Brain-computer interface using wavelet transformation and naïve bayes classifier.
Adv Exp Med Biol
; 657: 147-65, 2010.
Article
em En
| MEDLINE
| ID: mdl-20020346
The main purpose of this work is to establish an exploratory approach using electroencephalographic (EEG) signal, analyzing the patterns in the time-frequency plane. This work also aims to optimize the EEG signal analysis through the improvement of classifiers and, eventually, of the BCI performance. In this paper a novel exploratory approach for data mining of EEG signal based on continuous wavelet transformation (CWT) and wavelet coherence (WC) statistical analysis is introduced and applied. The CWT allows the representation of time-frequency patterns of the signal's information content by WC qualiatative analysis. Results suggest that the proposed methodology is capable of identifying regions in time-frequency spectrum during the specified task of BCI. Furthermore, an example of a region is identified, and the patterns are classified using a Naïve Bayes Classifier (NBC). This innovative characteristic of the process justifies the feasibility of the proposed approach to other data mining applications. It can open new physiologic researches in this field and on non stationary time series analysis.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Sinais Assistido por Computador
/
Interface Usuário-Computador
/
Encéfalo
/
Mapeamento Encefálico
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Adv Exp Med Biol
Ano de publicação:
2010
Tipo de documento:
Article
País de afiliação:
Brasil
País de publicação:
Estados Unidos