Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison
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 heuri...
| Authors: | , , , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2018 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/75395 |
| Online Access: | https://hdl.handle.net/11441/75395 https://doi.org/10.3390/e20010007 |
| Access Level: | Open access |
| Keyword: | Common spatial patterns Generalized divergences Brain computer interfaces |
| Summary: | 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. |
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