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

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Piotr Szczuko, Michal Lech, Andrzej Czyzewski

Published: 2019, Last Modified: 06 May 2026Intelligent Methods and Big Data in Industrial Applications 2019EveryoneRevisionsBibTeXCC BY-SA 4.0
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.