Extraction and recognition of electroencephalogram dynamic patterns for brain-computer interfaces

Author

Trofimov, A.G. ; Skrugin, V.I. ; Rodriguez, A. M. Herrera

Author_Institution

Cybern. Dept., Nat. Res. Nucl. Univ. MEPhI, Moscow, Russia

fYear

2012

fDate

1-5 Oct. 2012

Firstpage

1

Lastpage

9

Abstract

We discuss an original approach to multidimensional non-stationary time series classification based on dynamic patterns analysis. The main problem in time series classification is construction of appropriate feature space. The success of classification dramatically depends on the quality of the feature space chosen. To construct this space we develop the method for extraction of dynamic patterns that are the most specific for the time series of each class. This problem is formulated as an optimization problem and the genetic algorithms are used to resolve it. The simulation results are given for the real electroencephalogram signals that are used in the brain-computer interfaces.

Keywords

brain-computer interfaces; electrocardiography; genetic algorithms; medical signal processing; signal classification; time series; brain-computer interfaces; dynamic patterns analysis; electroencephalogram dynamic patterns; electroencephalogram signals; feature space; genetic algorithms; multidimensional nonstationary time series classification; optimization problem; signal classification; signal extraction; signal recognition; Accuracy; Classification algorithms; Electroencephalography; Feature extraction; Support vector machine classification; Time series analysis; Vectors; brain-computer interface; classification; clustering; dynamic patterns; electroencephalogram; feature extraction; genetic algorithm; multidimensional time series;

fLanguage

English

Publisher

ieee

Conference_Titel

Informatica (CLEI), 2012 XXXVIII Conferencia Latinoamericana En

Conference_Location

Medellin

Print_ISBN

978-1-4673-0794-9

Type

conf

DOI

10.1109/CLEI.2012.6427163

Filename

6427163