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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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Keywords
Details
- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Title of issue:
- W : Intelligent Methods and Big Data in Industrial Applications strony 247 - 257
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Szczuko P., Lech M., Czyżewski A.: Comparison of Methods for Real and Imaginary Motion Classification from EEG Signals// Intelligent Methods and Big Data in Industrial Applications/ ed. Bembenik R., Skonieczny Ł., Protaziuk G., Kryszkiewicz M., Rybinski H. Cham: Springer, 2018, s.247-257
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-319-77604-0_18
- Sources of funding:
- Verified by:
- Gdańsk University of Technology
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