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Denoising of ictal EEG data using semi-blind source separation methods based on time-frequency priors
, Article IEEE Journal of Biomedical and Health Informatics ; Volume 19, Issue 3 , July , 2015 , Pages 839-847 ; 21682194 (ISSN) ; Shamsollahi, M. B ; Albera, L ; Merlet, I ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2015
Abstract
Removing muscle activity from ictal ElectroEncephaloGram (EEG) data is an essential preprocessing step in diagnosis and study of epileptic disorders. Indeed, at the very beginning of seizures, ictal EEG has a low amplitude and its morphology in the time domain is quite similar to muscular activity. Contrary to the time domain, ictal signals have specific characteristics in the time-frequency domain. In this paper, we use the time-frequency signature of ictal discharges as a priori information on the sources of interest. To extract the time-frequency signature of ictal sources, we use the Canonical Correlation Analysis (CCA) method. Then, we propose two time-frequency based semi-blind source...