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

TL;DR

The paper introduces an R package called dbcsp that extends the Common Spacial Patterns method to use various distance measures for EEG data analysis, enhancing flexibility in brain activity classification.

Contribution

It presents a user-friendly R package implementing both classical CSP and a new Distance-Based CSP method for improved EEG data analysis.

Findings

Implemented in R package dbcsp

Supports multiple distance measures for CSP

Facilitates EEG classification with flexible metrics

Abstract

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.