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2019 | OriginalPaper | Chapter

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

Authors : Piotr Szczuko, Michał Lech, Andrzej Czyżewski

Published in: Intelligent Methods and Big Data in Industrial Applications

Publisher: Springer International Publishing

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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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Title: Comparison of Methods for Real and Imaginary Motion Classification from EEG Signals
Authors: Piotr Szczuko
Michał Lech
Andrzej Czyżewski
Publisher: Springer International Publishing
Book: Intelligent Methods and Big Data in Industrial Applications
Print ISBN: 978-3-319-77603-3

Electronic ISBN: 978-3-319-77604-0

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-319-77604-0_18

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