Information theoretic approaches for motor-imagery BCI systems: Review and experimental comparison

Rubén Martín-Clemente, Javier Olias, Deepa Beeta Thiyam, Andrzej Cichocki, Sergio Cruces

    Research output: Contribution to journalReview articlepeer-review

    19 Citations (Scopus)

    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.

    Original languageEnglish
    Article number7
    JournalEntropy
    Volume20
    Issue number1
    DOIs
    Publication statusPublished - 1 Jan 2018

    Keywords

    • Brain computer interfaces
    • Common spatial patterns
    • Generalized divergences

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