Resumen
Common Spatial Patterns (CSP) is a widely used method to analyse electroencephalography
(EEG) data, concerning the supervised classification of the activity of brain. More generally, it can
be useful to distinguish between multivariate signals recorded during a time span for two different
classes. CSP is based on the simultaneous diagonalization of the average covariance matrices of signals
from both classes and it allows the data to be projected into a low-dimensional subspace. Once the
data are represented in a low-dimensional subspace, a classification step must be carried out. The
original CSP method is based on the Euclidean distance between signals, and here we extend it so that
it can be applied on any appropriate distance for data at hand. Both the classical CSP and the new
Distance-Based CSP (DB-CSP) are implemented in an R package, called dbcsp.