Add to ORKGAbstract: In this paper we consider the self-consistent stationary level of electroencephalogram time series. The practical purpose of this statistics is to construct the disorder indicator. Unlike the classical problem of stationary test of two samples, in our case one should construct an indicator to predict the change in the nonstationary regime. For example, we consider special predictor of an attack of epilepsy.Note: Research direction:Mathematical modelling in actual problems of science and technic
The multivariate time series techniques in this thesis were developed for the analysis of the electr...
ABSTRACT We describe and illustrate Bayesian approaches to modelling and analysis of multiple non-st...
In this paper, Bayesian online inference in models of data series are constructed by change-points a...
Abstract: In this paper we consider the stationary level of electroencephalogram time seri...
Abstract: Self-consistent stationary level of non-stationary time series is investigated. ...
While developing models of brain functioning by using time series data, the stationary interval of t...
A new approach to the analysis of nonstationary possibly nonlinear time series is presented. It is b...
This paper present a new approach to the analysis of non-stationary possibly nonlinear time series. ...
In the dissertation, we propose (i) a new method for analyzing a bivariate non-stationary time serie...
Along this paper, we shall update the state-of-the-art concerning the application of fractal-based t...
The correlation dimension has been used to characterize the effect of anesthesia on the EEG [13][14]...
The recently proposed instantaneous statistical dimension is compared to new conditional Renyi entro...
This thesis focuses on statistical methods for non-stationary signals. The methods considered or dev...
Several interesting applications in areas such as neuroscience, economics, finance and seismology ha...
This paper presents results of a non-linear study of the human electroencephalogram to establish the...
The multivariate time series techniques in this thesis were developed for the analysis of the electr...
ABSTRACT We describe and illustrate Bayesian approaches to modelling and analysis of multiple non-st...
In this paper, Bayesian online inference in models of data series are constructed by change-points a...
Abstract: In this paper we consider the stationary level of electroencephalogram time seri...
Abstract: Self-consistent stationary level of non-stationary time series is investigated. ...
While developing models of brain functioning by using time series data, the stationary interval of t...
A new approach to the analysis of nonstationary possibly nonlinear time series is presented. It is b...
This paper present a new approach to the analysis of non-stationary possibly nonlinear time series. ...
In the dissertation, we propose (i) a new method for analyzing a bivariate non-stationary time serie...
Along this paper, we shall update the state-of-the-art concerning the application of fractal-based t...
The correlation dimension has been used to characterize the effect of anesthesia on the EEG [13][14]...
The recently proposed instantaneous statistical dimension is compared to new conditional Renyi entro...
This thesis focuses on statistical methods for non-stationary signals. The methods considered or dev...
Several interesting applications in areas such as neuroscience, economics, finance and seismology ha...
This paper presents results of a non-linear study of the human electroencephalogram to establish the...
The multivariate time series techniques in this thesis were developed for the analysis of the electr...
ABSTRACT We describe and illustrate Bayesian approaches to modelling and analysis of multiple non-st...
In this paper, Bayesian online inference in models of data series are constructed by change-points a...