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Brain-Computer Interface (BCI) systems are widely based on steady-state visual evoked potentials (SSVEP) detection using electroencephalography (EEG) signals. SSVEP-based BCIs are becoming attractive due to their higher signal-to-noise ratio (SNR) as well as faster information transfer rate (ITR). However, their performances are largely affected by the interference coming from the spontaneous EEG...
The Brain-Computer Interface is a system mainly designed to provide people suffering from severe neuromuscular disorder with a new mean of communication and control. Increasing the system accuracy rate is the goal of several studies. In fact, ameliorating this criterion allows to minimize the correction phase and makes the use of the system more natural. This is very important to develop Brain-Computer...
Recently, the low cost EEG acquisition systems such as the Emotiv Epoc give new tools to develop Brain-Computer interface systems for everyday use outside the laboratory. However, the low sampling rate and the low number of channels remain possible sources of failure. The Canonical Correlation Analysis and the Multivariate Synchronization Index (MSI) methods are applied in a SSVEP-based BCI in order...
Thresholding is one of the popular and fundamental techniques for conducting image segmentation. It is a widely used tool in image segmentation for extracting the object regions from their background. In this paper, image segmentation method based on two-dimensional histogram analysis through entropy maximization is presented. The 2-D maximum entropy threshold approach is proposed to segment a gray-scale...
The data interpolation is an essential part of Bidimensional Empirical Mode Decomposition (BEMD) of an image. In the decomposition process, local maxima and minima of the image are extracted at each iteration and then interpolated to form the upper and lower envelopes, respectively. Because of the properties of radial basis function (RBF) interpolators, they are good candidates for use in BEMD. However,...
This paper presents a new adaptive approach for image denoising with Gaussian noise based on a combination of the Bidimensional Empirical Mode Decomposition (BEMD) and the the discrete wavelet transforms (DWT). The BEMD is an auto-adaptive method for the analysis of nonlinear or non-stationary signals and images. The input image is decomposed into several modes called Intrinsic Mode Functions (IMFs),...
In this paper we propose a new approach for automated diagnosis and classification of Magnetic Resonance (MR) human brain images, using Wavelets Transform (WT) as input to Genetic Algorithm (GA) and Support Vector Machine (SVM). The proposed method segregates MR brain images into normal and abnormal. Our contribution employs genetic algorithm for feature selection witch requires much lighter computational...
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