Study on the method of feature extraction for brain-computer interface using discriminative common vector.
- Author:
Jinjia WANG
1
;
Bei HU
Author Information
1. College of Information Science and Engineer, Yanshan University, Qinhuangdao 066004, China. wjj@ysu.edu.cn
- Publication Type:Journal Article
- MeSH:
Algorithms;
Artificial Intelligence;
Brain-Computer Interfaces;
Discriminant Analysis;
Electroencephalography;
Face;
anatomy & histology;
Humans;
Pattern Recognition, Automated;
methods;
Principal Component Analysis;
Sample Size;
Signal Processing, Computer-Assisted;
User-Computer Interface
- From:
Journal of Biomedical Engineering
2013;30(1):12-27
- CountryChina
- Language:Chinese
-
Abstract:
Discriminative common vector (DCV) is an effective method that was proposed for the small sample size problems of face recognition. There is the same problem in brain-computer interface (BCI). Using directly the linear discriminative analysis (LDA) could result in errors because of the singularity of the within-class matrix of data. In our studies, we used the DCV method from the common vector theory in the within-class scatter matrix of data of all classes, and then applied eigenvalue decomposition to the common vectors to obtain the final projected vectors. Then we used kernel discriminative common vector (KDCV) with different kernel. Three data sets that include BCI Competition I data set, Competition II data set IV, and a data set collected by ourselves were used in the experiments. The experiment results of 93%, 77% and 97% showed that this feature extraction method could be used well in the classification of imagine data in BCI.
