The challenge of brain complexity - A brief discussion about a fractal intermittency-based approach

Paradisi P., Righi M., Barcaro U.

Threshold analysis  Electroencephalogram  Life and medical sciences  Complex Systems  Pattern recognition  Complex systems  Special-purpose and application-based systems  Pattern Recognition  Biomedical Signal Processing  Fractal Intermittency  Fractal intermittency  Brain events  Brain Events  Threshold Analysis  Operating systems performance  Biomedical signal processing 

In the last years, the complexity paradigm is gaining momentum in many research fields where large multidimensional datasets are made available by the advancements in instrumental technology. A complex system is a multi-component system with a large number of units characterized by cooperative behavior and, consequently, emergence of well-defined self-organized structures, such as communities in a complex network. The self-organizing behavior of the brain neural network is probably the most important prototype of complexity and is studied by means of physiological signals such as the ElectroEncephaloGram (EEG). Physiological signals are typically intermittent, i.e., display non-smooth rapid variations or crucial events (e.g., cusps or abrupt jumps) that occur randomly in time, or whose frequency changes randomly. In this work, we introduce a complexity-based approach to the analysis and modeling of physiological data that is focused on the characterization of intermittent events. Recent findings about self-similar or fractal intermittency in human EEG are reviewed. The definition of brain event is a crucial aspect of this approach that is discussed in the last part of the paper, where we also propose and discuss a first version of a general-purpose event detection algorithm for EEG signals.

Source: PhyCS 2016 - International Conference on Physiological Computing Systems 2016, pp. 123–129, Lisbona, Portugal, 27-28 June 2016

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