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 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)
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)
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)
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)
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)
Orthogonal Matching Pursuit (OMP) is the most popular greedy algorithm that has been developed to find a sparse solution vector to an under-determined linear system of equations. OMP follows the projection procedure to identify the indices of the support of the sparse solution vector. This paper shows that the least-squares (LS) procedure can perform better(More)