Complexity measures based on intermittent events in brain EEG data

In this work we discuss the application of the complexity approach to the study of physiological signals. In particular, a theoretical framework based on the ubiquitous emergence of fractal intermittency in complex signals is introduced. This approach is based on the ability of complex systems' cooperative micro-dynamics of triggering metastable, macroscopic, self-organized states. The metastability is strictly connected with the emergence of a intermittent point process displaying anomalous non-Poisson statistics and driving the fast transition events between successive metastable states. As a consequence, the estimation of features related to intermittent events can be used to characterize the ability of the complex system to trigger self-organized structures. We introduce an algorithm for the processing of complex signals that is based on the fractal intermittency paradigm, thus focusing on the detection and scaling analysis of intermittent events in human ElectroEncephaloGram (EEG). We finally discuss the application of this approach to real EEG recordings and introduce the preliminary findings.