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

1
Departamento de Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Seville, Spain
2
Department of Sensor and Biomedical Technology, School of Electronics Engineering, VIT University, Vellore, Tamil Nadu 632014, India
3
Skolkovo Institute of Science and Technology (Skoltech), Moscow 143026, Russia
4
Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
5
Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland
*
Authors to whom correspondence should be addressed.
Entropy 2018, 20(1), 7; https://doi.org/10.3390/e20010007
Received: 31 October 2017 / Revised: 10 December 2017 / Accepted: 19 December 2017 / Published: 2 January 2018
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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Abstract

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. View Full-Text
Keywords: common spatial patterns; generalized divergences; brain computer interfaces common spatial patterns; generalized divergences; brain computer interfaces
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Martín-Clemente, R.; Olias, J.; Thiyam, D.B.; Cichocki, A.; Cruces, S. Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison. Entropy 2018, 20, 7.

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