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.
|Author||Itziar Irigoien [aut], Concepción Arenas [aut], Itsaso Rodríguez-Moreno [cre, aut]|
|Maintainer||Itsaso Rodríguez-Moreno <email@example.com>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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