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

Piotr Szczuko, Michal Lech, Andrzej Czyzewski

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.