Research activities

Core goals of the research carried out within the NSE Lab are the development of new methods 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, and the disentanglement of the noisy component from the deterministic one in a time series. These goals are pursued both through mathematical and statistical tools and by carrying out intensive numerical computations applied to experimentally-recorded and synthetic signals.
An increasing part of the research is devoted to addressing physical problems with information-theoretical – i.e. entropy-based – methods, which provide a complementary approach to more conventional and established analytical techniques.
Another main research line of the laboratory is the study of multivariate, experimentally-recorded time series 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 functions, assess the existence of network structures, and characterize these functions and networks. Within this context, we are keen to investigate the role of noise within the human brain. To this goal, advanced statistical methods are 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.
Finally, the development of advanced analog and digital electronics is possibly the main technical expertise present in the lab.

Group members