Cluster analysis for EEG biosignal discrimination

Author

Georgieva, Olga ; Milanov, Sergey ; Georgieva, Petia

Author_Institution

Fac. of Math. & Inf., Sofia Univ. “St. Kl. Ohridski”, Sofia, Bulgaria

fYear

2013

fDate

19-21 June 2013

Firstpage

1

Lastpage

5

Abstract

The paper aims to define the ability of unsupervised learning approach to identify emotional biosignals evoked while viewing affected pictures. Two problems are consequently resolved. First, the most important features of the Electroencephalography (EEG) data set have been selected. Secondly, cluster analysis technique is applied in order to extract the specific knowledge of the existing dependencies. The clustering results of particular data subsets are presented and discussed.

Keywords

data mining; electroencephalography; medical signal processing; pattern clustering; unsupervised learning; EEG biosignal discrimination; biosignal retrieval; cluster analysis technique; data mining; electroencephalography data set; emotional biosignal identification; knowledge extraction; unsupervised learning approach; Algorithm design and analysis; Clustering algorithms; Data mining; Educational institutions; Electroencephalography; Unsupervised learning; Vectors; EEG signals; biosignal retrieval; cluster analysis; data mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on

Conference_Location

Albena

Print_ISBN

978-1-4799-0659-8

Type

conf

DOI

10.1109/INISTA.2013.6577646

Filename

6577646