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

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.

Itsaso Rodríguez (Department of Computation Science and Artificial Intelligence, University of the Basque Country UPV/EHU) , Itziar Irigoien (Department of Computation Science and Artificial Intelligence, University of the Basque Country UPV/EHU) , Basilio Sierra (Department of Computation Science and Artificial Intelligence, University of the Basque Country UPV/EHU) , Concepción Arenas (Department of Genetics, Microbiology and Statistics. Statistics Section, University of Barcelona UB)
2022-12-20

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Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-044.zip

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Citation

For attribution, please cite this work as

Rodríguez, et al., "The R Journal: dbcsp: User-friendly R package for Distance-Based Common Spatial Patterns", The R Journal, 2022

BibTeX citation

@article{RJ-2022-044,
  author = {Rodríguez, Itsaso and Irigoien, Itziar and Sierra, Basilio and Arenas, Concepción},
  title = {The R Journal: dbcsp: User-friendly R package for Distance-Based Common Spatial Patterns},
  journal = {The R Journal},
  year = {2022},
  note = {https://doi.org/10.32614/RJ-2022-044},
  doi = {10.32614/RJ-2022-044},
  volume = {14},
  issue = {3},
  issn = {2073-4859},
  pages = {80-94}
}