English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.Ministerio de Economía y Competitividad TEC2014-53103-

Optimization of Alpha-Beta Log-Det Divergences and their Application in the Spatial Filtering of Two Class Motor Imagery Movements