Application of multiscale amplitude modulation features and fuzzy C-means to brain-computer interface.
Clin EEG Neurosci
; 43(1): 32-8, 2012 Jan.
Article
em En
| MEDLINE
| ID: mdl-22423549
ABSTRACT
This study proposed a recognized system for electroencephalogram (EEG) data classification. In addition to the wavelet-based amplitude modulation (AM) features, the fuzzy c-means (FCM) clustering is used for the discriminant of left finger lifting and resting. The features are extracted from discrete wavelet transform (DWT) data with the AM method. The FCM is then applied to recognize extracted features. Compared with band power features, k-means clustering, and linear discriminant analysis (LDA) classifier, the results indicate that the proposed method is satisfactory in applications of brain-computer interface (BCI).
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Interface Usuário-Computador
/
Encéfalo
/
Reconhecimento Automatizado de Padrão
/
Lógica Fuzzy
/
Potencial Evocado Motor
/
Eletroencefalografia
Tipo de estudo:
Diagnostic_studies
Limite:
Female
/
Humans
/
Male
Idioma:
En
Ano de publicação:
2012
Tipo de documento:
Article