to classify the pattern of left and right hand movement imagery. Its performance was evaluated by mutual information (MI) using the data set III of BCI competition 2003. From the results of the experiment, we can get that RWE is a very good method for feature selection in BCI research and there is not much difference between LDA and SVM.

Keywords

man-machine systems; medical signal processing; neurophysiology; support vector machines; user interfaces; brain-computer interface design; communication system; linear discriminant analysis; mutual information; relative wavelet energy; support vector machine; Brain computer interfaces; Continuous wavelet transforms; Discrete wavelet transforms; Electroencephalography; Feedback; Linear discriminant analysis; Mutual information; Support vector machine classification; Support vector machines; Wavelet transforms; brain-computer interface; linear discriminant analysis; relative wavelet energy; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2008. CCDC 2008. Chinese

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-1733-9

Electronic_ISBN

978-1-4244-1734-6

Type

conf

DOI

10.1109/CCDC.2008.4597992

Filename

4597992