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.
Version: 0.0.2.1 Depends: caret, R (≥ 2.10), TSdist (≥ 3.7) Imports: geigen, ggplot2, MASS, Matrix, methods, parallelDist, plyr, stats, zoo Suggests: testthat (≥ 3.0.0) Published: 2022-06-30 DOI: 10.32614/CRAN.package.dbcsp Author: Itziar Irigoien [aut], Concepción Arenas [aut], Itsaso RodrÃguez-Moreno [cre, aut] Maintainer: Itsaso RodrÃguez-Moreno <itsaso.rodriguez at ehu.eus> License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] NeedsCompilation: no Materials: NEWS CRAN checks: dbcsp results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=dbcsp to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4