Sebastiano Stramaglia

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We consider an extension of Granger causality to nonlinear bivariate time series. In this frame, if the prediction error of the first time series is reduced by including measurements from the second time series, then the second time series is said to have a causal influence on the first one. Not all the nonlinear prediction schemes are suitable to evaluate(More)
The communication among neuronal populations, reflected by transient synchronous activity, is the mechanism underlying the information processing in the brain. Although it is widely assumed that the interactions among those populations (i.e. functional connectivity) are highly nonlinear, the amount of nonlinear information transmission and its functional(More)
Important information on the structure of complex systems can be obtained by measuring to what extent the individual components exchange information among each other. The linear Granger approach, to detect cause-effect relationships between time series, has emerged in recent years as a leading statistical technique to accomplish this task. Here we(More)
We propose a method of analysis of dynamical networks based on a recent measure of Granger causality between time series, based on kernel methods. The generalization of kernel-Granger causality to the multivariate case, here presented, shares the following features with the bivariate measures: (i) the nonlinearity of the regression model can be controlled(More)
L. Angelini, 2, 3 M. De Tommaso, 4 M. Guido, K. Hu, 6 P. Ch. Ivanov, 6 D. Marinazzo, G. Nardulli, 2, 3 L. Nitti, 7, 3 M. Pellicoro, 2, 3 C. Pierro, and S. Stramaglia 2, 3 TIRES: Center of Innovative Technologies for Signal Detection and Processing, University of Bari, Italy Physics Department, University of Bari, Italy Istituto Nazionale di Fisica Nucleare,(More)
A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely(More)
A new approach to clustering, based on the physical properties of inhomogeneous coupled chaotic maps, is presented. A chaotic map is assigned to each data point and short range couplings are introduced. The stationary regime of the system corresponds to a macroscopic attractor independent of the initial conditions. The mutual information between pairs of(More)
We consider kernel-based learning methods for regression and analyze what happens to the risk minimizer when new variables, statistically independent of input and target variables, are added to the set of input variables. This problem arises, for example, in the detection of causality relations between two time series. We find that the risk minimizer(More)
Migraine is a cyclic disorder, in which functional and morphological brain changes fluctuate over time, culminating periodically in an attack. In the migrainous brain, temporal processing of external stimuli and sequential recruitment of neuronal networks are often dysfunctional. These changes reflect complex CNS dysfunction patterns. Assessment of(More)
Previous studies have revealed that migraine patients display an increased photic driving to flash stimuli in the medium frequency range. The aim of this study was to perform a topographic analysis of steady-state visual evoked potentials (SVEPs) in the low frequency range (3-9 Hz), evaluating the temporal behaviour of the F1 amplitude by investigating(More)