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

Chapter
Part of the Studies in Big Data book series (SBD, volume 40)

Abstract

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.

Keywords

EEG Electroencephalography Imaginary motion Classification 

Notes

Acknowledgements

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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