Fuzzy Entropy relevance analysis in DWT and EMD for BCI motor imagery applications
| dc.contributor.author | Medina Salgado, Boris | spa |
| dc.contributor.author | Duque Muñoz, Leonardo | spa |
| dc.date | 2017-10-10 | |
| dc.date.accessioned | 2019-09-19T21:38:47Z | |
| dc.date.available | 2019-09-19T21:38:47Z | |
| dc.description | Rhythm analysis in advanced signal processing methods has long of interest in application areas such as diagnosis of brain disorders, epilepsy, sleep or anesthesia analysis, and more recently in brain computer interfaces. In this paper the Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) techniques are applied to extract the brain rhythms from electroencephalographic (EEG) signals in motor imagination tasks, of left-and right hand, using public dataset BCI Competition 2003. Then the brain rhythms are characterized by statistical features. Additionally, fuzzy entropy algorithm was used to perform the relevance analysis to determine the most important features in the training set. Classification stage was performed using K-NN classifiers and SVM, obtaining classification accuracy up to 100% with EMD. Classification results allow us to infer that the techniques used are appropriate to generate solutions in BCI applications for recognizing motor imagination in people with motor disabilities. | en-US |
| dc.description | Rhythm analysis in advanced signal processing methods has long of interest in application areas such as diagnosis of brain disorders, epilepsy, sleep or anesthesia analysis, and more recently in brain computer interfaces. In this paper the Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) techniques are applied to extract the brain rhythms from electroencephalographic (EEG) signals in motor imagination tasks, of left-and right hand, using public dataset BCI Competition 2003. Then the brain rhythms are characterized by statistical features. Additionally, fuzzy entropy algorithm was used to perform the relevance analysis to determine the most important features in the training set. Classification stage was performed using K-NN classifiers and SVM, obtaining classification accuracy up to 100% with EMD. Classification results allow us to infer that the techniques used are appropriate to generate solutions in BCI applications for recognizing motor imagination in people with motor disabilities. | es-ES |
| dc.identifier | https://revistas.udistrital.edu.co/index.php/reving/article/view/7455 | |
| dc.identifier | 10.14483/23448393.7455 | |
| dc.identifier.uri | http://hdl.handle.net/11349/19810 | |
| dc.language | en | |
| dc.publisher | Universidad Distrital Francisco José de Caldas | en-US |
| dc.rights | Copyright (c) 2015 Ingeniería | en-US |
| dc.source | Ingeniería; Vol 1 No 1 | en-US |
| dc.source | Ingeniería; Vol. 1 Núm. 1 | es-ES |
| dc.source | 2344-8393 | |
| dc.source | 0121-750X | |
| dc.title | Fuzzy Entropy relevance analysis in DWT and EMD for BCI motor imagery applications | en-US |
| dc.title | Fuzzy Entropy relevance analysis in DWT and EMD for BCI motor imagery applications | es-ES |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion | |
| dc.type | Article | en-US |
| dc.type | Artículo | es-ES |
| dc.type.coar | http://purl.org/coar/resource_type/c_6501 |