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Facing High EEG Signals Variability during Classification Using Fractal Dimension and Different Cutoff Frequencies.
Salazar-Varas, R; Vazquez, Roberto A.
Afiliação
  • Salazar-Varas R; Escuela de Ingeniería, Universidad de las Americas Puebla, Sta. Catarina Mártir, Puebla, CP 72810 San Andrés Cholula, Mexico.
  • Vazquez RA; Intelligent Systems Group, Facultad de Ingeniería, Universidad La Salle México, Benjamin Franklin 45, Col. Condesa, CP 06140 Mexico City, Mexico.
Comput Intell Neurosci ; 2019: 9174307, 2019.
Article em En | MEDLINE | ID: mdl-31236108
In the development of a brain-computer interface (BCI), some issues should be regarded in order to improve its reliability and performance. Perhaps, one of the most challenging issues is related to the high variability of the brain signals, which directly impacts the accuracy of the classification. In this sense, novel feature extraction techniques should be explored in order to select those able to face this variability. Furthermore, to improve the performance of the selected feature extraction technique, the parameters of the filter applied in the preprocessing stage need to be properly selected. Then, this work presents an analysis of the robustness of the fractal dimension as feature extraction technique under high variability of the EEG signals, particularly when the training data are recorded one day and the testing data are obtained on a different day. The results are compared with those obtained by an autoregressive model, which is a technique commonly used in BCI applications. Also, the effect of properly selecting the cutoff frequencies of the filter in the preprocessing stage is evaluated. This research is supported by several experiments carried out using a public data set from the BCI international competition, specifically data set 2a from BCIIC IV, related to motor tasks. By a statistical test, it is demonstrated that the performance achieved using the fractal dimension is significantly better than that reached by the AR model. Also, it is demonstrated that the selection of the appropriate cutoff frequencies improves significantly the performance in the classification. The increase rate is approximately of 17%.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Sinais Assistido por Computador / Fractais / Eletroencefalografia / Interfaces Cérebro-Computador Limite: Humans Idioma: En Revista: Comput Intell Neurosci Assunto da revista: INFORMATICA MEDICA / NEUROLOGIA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: México País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Sinais Assistido por Computador / Fractais / Eletroencefalografia / Interfaces Cérebro-Computador Limite: Humans Idioma: En Revista: Comput Intell Neurosci Assunto da revista: INFORMATICA MEDICA / NEUROLOGIA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: México País de publicação: Estados Unidos