Real and imaginary motion classification based on rough set analysis of EEG signals for multimedia applications

Rough set-based approach to the classification of EEG signals of real and imaginary motion is presented. The pre-processing and signal parametrization procedures are described, the rough set theory is briefly introduced, and several classification scenarios and parameters selection methods are propo...

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Bibliographic Details
Published inMultimedia tools and applications Vol. 76; no. 24; pp. 25697 - 25711
Main Author Szczuko, Piotr
Format Journal Article
LanguageEnglish
Published New York Springer US 01.12.2017
Springer Nature B.V
Subjects
Classification
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Electroencephalography
Multimedia
Multimedia Information Systems
Parameterization
Parameters
Set theory
Signal classification
Signal processing
Special Purpose and Application-Based Systems
Signal processing
EEG
Classification
Rough set
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Summary:Rough set-based approach to the classification of EEG signals of real and imaginary motion is presented. The pre-processing and signal parametrization procedures are described, the rough set theory is briefly introduced, and several classification scenarios and parameters selection methods are proposed. Classification results are provided and discussed with their potential utilization for multimedia applications controlled by the motion intent. Accuracy metrics of classification for real and imaginary motion obtained with different parameter sets are compared. Results of experiments employing recorded EEG signals are commented and further research directions are proposed.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-017-4458-7