Robert Krupiński

Towards to real-time system with optimization based approach for EOG and blinking signals separation for human computer interaction

Electrooculography (EOG) systems could be used for Human Computer Interaction but measurements with minimal number of electrodes and appropriate signals processing algorithms for the separation of EOG and blinking signals are required. Median filter with a carefully selected mask size could be used for the initialization of the evolution-based algorithm, which is used for the estimation and separation of EOG and blinking signals.

Modified Moment Method Estimator for the Shape Parameter of Generalized Gaussian Distribution for a Small Sample Size

The moment method (MM) estimator for the shape parameter of generalized Gaussian distribution (GGD) assume asymptotic case when there is available infinite number of observations, but with the smaller sample size the variance of the estimator increases and the moment method equation may not converge to a real solution for some sample sets. The higher order moments can be expanded into series in the moment method equation leading to a drop in the relative mean square error (RMSE) and assuring a solution for a smaller sample size comparing to the moment method without modification.

Electrooculography signal estimation by using evolution-based technique for computer animation applications

The measurement of electrooculography biosignals (EOG) gives the possibility of eye orientation and eyelid movements (blinking) estimation what is important for realistic eyes motion-capture. In this paper the linear smooth pursuit movements of eyes are considered. The optimization techniques for the estimation of eye signal and the separation of EOG and blinking signals are applied. The reconstruction of both signal is possible by the application of a signal model with a coefficient set, which are the subject of optimization.

Convergence Improving in Evolution–Based Technique for Estimation and Separation of Electrooculography and Blinking Signals

Estimation of electrooculography (EOG) and blinking signals could be done by using numerous algorithms. The separation of EOG and blinking signals is hard if the blinking pulses occur in the neighborhood of saccades. The appropriate estimation and signals separation could be obtained by using the optimization algorithm and a proper signals model. The proposed method improves the convergence of evolution–based technique and reduces the optimization time by using the additional algorithm for the estimation of possible positions of blinking pulses, the saccades positions and the EOG levels. A median filter based estimator is used for the initialization of optimization algorithm of blinking pulses. A differential filter based detector is used for the initialization of saccades position and the EOG signal level values between them. The proposed method reduces the computation time a few times what is important for the possibility of real–time implementation.

Reconstructed quantized coefficients modeled with generalized Gaussian distribution with exponent 1/3

Abstract Generalized Gaussian distribution (GGD) includes specials cases when the shape parameter equals p = 1 and p = 2. It corresponds to Laplacian and Gaussian distributions respectively. For p → ∞, f(x) becomes a uniform distribution, and for p → 0, f(x) approaches an impulse function. Chapeau-Blondeau et al. [4] considered another special case p = 0.5. The article discusses more peaky case in which GGD p = 1/3.

Estimation of electrooculography and blinking signals based on filter banks

Estimation of electrooculography (EOG) and blinking signals are important for medical applications and non---medical, like computer animation. Both signals are measured together using set of electrodes and the separation of them is necessary. Filter banks based technique for the estimation of EOG and blinking signals is considered. The gradient search allows estimation of the height and width of blinking pulses and the slopes between saccades. Improved detection related to the time domain of saccades is proposed too.

Modeling Quantized Coefficients with Generalized Gaussian Distribution with Exponent 1 / m, \(m=2,3,\ldots \)

The different types of signals in the image and signal processing applications can be modeled with generalized Gaussian distribution (GGD). When limiting to the special cases, then the closed form equations can be determined. The special cases with the exponents \(p=2\) (Gaussian distribution), \(p=1\) (Laplacian distribution), \(p=1/2\) and \(p=1/3\) are considered in literature. In the article, more general approach for the exponents 1 / m, \(m=2,3,\ldots \) is analyzed, which are related to the peaky shapes of GGD. The maximum likelihood method for a discrete random variable is derived for this subclass of distributions.