Elektroensefalografi Verilerinin Yarı-güdümlü Öğrenme ile Otomatik Olarak İşaretlenmesi

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.