ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)


анализ связанности

Selecting time scales for empirical model construction

The task is considered of taking into account the multiple time scales of original time series, with these time series being used for Granger causality estimation. It is proposed to use the combination of prediction length and lag, different in value, that could be fruitful for comparatively short times series, e. g. of medical-biological nature. The automated methods are constructed to select lag and prediction length values. The proposed approach is tested on a set of examples – ethalon systems.

Role of model nonlinearity for granger causality based coupling estimation for pathological tremor

Estimating coupling between systems of different nature is an urgent field of nonlinear dynamics method application. This work aims to compare classical linear Granger approach and its nonlinear analogues based on analysis of ethalon dynamical systems and neurophysiological data. The results achieved show nonlinear approach to be more sensitive, and so it is able to detect significant coupling, when linear one fails.

Diagnostics and correction of systematic error while estimating transfer entropy with k-nearest neighbours method

Transfer entropy is widely used to detect the directed coupling in oscillatory systems from their observed time series. The systematic error is detected, while estimating transfer entropy between nonlinear systems with K-nearest neighbours method. The way to minimize this error is suggested: the error is decreasing with increase of the neighbour number. The possibility to detect the systematic error is shown using two sets of measured data.

Optimization of granger causation method parameters for the study of limbic epilepsy

Purpose. The aim is to reveal the dependence of Granger causality results on chosen time scales of constructed empirical models in application to the task of investigation of evolution of coupling between brain areas during limbic seizures. Methods. We use combination of methods for coupling analysis of the experimental time series and approaches to modeling from the first principles, which reproduce the main time and frequency properties of the experimental signals.