Using ART2 for clustering of Gabor atoms describing ERP P3 waveforms

Author

Rondik, Tomas ; Mautner, Pavel

Author_Institution

Dept. of Comput. Sci. & Eng., Univ. of West Bohemia, Pilsen, Czech Republic

fYear

2012

fDate

16-18 Oct. 2012

Firstpage

614

Lastpage

618

Abstract

This paper deals with a suitable method for decomposition of EEG/ERP signal to waveforms which are grouped is such way that one or few groups contain ERP P3 waveforms. At the beginning, the EEG/ERP domain is briefly introduced and essential information about EEG and ERP signals is given. Then, the method for waveforms grouping based on matching pursuit algorithm with Gabor dictionary as a preprocessing method for feature extraction for ART2 neural network is explained in detail. Emphasis is placed on selection of suitable feature extraction method. Comparison of tested feature extraction methods and summarization is given at the end.

Keywords

decomposition; electroencephalography; feature extraction; iterative methods; medical signal processing; neurophysiology; pattern clustering; ART2 neural network; EEG-ERP domain; EEG-ERP signal decomposition; ERP P3 waveforms; Gabor dictionary; clustering; feature extraction; gabor atoms; matching pursuit algorithm; ANN; ART2; EEG; ERP; Gabor atoms; MP; P3 component; adaptive resonance theory neural network; clustering; electroencephalography; event-related potential; feature vector; matching pursuit algorithm; signal energy;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics (BMEI), 2012 5th International Conference on

Conference_Location

Chongqing

Print_ISBN

978-1-4673-1183-0

Type

conf

DOI

10.1109/BMEI.2012.6513026

Filename

6513026