Elektroensefalografi Verilerinin Yarı-güdümlü Öğrenme ile Otomatik Olarak İşaretlenmesi - Köktürk, Başak Esin; Karaçalı, Bilge; Karaçalı, Bilge (IEEE, 2012)
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.
Automated Labeling of Electroencephalography Data Using Quasi-Supervised Learning
Keywords: Electroencephalogram; Independent component analysis; Quasi-supervised learning; Wavelet transform