Title :
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
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;
Conference_Titel :
Bioinformatics and Biomedical Engineering, (iCBBE) 2011 5th International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5088-6
DOI :
10.1109/icbbe.2011.5780688
