Signal Subspace Identification for Epileptic Source Localization from EEG Data, Ph.D. Dissertation Sharif University of Technology ; 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...
Cataloging briefSignal Subspace Identification for Epileptic Source Localization from EEG Data, Ph.D. Dissertation Sharif University of Technology ; 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...
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