Comparison of Methods for Real and Imaginary Motion Classification from EEG Signals

  • Piotr Szczuko
  • Michał Lech
  • Andrzej Czyżewski
Part of the Studies in Big Data book series (SBD, volume 40)


A method for feature extraction and results of classification of EEG signals obtained from performed and imagined motion are presented. A set of 615 features was obtained to serve for the recognition of type and laterality of motion using 8 different classifications approaches. A comparison of achieved classifiers accuracy is presented in the paper, and then conclusions and discussion are provided. Among applied algorithms the highest accuracy was achieved with: Rough Set, SVM and ANN methods.


EEG Electroencephalography Imaginary motion Classification 



The research is funded by the National Science Centre of Poland on the basis of the decision DEC-2014/15/B/ST7/04724.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Piotr Szczuko
    • 1
  • Michał Lech
    • 1
  • Andrzej Czyżewski
    • 1
  1. 1.Faculty of Electronics, Telecommunications and InformaticsGdańsk University of TechnologyGdańskPoland

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