Fuzzy entropy relevance analysis in DWT and EMD for BCI motor imagery applications
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| Natura: | Artículo científico |
| Lingua: | en |
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Universidad Distrital Francisco José de Caldas
2015
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| _version_ | 1876489738346561536 |
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| author | Boris Medina Salgado |
| author_facet | Boris Medina Salgado |
| contents | Fuzzy entropy relevance analysis in DWT and EMD for BCI motor imagery applications Boris Medina Salgado Leonardo Duque Muñoz Ingeniería EMD BCI wavelet Fuzzy entropy Rhythm analysis in advanced signal processing methods has long of interest in applicationareas such as diagnosis of brain disorders, epilepsy, sleep or anesthesia analysis,and more recently in brain computer interfaces. In this paper the Discrete WaveletTransform (DWT) and Empirical Mode Decomposition (EMD) techniques are appliedto extract the brain rhythms from electroencephalographic (EEG) signals in motor imaginationtasks, of left-and right hand, using public dataset BCI Competition 2003. Thenthe brain rhythms are characterized by statistical features. Additionally, fuzzy entropyalgorithm was used to perform the relevance analysis to determine the most importantfeatures in the training set. Classification stage was performed using K-NN classifiersand SVM, obtaining classification accuracy up to 100% with EMD. Classification resultsallow us to infer that the techniques used are appropriate to generate solutions inBCI applications for recognizing motor imagination in people with motor disabilities. 2015 artículo científico 0121-750X https://www.redalyc.org/articulo.oa?id=498850180002 en http://www.redalyc.org/revista.oa?id=4988 Ingeniería application/pdf Universidad Distrital Francisco José de Caldas Ingeniería (Colombia) Num.1 Vol.20 |
| format | Artículo científico |
| id | redalyc_498850180002 |
| institution | Redalyc |
| language | en |
| publishDate | 2015 |
| publisher | Universidad Distrital Francisco José de Caldas |
| spellingShingle | Fuzzy entropy relevance analysis in DWT and EMD for BCI motor imagery applications Boris Medina Salgado Ingeniería EMD BCI wavelet Fuzzy entropy Fuzzy entropy relevance analysis in DWT and EMD for BCI motor imagery applications Boris Medina Salgado Leonardo Duque Muñoz Ingeniería EMD BCI wavelet Fuzzy entropy Rhythm analysis in advanced signal processing methods has long of interest in applicationareas such as diagnosis of brain disorders, epilepsy, sleep or anesthesia analysis,and more recently in brain computer interfaces. In this paper the Discrete WaveletTransform (DWT) and Empirical Mode Decomposition (EMD) techniques are appliedto extract the brain rhythms from electroencephalographic (EEG) signals in motor imaginationtasks, of left-and right hand, using public dataset BCI Competition 2003. Thenthe brain rhythms are characterized by statistical features. Additionally, fuzzy entropyalgorithm was used to perform the relevance analysis to determine the most importantfeatures in the training set. Classification stage was performed using K-NN classifiersand SVM, obtaining classification accuracy up to 100% with EMD. Classification resultsallow us to infer that the techniques used are appropriate to generate solutions inBCI applications for recognizing motor imagination in people with motor disabilities. 2015 artículo científico 0121-750X https://www.redalyc.org/articulo.oa?id=498850180002 en http://www.redalyc.org/revista.oa?id=4988 Ingeniería application/pdf Universidad Distrital Francisco José de Caldas Ingeniería (Colombia) Num.1 Vol.20 |
| title | Fuzzy entropy relevance analysis in DWT and EMD for BCI motor imagery applications |
| topic | Ingeniería EMD BCI wavelet Fuzzy entropy |
| url | https://www.redalyc.org/articulo.oa?id=498850180002 |