introduction: evoked potentials arisen by stimulating the brain can be utilized as a communication tool  between humans and machines. most brain-computer interface (bci) systems use the p300 component,  which is an evoked potential. in this paper, we evaluate the use of the hidden markov model (hmm) for  detection of p300.  materials and methods: the wavelet transforms, wavelet-enhanced indepen...

Introduction: Evoked potentials arisen by stimulating the brain can be utilized as a communication tool  between humans and machines. Most brain-computer interface (BCI) systems use the P300 component,  which is an evoked potential. In this paper, we evaluate the use of the hidden Markov model (HMM) for  detection of P300.  Materials and Methods: The wavelet transforms, wavelet-enhanced indepen...

In view of the existing seismic signal analysis model there are some problems is Analysis of the result is bad, the accuracy is not high. This paper puts forward an algorithm based on discrete wavelet and generalized the ICA model of seismic signal analysis. First for continuous Wavelet transform exists redundant of problem, on standard small wave transform algorithm of transform domain in the ...

We present a novel integrated wavelet-domain based framework (w-ICA) for 3-D denoising functional magnetic resonance imaging (fMRI) data followed by source separation analysis using independent component analysis (ICA) in the wavelet domain. We propose the idea of a 3-D wavelet-based multi-directional denoising scheme where each volume in a 4-D fMRI data set is sub-sampled using the axial, sagi...

The ball detection in soccer images is one of the applications of the most general problem of object recognition, where the approach mainly used is based on classifying the pattern images after a suitable preprocessing. In this paper we have compared two different pre-processing techniques: the initial vectorial representation of the image has been projected both on the Haar basis and on the ba...

To analyze EEG accurately, it is necessary to remove artifacts from EEG, which gets coupled with signal at the time of recording and can’t be eliminated at preprocessing stage. Ocular artifact is most obvious artifact in EEG. In this paper, a new method using Stationary Wavelet Enhanced Independent Component Analysis with a novel thresholding, is proposed for ocular artifact removal from EEG. P...

Independent component analysis (ICA) is a new effective technique for separation of statistically independent sources existing simultaneously in observations. Generally, ICA requires that the number of sensors should be no less than the number of independent sources to ensure enough information for separation of all sources. In some practical applications, this requirement of ICA is not met and...

The goal of this paper is to describe a robust artifact removal (RAR) method, an automatic sequential procedure which is capable of removing short-duration, high-amplitude artifacts from long-term neonatal EEG recordings. Such artifacts are mainly caused by movement activity, and have an adverse effect on the automatic processing of long-term sleep recordings. The artifacts are removed sequenti...

In recent years, access to multimedia data has become much easier due to rapid growth of the internet. While this is usually considered an improvement of everyday life, it also makes unauthorized copying and distributing of multimedia data much easier, therefore presenting a field of watermarking. Many literatures have reported about Discrete Wavelet Transform watermarking techniques for data s...

Image fusion is commonly described as the task of enhancing the perception of a scene by combining information captured by different modality sensors. The pyramid decomposition and the Dual-Tree Wavelet Transform have been employed as analysis and synthesis tools for image fusion by the fusion community. Using various fusion rules, one can combine the important features of the input images in t...