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Title: Elektroensefalografi verilerinin yarı-güdümlü öğrenme ile otomatik olarak işaretlenmesi
Other Titles: Automated labeling of electroencephalography data using quasi-supervised learning
Authors: Köktürk, Başak Esin
Karaçalı, Bilge
Keywords: Electroencephalogram
Independent component analysis
Quasi-supervised learning
Wavelet transform
Issue Date: 2012
Publisher: IEEE
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. © 2012 IEEE.
ISBN: 978-146730056-8
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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