A way to apply DistanceBased Common Spatial Patterns (DBCSP) techniques in different fields, both classical Common Spatial Patterns (CSP) as well as DBCSP. The method is composed of two phases: applying the DBCSP algorithm and performing a classification. The main idea behind the CSP is to use a linear transform to project data into lowdimensional subspace with a projection matrix, in such a way that each row consists of weights for signals. This transformation maximizes the variance of twoclass 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.
Package details 


Author  Itziar Irigoien [aut], Concepción Arenas [aut], Itsaso RodríguezMoreno [cre, aut] 
Maintainer  Itsaso RodríguezMoreno <itsaso.rodriguez@ehu.eus> 
License  GPL (>= 2) 
Version  0.0.2.1 
Package repository  View on CRAN 
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