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Mu-suppression detection in motor imagery electroencephalographic signals using the generalized extreme value distribution

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dc.contributor.author Quintero Rincón, Antonio
dc.contributor.author D’Giano, Carlos
dc.contributor.author Batatia, Hadj
dc.date.accessioned 2021-09-07T15:11:22Z
dc.date.available 2021-09-07T15:11:22Z
dc.date.issued 2020-06-19
dc.identifier.citation Quintero Rincón A, D’Giano C, Batatia H. Mu-suppression detection in motor imagery electroencephalographic signals using the generalized extreme value distribution, 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1-5, doi: 10.1109/IJCNN48605.2020.9206862. es_ES
dc.identifier.uri https://repositorio.fleni.org.ar/xmlui/handle/123456789/577
dc.identifier.uri https://ieeexplore.ieee.org/document/9206862
dc.description.abstract This paper deals with the detection of mu-suppression from electroencephalographic (EEG) signals in brain-computer interface (BCI). For this purpose, an efficient algorithm is proposed based on a statistical model and a linear classifier. Precisely, the generalized extreme value distribution (GEV) is proposed to represent the power spectrum density of the EEG signal in the central motor cortex. The associated three parameters are estimated using the maximum likelihood method. Based on these parameters, a simple and efficient linear classifier was designed to classify three types of events: imagery, movement, and resting. Preliminary results show that the proposed statistical model can be used in order to detect precisely the mu-suppression and distinguish different EEG events, with very good classification accuracy. es_ES
dc.language.iso eng es_ES
dc.publisher IEEE es_ES
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by/2.5/ar/
dc.subject Electroencephalography es_ES
dc.subject Electroencefalografía es_ES
dc.subject Epilepsy es_ES
dc.subject Epilepsia es_ES
dc.title Mu-suppression detection in motor imagery electroencephalographic signals using the generalized extreme value distribution es_ES
dc.type info:eu-repo/semantics/other es_ES
dc.type info:eu-repo/semantics/publishedVersion
dc.description.fil Fil: D’Giano, Carlos. Fleni. Centro Integral de Epilepsia y Unidad de Monitoreo de Videoelectroencefalografía; Argentina.
dc.description.fil Fil: Quintero Rincón, Antonio. Instituto Tecnológico de Buenos Aires. Departamento de Bioingeniería; Argentina.
dc.description.fil Fil: Batatia, Hadj. University of Toulouse; Francia.
dc.relation.ispartofCOUNTRY Reino Unido
dc.relation.ispartofCITY Glasgow
dc.relation.ispartofTITLE 2020 International Joint Conference on Neural Networks (IJCNN)
dc.relation.ispartofISSN 2161-4407
dc.type.snrd info:ar-repo/semantics/artículo es_ES


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