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.
| 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 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: |
NEWS |
| CRAN checks: |
dbcsp results |
Documentation:
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