Automated labeling of electroencephalography data using quasi-supervised learning [Elektroensefalografi? veri?leri?ni?n yari- güdümlü ö?renme i?le otomati?k olarak i?şaretlenmesi?]
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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.