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Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison

 

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Opened Access Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison
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Autor: Martín-Clemente, Rubén
Olias Sánchez, Francisco Javier
Thiyam, Deepa Beeta
Cichocki, Andrzej
Cruces Álvarez, Sergio Antonio
Departamento: Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones
Fecha: 2018-01-02
Publicado en: Entropy, 20 (1), 1-29.
Tipo de documento: Artículo
Resumen: Brain computer interfaces (BCIs) have been attracting a great interest in recent years. The common spatial patterns (CSP) technique is a well-established approach to the spatial filtering of the electroencephalogram (EEG) data in BCI applications. Even though CSP was originally proposed from a heuristic viewpoint, it can be also built on very strong foundations using information theory. This paper reviews the relationship between CSP and several information-theoretic approaches, including the Kullback–Leibler divergence, the Beta divergence and the Alpha-Beta log-det (AB-LD)divergence. We also revise other approaches based on the idea of selecting those features that are maximally informative about the class labels. The performance of all the methods will be also compared via experiments.
Cita: Martín-Clemente, R., Olias Sánchez, F.J., Thiyam, D.B., Cichocki, A. y Cruces Álvarez, S.A. (2018). Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison. Entropy, 20 (1), 1-29.
Tamaño: 1012.Kb
Formato: PDF

URI: https://hdl.handle.net/11441/75395

DOI: 10.3390/e20010007

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