How do brain areas communicate during the processing of noxious stimuli? An analysis of laser-evoked event-related potentials using the Granger causality index.

Several imaging techniques have identified different brain areas involved in the processing of noxious stimulation and thus in the constitution of pain. However, only little is known how these brain areas communicate with one another after activation by stimulus processing and which areas directionally affect or modulate the activity of succeeding areas. One measure for the analysis of such interactions is represented by the Granger Causality Index (GCI).

Cortical network dynamics during foot movements.

The present work intends to evaluate the dynamics of the cerebral networks during the preparation and the execution of the foot movement. In order to achieve this objective, we have used mathematical tools capable of estimating the cortical activity via high-resolution EEG techniques. Afterwards we estimated, the instantaneous relationships occurring among the time-series of sixteen regions of interest (ROIs) in the Alpha (7-12 Hz) and Beta (13-29 Hz) band through the adaptive multivariate autoregressive models.

Coupled oscillators for modeling and analysis of EEG/MEG oscillations.

This study presents three EEG/MEG applications in which the modeling of oscillatory signal components offers complementary analysis and an improved explanation of the underlying generator structures. Coupled oscillator networks were used for modeling. Parameters of the corresponding ordinary coupled differential equation (ODE) system are identified using EEG/MEG data and the resulting solution yields the modeled signals.

Detection of directed information flow in biosignals.

Several analysis techniques have been developed for time series to detect interactions in multidimensional dynamic systems. When analyzing biosignals generated by unknown dynamic systems, awareness of the different concepts upon which these analysis techniques are based, as well as the particular aspects the methods focus on, is a basic requirement for drawing reliable conclusions. For this purpose, we compare four different techniques for linear time series analysis.

Development of interaction measures based on adaptive non-linear time series analysis of biomedical signals.

An important feature of interaction between two signal components is the direction of the interaction. Recently, different methods have been developed and applied for detecting the direction of interactions. Besides frequency-dependent methods, Granger causality is a well-known frequency-independent approach.

The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies.

Understanding of brain functioning requires the investigation of activated cortical networks, in particular the detection of interactions between different cortical sites. Commonly, coherence and correlation are used to describe interrelations between EEG signals. However, on this basis, no statements on causality or the direction of their interrelations are possible.