Study on the method of feature extraction for brain-computer interface using discriminative common vector / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 12-27, 2013.
Artigo
em Chinês
| WPRIM
| ID: wpr-246471
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.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Algoritmos
/
Processamento de Sinais Assistido por Computador
/
Interface Usuário-Computador
/
Reconhecimento Automatizado de Padrão
/
Inteligência Artificial
/
Análise Discriminante
/
Tamanho da Amostra
/
Análise de Componente Principal
/
Eletroencefalografia
/
Face
Tipo de estudo:
Estudo prognóstico
Limite:
Humanos
Idioma:
Chinês
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2013
Tipo de documento:
Artigo
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