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Title: 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: Institute of Electrical and Electronics Engineers Inc.
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: 9.78E+12
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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