dbcsp: Distance-Based Common Spatial Patterns

A way to apply Distance-Based Common Spatial Patterns (DB-CSP) techniques in different fields, both classical Common Spatial Patterns (CSP) as well as DB-CSP. The method is composed of two phases: applying the DB-CSP algorithm and performing a classification. The main idea behind the CSP is to use a linear transform to project data into low-dimensional subspace with a projection matrix, in such a way that each row consists of weights for signals. This transformation maximizes the variance of two-class signal matrices.The dbcsp object is created to compute the projection vectors. For exploratory and descriptive purpose, plot and boxplot functions can be used. Functions train, predict and selectQ are implemented for the classification step.

Depends: R (≥ 2.10), TSdist (≥ 3.7)
Imports: plyr, parallelDist, Matrix, geigen, caret, MASS, stats, ggplot2, methods
Published: 2021-07-09
Author: Itziar Irigoien [aut], Concepción Arenas [aut], Itsaso Rodríguez-Moreno [cre, aut]
Maintainer: Itsaso Rodríguez-Moreno <itsaso.rodriguez at ehu.eus>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README
CRAN checks: dbcsp results


Reference manual: dbcsp.pdf


Package source: dbcsp_0.0.1.0.tar.gz
Windows binaries: r-devel: dbcsp_0.0.1.0.zip, r-release: dbcsp_0.0.1.0.zip, r-oldrel: dbcsp_0.0.1.0.zip
macOS binaries: r-release (arm64): dbcsp_0.0.1.0.tgz, r-release (x86_64): dbcsp_0.0.1.0.tgz, r-oldrel: dbcsp_0.0.1.0.tgz


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