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dc.contributor.authorVillar, Ana Julia
dc.date.accessioned2019-09-12T11:29:15Z
dc.date.available2019-09-12T11:29:15Z
dc.date.issued2017-10
dc.identifier.isbn978-981104085-6
dc.identifier.urihttp://ri.itba.edu.ar/handle/123456789/1746
dc.description.abstract" Common spatial patterns analysis and linear discriminant analysis are popular algorithms for spatial filtering and classifying in motor imagery. These algorithms are very sensitive to noise and artifacts which affect the classification accuracy. To deal with this issue, it is proposed to replace the usual estimators of covariance and scale used in the algorithms for robust versions. The performance of the methods are evaluated and compared on EGG data from BCI competition data sets; results show that robust methods outperformed classical techniques for subjects with poor classification accuracy. "en
dc.language.isoenen
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/10.1007/978-981-10-4086-3_32
dc.subjectANALISIS DISCRIMINANTEes
dc.subjectSISTEMAS DE CONTROLes
dc.subjectCLASIFICACIONes
dc.subjectBIOINGENIERIAes
dc.titleComparative study of robust methods for motor imagery classification based on CSP and LDAen
dc.typePonencias en Congresoses
dc.typeinfo:eu-repo/semantics/acceptedVersion
itba.description.filiationFil: Villar, Ana Julia. Instituto Tecnológico de Buenos Aires; Argentina.


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