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Title: dbcsp: User-friendly R package for Distance-Based Common Spacial Patterns
Author: Rodríguez, Itsaso
Irigoien, Itziar
Sierra, Basilio
Arenas Solà, Concepción
Keywords: R (Llenguatge de programació)
R (Computer program language)
Issue Date: 2022
Publisher: The R Foundation
Abstract: 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.
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It is part of: The R Journal, 2022, vol. 14, num. 3, p. 80-94
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ISSN: 2073-4859
Appears in Collections:Articles publicats en revistes (Genètica, Microbiologia i Estadística)

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