Optimization of Alpha-Beta Log-Det divergences and their application in the spatial filtering of two class motor imagery movements

Deepa Beeta Thiyam, Sergio Cruces, Javier Olias, Andrzej Cichocki

    Research output: Contribution to journalArticlepeer-review

    15 Citations (Scopus)

    Abstract

    The Alpha-Beta Log-Det divergences for positive definite matrices are flexible divergences that are parameterized by two real constants and are able to specialize several relevant classical cases like the squared Riemannian metric, the Steins loss, the S-divergence, etc. A novel classification criterion based on these divergences is optimized to address the problem of classification of the motor imagery movements. This research paper is divided into three main sections in order to address the above mentioned problem: (1) Firstly, it is proven that a suitable scaling of the class conditional covariance matrices can be used to link the Common Spatial Pattern (CSP) solution with a predefined number of spatial filters for each class and its representation as a divergence optimization problem by making their different filter selection policies compatible; (2) A closed form formula for the gradient of the Alpha-Beta Log-Det divergences is derived that allows to perform optimization as well as easily use it in many practical applications; (3) Finally, in similarity with the work of Samek et al. 2014, which proposed the robust spatial filtering of the motor imagery movements based on the beta-divergence, the optimization of the Alpha-Beta Log-Det divergences is applied to this problem. The resulting subspace algorithm provides a unified framework for testing the performance and robustness of the several divergences in different scenarios.

    Original languageEnglish
    Article number89
    JournalEntropy
    Volume19
    Issue number3
    DOIs
    Publication statusPublished - 2017

    Keywords

    • Alpha-Beta Log-Det divergence
    • Brain Computer Interface (BCI)
    • Common Spatial Pattern (CSP)
    • Generalized divergences for symmetric positive definite matrices
    • Similarity measures

    Fingerprint

    Dive into the research topics of 'Optimization of Alpha-Beta Log-Det divergences and their application in the spatial filtering of two class motor imagery movements'. Together they form a unique fingerprint.

    Cite this