Document type
ArticleVersion
Published versionPublication date
Publication license
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/193826
dbcsp: User-friendly R package for Distance-Based Common Spacial Patterns
Journal Title
Director/Tutor
Journal ISSN
Volume Title
Related resource
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.
Subject (English)
Citation
Citation
RODRÍGUEZ, Itsaso, et al. dbcsp: User-friendly R package for Distance-Based Common Spacial Patterns. The R Journal. 2022. Vol. 14, num. 3, pags. 80-94. ISSN 2073-4859. [consulted: 8 of June of 2026]. Available at: https://hdl.handle.net/2445/193826