Reda R. Gharieb

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A novel approach is proposed in order to reduce the number of sweeps (trials) required for the efficient extraction of the brain evoked potentials (EPs). This approach is developed by combining both the Wiener filtering and the subspace methods. First, the signal subspace is estimated by applying the singular-value decomposition (SVD) to an enhanced version(More)
This paper presents a novel approach to blind source separation of narrowband signals in additive either white (i.e., time-uncorrelated) noise or colored (i.e., time-correlated) noise of which the frequencies of its spectral peaks are either known or can be estimated. The main idea behind this approach is that a signal vector can be decomposed into a set of(More)
An adaptive filtering approach for the segmentation and tracking of electro-encephalogram (EEG) signal waves is described. In this approach, an adaptive recursive bandpass filter is employed for estimating and tracking the centre frequency associated with each EEG wave. The main advantage inherent in the approach is that the employed adaptive filter has(More)
In this paper, we use third-order correlations (TOC) in developing a filtering technique for the recovery of brain evoked potentials (EPs). The main idea behind the presented technique is to pass the noisy signal through a finite impulse response filter whose impulse response is matched with the shape of the noise-free signal. It is shown that it is(More)
This paper presents a novel adaptive filtering approach for the classification and tracking of the electroencephalogram (EEG) waves. In this approach, an adaptive recursive bandpass filter is employed for estimating and tracking the center frequency associated with each EEG wave. The main advantage inherent in the approach is that the employed adaptive(More)
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