# name
dbcsp: User-friendly R package for Distance-Based Common Spacial Patterns

# metadata.dc.description.abstract[].value
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.

# metadata.dc.rights[].value
cc-by (c) Rodríguez, Itsaso et al., 2022

# metadata.dc.source[].value
Articles publicats en revistes (Genètica, Microbiologia i Estadística)

# metadata.dc.title[].value
dbcsp: User-friendly R package for Distance-Based Common Spacial Patterns