Title :
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
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;
Conference_Titel :
Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on
Conference_Location :
Albena
Print_ISBN :
978-1-4799-0659-8
DOI :
10.1109/INISTA.2013.6577646
