Feature extraction and classification of EEG for mental tasks based on wavelet packet analysis / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 397-400, 2004.
Artigo
em Chinês
| WPRIM
| ID: wpr-291103
ABSTRACT
This paper explores the use of wavelet packet analysis to extract features from spontaneous electroencephalogram (EEG) during three different mental tasks. Artifact-free EEG segments are transformed to multi-scale representations by dyadic wavelet packet decomposition channel by channel. Their feature vectors formed by energy values of different sub-spaces EEG components are used as inputs of a radial basis function network to test the classification accuracies of three task pairs. The results indicate that the classification accuracies of the wavelet packet analysis method are significantly better than those of autoregressive model method. Wavelet packet analysis would be a promising method to extract features from EEG signals.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Fisiologia
/
Processamento de Sinais Assistido por Computador
/
Análise Multivariada
/
Análise de Regressão
/
Modelos Estatísticos
/
Redes Neurais de Computação
/
Eletroencefalografia
/
Processos Mentais
Tipo de estudo:
Estudo diagnóstico
/
Estudo prognóstico
/
Fatores de risco
Limite:
Humanos
Idioma:
Chinês
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2004
Tipo de documento:
Artigo
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