Visually evoked potential (VEP) is a very small electrical signal originated in the visual cortex in response to periodic
visual stimulation. Sweep-VEP is a modified VEP procedure used to measure grating visual acuity in non-verbal and
preverbal patients. This biopotential is buried in a large amount of electroencephalographic (EEG) noise and movement
related artifact. The signal-to-noise ratio (SNR) plays a dominant role in determining both systematic and statistic errors.
The purpose of this study is to present a method based on wavelet transform technique for filtering and extracting steady-state
sweep-VEP. Counter-phase sine-wave luminance gratings modulated at 6 Hz were used as stimuli to determine
sweep-VEP grating acuity thresholds. The amplitude and phase of the second-harmonic (12 Hz) pattern reversal response
were analyzed using the fast Fourier transform after the wavelet filtering. The wavelet transform method was used to
decompose the VEP signal into wavelet coefficients by a discrete wavelet analysis to determine which coefficients yield
significant activity at the corresponding frequency. In a subsequent step only significant coefficients were considered and
the remaining was set to zero allowing a reconstruction of the VEP signal. This procedure resulted in filtering out other
frequencies that were considered noise. Numerical simulations and analyses of human VEP data showed that this method
has provided higher SNR when compared with the classical recursive least squares (RLS) method. An additional
advantage was a more appropriate phase analysis showing more realistic second-harmonic amplitude value during phase
In current clinical practice, the noninvasive assessment of left ventricular deformation can be determined using all the principal imaging modalities, including contrast angiography, echocardiography, cine computed tomography, single photon emission tomography and magnetic resonance imaging. However, since the heart undergoes complex motion, proper characterization of its motion still remains an open and challenging research problem. A number of approaches for nonrigid motion analysis have been studied in the literature. Much of the effort has confined to estimate the displacement vector for each image point or optical flow. This is a challenging problem in image analysis because of a wide range of possible motions and the presence of noise in the image sets. In this work, we present an algorithm for computation of optical flow based on a signal-dependent radially Gaussian kernel that adapts over time. The adaptive kernel obtained from the proposed algorithm is used to estimate a 3D-frequency spectrum for a given pixel in a series of images. The orientation of the spectrum in the frequency domain is totally governed by the pixel velocity. In a recent contribution, a linear regression model is used over the spectrum to obtain the velocity components that are proportional to the pixel movement.