  Common Spacial Patterns (CSP) is a widely used method to analyse
electroencephalography (EEG) data, concerning the supervised classification of
brain's activity. 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 to project the data into a
low-dimensional subspace. Once 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.
