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Extensions of Independent Component Analysis Mixture Models for classification and prediction of EEG signals

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Extensions of Independent Component Analysis Mixture Models for classification and prediction of EEG signals

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Safont Armero, G.; Salazar Afanador, A.; Rodríguez Martínez, A.; Vergara Domínguez, L. (2013). Extensions of Independent Component Analysis Mixture Models for classification and prediction of EEG signals. WAVES. 5:59-68. http://hdl.handle.net/10251/52797

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Title: Extensions of Independent Component Analysis Mixture Models for classification and prediction of EEG signals
Author:
UPV Unit: Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia
Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Issued date:
Abstract:
[EN] This paper presents two applications of Independent Component Analysis Mixture Modeling (ICAMM) for the classification and prediction of data. The first one of these extensions is Sequential ICAMM (SICAMM), an ICAMM ...[+]
Subjects: ICA mixture model , EEG , Prediction , Classification , Working-memory task
Copyrigths: Reserva de todos los derechos
Source:
WAVES. (issn: 1889-8297 )
Publisher version: http://www.iteam.upv.es/waves.php?id=6&lang=es
Thanks:
This work has been supported by Universitat Politècnica de Valencia under grant 20130072, Generalitat Valenciana under grants PROMETEO/2010/040 and ISIC/2012/006; and Spanish Administration and European Union FEDER Programme ...[+]
Type: Artículo

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