Signal Subspace Identification for Epileptic Source Localization from EEG Data

, Ph.D. Dissertation Sharif University of Technology Hajipour Sardouie, Sepideh (Author) ; Shamsollahi, Mohammad Bagher (Supervisor) ; Albera, Laurent (Co-Advisor) ; Merlet, Isabelle (Co-Advisor)

Abstract

In the process of recording electrical activity of the brain, the signal of interest is usually contaminated with different activities arising from various sources of noise and artifact such as muscle activity. This renders denoising as an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications such as source localization. In this thesis, we propose six methods for noise cancelation of epileptic signals. The first two methods, which are based on Generalized EigenValue Decomposition (GEVD) and Denoising Source Separation (DSS) frameworks, are used to denoise interictal data. To extract a priori information required by GEVD and DSS, we propose a series of preprocessing...

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) Hajipour, S ; 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...

Canonical polyadic decomposition of complex-valued multi-way arrays based on simultaneous schur decomposition

, Article ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings ; 2013 , Pages 4178-4182 ; 15206149 (ISSN) ; 9781479903566 (ISBN) Sardouie, S. H ; Albera, L ; Shamsollahi, M. B ; Merlet, I ; Sharif University of Technology

2013

Abstract

In this paper, we propose a new semi-algebraic algorithm to compute the Canonical Polyadic (CP) decomposition of complex-valued multi-way arrays. The proposed algorithm is based on the Simultaneous Schur Decomposition (SSD) of particular matrices derived from the array to process. This CP algorithm solves some convergence problems of classical iterative techniques and its identifiability assumptions are less restrictive than those of other semi-algebraic methods. We also propose a new Jacobi-like algorithm to calculate the SSD of several complex-valued matrices. Finally the usefulness of the proposed method is illustrated in the context of fluorescence spectroscopy and epileptic source...

An efficient Jacobi-like Deflationary ICA algorithm: Application to EEG denoising

, Article IEEE Signal Processing Letters ; Volume 22, Issue 8 , December , 2015 , Pages 1198-1202 ; 10709908 (ISSN) Sardouie, S. H ; Albera, L ; Shamsollahi, M. B ; Merlet, I ; Sharif University of Technology

Institute of Electrical and Electronics Engineers Inc 2015

Abstract

In this paper, we propose a Jacobi-like Deflationary ICA algorithm, named JDICA. More particularly, while a projection-based deflation scheme inspired by Delfosse and Loubaton's ICA technique (DelLR) is used, a Jacobi-like optimization strategy is proposed in order to maximize a fourth order cumulant-based contrast built from whitened observations. Experimental results obtained from simulated epileptic EEG data mixed with a real muscular activity and from the comparison in terms of performance and numerical complexity with the FastICA, RobustICA and DelLR algorithms, show that the proposed algorithm offers the best trade-off between performance and numerical complexity when a low number (∼...

Interictal EEG noise cancellation: GEVD and DSS based approaches versus ICA and DCCA based methods

, Article IRBM ; Volume 36, Issue 1 , 2015 , Pages 20-32 ; 19590318 (ISSN) Hajipour Sardouie, S ; Shamsollahi, M. B ; Albera, L ; Merlet, I ; Sharif University of Technology

Elsevier Masson SAS 2015

Abstract

Denoising is an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications. For this purpose, Blind Source Separation (BSS) methods, such as Independent Component Analysis (ICA) and Decorrelated and Colored Component Analysis (DCCA), are commonly used. Although ICA and DCCA-based methods are powerful tools to extract sources of interest, the procedure of eliminating the effect of sources of non-interest is usually manual. It should be noted that some methods for automatic selection of artifact sources after BSS methods exist, although they imply a training supervised step. On the other hand, in cases where there are some a prioriinformation about the subspace of...

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) Hajipour Sardouie, S ; 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...