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generalized-eigenvalue-decomposition--gevd
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Noise cancelation of epileptic interictal EEG data based on generalized eigenvalue decomposition
, Article 2012 35th International Conference on Telecommunications and Signal Processing, TSP 2012 - Proceedings ; 2012 , Pages 591-595 ; 9781467311182 (ISBN) ; Shamsollahi, M. B ; Albera, L ; Merlet, I ; Sharif University of Technology
2012
Abstract
Denoising is an important preprocessing stage in some Electroencephalography (EEG) applications such as epileptic source localization. In this paper, we propose a new algorithm for denoising the interictal EEG data. The proposed algorithm is based on Generalized Eigenvalue Decomposition of two covariance matrices of the observations. Since one of these matrices is related to the spike durations, we should estimate the occurrence time of the spike peaks and the exact spike durations. To this end, we propose a spike detection algorithm which is based on the available spike detection methods. The comparison of the results of the proposed algorithm with the results of two well-known ICA...
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...
Ictal EEG signal denoising by combination of a semi-blind source separation method and multiscale PCA
, Article 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering, ICBME 2016, 23 November 2016 through 25 November 2016 ; 2017 , Pages 226-231 ; 9781509034529 (ISBN) ; Hajipour Sardoubie, S ; Shamsollahi, M. B ; Sharif University of Technology
Abstract
Contamination of ictal Electroencephalogram (EEG) signals by muscle artifacts is one of the critical issues related to clinically diagnosing seizure. Over the past decade, several methods have been proposed in time, frequency and time-frequency domain to accurately isolate ictal EEG activities from artifacts. Among denoising approaches Canonical Correlation Analysis (CCA) and Independent Component Analysis (ICA) are widely used. Denoising based on Generalized EigenValue Decomposition (GEVD) is one of the Semi-Blind Source Separation (SBSS) methods which has been recently proposed. In the GEVD-based method, a couple of time-frequency covariance matrices are used. These time-frequency (TF)...