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

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Intelligent Methods and Big Data in Industrial Applications

Part of the book series: Studies in Big Data ((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.

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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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Authors and Affiliations

  1. Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gdańsk, Poland

    Piotr Szczuko, Michał Lech & Andrzej Czyżewski

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  1. Piotr Szczuko
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  2. Michał Lech
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  3. Andrzej Czyżewski
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Correspondence to Piotr Szczuko .

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Editors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

    Robert Bembenik

  2. Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

    Łukasz Skonieczny

  3. Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

    Grzegorz Protaziuk

  4. Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

    Marzena Kryszkiewicz

  5. Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

    Henryk Rybinski

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Szczuko, P., Lech, M., Czyżewski, A. (2019). Comparison of Methods for Real and Imaginary Motion Classification from EEG Signals. In: Bembenik, R., Skonieczny, Ł., Protaziuk, G., Kryszkiewicz, M., Rybinski, H. (eds) Intelligent Methods and Big Data in Industrial Applications. Studies in Big Data, vol 40. Springer, Cham. https://doi.org/10.1007/978-3-319-77604-0_18

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