Classification of Imaginary Movements in ECoG

Author

Li, Lijun ; Xiong, Dongsheng ; Wu, Xiaoming

Author_Institution

Dept. of Biomed. Eng., South China Univ. of Technol., Guangzhou, China

fYear

2011

fDate

10-12 May 2011

Firstpage

1

Lastpage

3

Abstract

The electrocorticogram (ECoG) is a kind of signal source that can be classified for making use of a human brain computer interface (BCI) field. The feature extraction is crucial for increasing classification accuracy rate. In this paper, Power Spectral Density is used for the selection of the optimal electrodes. Common spatial pattern (CSP) algorithm is used for feature extraction, and the nonlinear classification of motor imagery with support vector machines (SVM).The classification accuracy rate of 83% is achieved on Data set I of BCI Competition III.

Keywords

brain-computer interfaces; feature extraction; medical signal processing; neurophysiology; support vector machines; CSP algorithm; ECoG imaginary movements classification; Power Spectral Density; classification accuracy; common spatial pattern algorithm; electrocorticogram; feature extraction; human brain computer interface; motor imagery; support vector machines; Accuracy; Covariance matrix; Eigenvalues and eigenfunctions; Electrodes; Feature extraction; Rhythm; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedical Engineering, (iCBBE) 2011 5th International Conference on

Conference_Location

Wuhan

ISSN

2151-7614

Print_ISBN

978-1-4244-5088-6

Type

conf

DOI

10.1109/icbbe.2011.5780688

Filename

5780688