Nasser Mourad

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OBJECTIVE EEG recording is useful for neurological and cognitive assessment, but acquiring reliable data in infants and special populations has the challenges of limited recording time, high-amplitude background activity, and movement-related artifacts. This study objectively evaluated our previously proposed ERP analysis techniques. METHODS We compared(More)
In this paper, we develop a novel methodology for minimizing a class of nonconvex (concave on the non-negative orthant) functions for solving an underdetermined linear system of equations As = <i>x</i> when the solution vector <i>s</i> is known <i>a priori</i> to be sparse. The proposed technique is based on locally replacing the original objective function(More)
In this paper we propose a new technique for automatic correction of eye blink artifact in single channel EEG recording. The proposed technique consists of three steps. In the first two steps a dictionary matrix and a reference signal to the eye blink artifact are constructed from the recorded data, respectively. In the proposed technique we suggest(More)
In this paper we present a simple and fast technique for correcting high amplitude artifacts that contaminate EEG signals. Examples of such artifacts are ocular movement, eye blinks, head movement, etc. Since the measured EEG data can be modeled as a linear combination of brain sources and artifacts, the proposed technique is based on multiplying the(More)
Human infants rapidly develop their auditory perceptual abilities and acquire culture-specific knowledge in speech and music in the second 6 months of life. In the adult brain, neural rhythm around 10 Hz in the temporal lobes is thought to reflect sound analysis and subsequent cognitive processes such as memory and attention. To study when and how such(More)
The under-determined blind source separation (BSS) problem is usually solved using the sparse component analysis (SCA) technique. In SCA, the BSS is usually solved in two steps, where the mixing matrix is estimated in the first step, while the sources are estimated in the second step. In this paper we propose a novel clustering algorithm for estimating the(More)
In this paper we present a new technique for minimizing a class of nonconvex functions for solving the problem of under–determined systems of linear equations. The proposed technique is based on locally replacing the nonconvex objective function by a convex objective function. The main property of the utilized convex function is that it is minimized at a(More)
In this paper we propose an iterative algorithm for solving the problem of extracting a sparse source signal when a reference signal for the desired source signal is available. In the proposed algorithm, a nonconvex objective function is used for measuring the diversity (antisparsity) of the desired source signal. The nonconvex function is locally replaced(More)
In this paper we propose a new algorithm for solving the blind source extraction (BSE) problem when the desired source signals are sparse. Previous approaches for solving this problem are based on the independent component analysis (ICA ) technique, that extracts a source signal by finding a separating vector that maximizes the non-Gaussianity of the(More)
Recently, a new class of algorithms has been developed which iteratively build a sparse solution to an underdetermined linear system of equations. These algorithms are known in the literature as Iterative Shrinkage Algorithms (ISA). ISA algorithms depend on a thresholding parameter, which is usually provided by the user. In this paper we develop a new(More)