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Classifier Selection for Motor Imagery Brain Computer Interface

  • Izabela RejerEmail author
  • Robert Burduk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10244)

Abstract

The classification process in the domain of brain computer interfaces (BCI) is usually carried out with simple linear classifiers, like LDA or SVM. Non-linear classifiers rarely provide a sufficient increase in the classification accuracy to use them in BCI. However, there is one more type of classifiers that could be taken into consideration when looking for a way to increase the accuracy - boosting classifiers. These classification algorithms are not common in BCI practice, but they proved to be very efficient in other applications.

Keywords

Imagery brain computer interface Classification Boosting 

Notes

Acknowledgments

This work was supported in part by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Science and Technology.

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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of TechnologySzczecinPoland
  2. 2.Department of Systems and Computer NetworksWroclaw University of Science and TechnologyWrocławPoland

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