Registro completo de metadatos
| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.creator | Diez, Pablo Federico | - |
| dc.creator | Mut, Vicente Antonio | - |
| dc.creator | Laciar Leber, Eric | - |
| dc.creator | Torres, Abel | - |
| dc.creator | Avila Perona, Enrique Mario | - |
| dc.date | 2017-10-17T15:36:20Z | - |
| dc.date | 2017-10-17T15:36:20Z | - |
| dc.date | 2013-07 | - |
| dc.date | 2017-10-12T21:22:54Z | - |
| dc.date.accessioned | 2019-04-29T15:46:23Z | - |
| dc.date.available | 2019-04-29T15:46:23Z | - |
| dc.date.issued | 2013-07 | - |
| dc.identifier | Diez, Pablo Federico; Mut, Vicente Antonio; Laciar Leber, Eric; Torres, Abel; Avila Perona, Enrique Mario; Features extraction method for brain-machine communication based on the empirical mode decomposition; World Scientific; Biomedical Engineering-applications Basis Communications; 25; 6; 7-2013; 1-13 | - |
| dc.identifier | 1016-2372 | - |
| dc.identifier | http://hdl.handle.net/11336/26707 | - |
| dc.identifier | 1793-7132 | - |
| dc.identifier | CONICET Digital | - |
| dc.identifier | CONICET | - |
| dc.identifier.uri | http://rodna.bn.gov.ar:8080/jspui/handle/bnmm/301615 | - |
| dc.description | A brain-machine interface (BMI) is a communication system that translates human brain activity into commands, and then these commands are conveyed to a machine or a computer. It is proposes a technique for features extraction from electroencephalographic (EEG) signals and afterward, their classification on different mental tasks. The empirical mode decomposition (EMD) is a method capable of processing non-stationary and nonlinear signals, as the EEG. The EMD was applied on EEG signals of seven subjects performing five mental tasks. Six features were computed, namely, root mean square (RMS), variance, Shannon entropy, Lempel-Ziv complexity value, and central and maximum frequencies. In order to reduce the dimensionality of the feature vector, the Wilks´ lambda (WL) parameter was used for the selection of the most important variables. The classification of mental tasks was performed using linear discriminant analysis (LDA) and neural networks (NN). Using this method, the average classification over all subjects in database is 91 ± 5% and 87 ± 5% using LDA and NN, respectively. Bit rate was ranging from 0.24 bits/trial up to 0.84 bits/trial. The proposed method allows achieving higher performances in the classification of mental tasks than other traditional methods using the same database. This represents an improvement in the brain-machine communication system. | - |
| dc.description | Fil: Diez, Pablo Federico. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina | - |
| dc.description | Fil: Mut, Vicente Antonio. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina | - |
| dc.description | Fil: Laciar Leber, Eric. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina | - |
| dc.description | Fil: Torres, Abel. Universidad Politécnica de Catalunya. Departamento de Ing.de Sistemas, Automat.e Inf.industrial; España | - |
| dc.description | Fil: Avila Perona, Enrique Mario. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina | - |
| dc.format | application/pdf | - |
| dc.format | application/pdf | - |
| dc.format | application/pdf | - |
| dc.format | application/pdf | - |
| dc.language | eng | - |
| dc.publisher | World Scientific | - |
| dc.relation | info:eu-repo/semantics/altIdentifier/url/http://www.worldscientific.com/doi/abs/10.4015/S1016237213500580 | - |
| dc.relation | info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1142/S1016237213500580 | - |
| dc.rights | info:eu-repo/semantics/restrictedAccess | - |
| dc.rights | https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ | - |
| dc.source | reponame:CONICET Digital (CONICET) | - |
| dc.source | instname:Consejo Nacional de Investigaciones Científicas y Técnicas | - |
| dc.source | instacron:CONICET | - |
| dc.subject | Brain-machine interface (BMI) | - |
| dc.subject | Brain-computer interface (BCI) | - |
| dc.subject | Empirical mode decomposition (EMD) | - |
| dc.subject | Feature extraction | - |
| dc.title | Features extraction method for brain-machine communication based on the empirical mode decomposition | - |
| dc.type | info:eu-repo/semantics/article | - |
| dc.type | info:eu-repo/semantics/publishedVersion | - |
| dc.type | info:ar-repo/semantics/articulo | - |
| Aparece en las colecciones: | CONICET | |
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