Core goals are the development of new techniques and algorithms for the investigation of signals produced by nonlinear dynamical systems. Examples of issues tackled in the lab are the assessment of optimal embedding parameters for sequences generated by chaotic systems, new ways for estimating invariant quantities such as Correlation Dimension and Maximum Lyapunov Exponent, the disentanglement of the noisy components from the deterministic one contained in a signal. These goals are pursued both through mathematical and statistical tools and by carrying out intensive numerical computations applied to experimentally-recorded and synthetic signals.
Another main research line of the laboratory is the study of multivariate, experimentally-recorded signals produced by complex systems. For example, the investigation of signals produced by the human brain makes up an issue that requires powerful nonlinear techniques to unveil brain function, to assess the existence and characterize network structures. Within this context, we are keen to investigate the role of noise in the human brain. In our lab, this kind of challenging studies is tackled by means of advanced statistical methods applied to multivariate time series sampled via magnetoencephalography (MEG) and electroencephalography (EEG). Part of the research is carried out within the Center for Mind/Brain Sciences (CIMeC) of the University of Trento. Possible additional fields of application of these techniques are climate research and economics.
|PhD students||Michele Castelluzzo, Alessio Perinelli|