DocumentCode :
3646554
Title :
Automated labeling of electroencephalography data using quasi-supervised learning
Author :
Başak Esin Köktürk;Bilge Karaçalı
Author_Institution :
Elektrik ve Elektronik Mü
fYear :
2012
fDate :
4/1/2012 12:00:00 AM
Firstpage :
1
Lastpage :
4
Abstract :
In this study, the separation of the stimulus effects from the baseline was investigated in electroencephalography data recorded under different visual stimuli using quasi-supervised learning. The data feature vectors were constructed using independent component analysis and wavelet transform, and then, these feature vectors were separated using quasi-supervised learning. Experiment results showed that the EEG data of the stimuli can be separated using quasi-supervised learning.
Keywords :
"Electroencephalography","Wavelet analysis","Wavelet transforms","Vectors","Labeling","Abstracts"
Publisher :
ieee
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2012 20th
Print_ISBN :
978-1-4673-0055-1
Type :
conf
DOI :
10.1109/SIU.2012.6204600
Filename :
6204600
Link To Document :
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