Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği

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Publisher: search.filters.publisher.IEEESubject: search.filters.subject.Quasi-supervised learning

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  • Conference Object
    Citation - Scopus: 1
    Elektroensefalografi Verilerinin Yarı-güdümlü Öğrenme ile Otomatik Olarak İşaretlenmesi
    (IEEE, 2012) Köktürk, Başak Esin; Karaçalı, Bilge
    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.