Authors:
Paolo Paradisi
1
;
Marco Righi
2
and
Umberto Barcaro
3
Affiliations:
1
Istituto di Scienza e Tecnologie dell'Informazione ``A. Faedo'' (ISTI-CNR) and Basque Center of Applied Mathematics (BCAM), Italy
;
2
Istituto di Scienza e Tecnologie dell'Informatica ``A. Faedo'' (ISTI-CNR), Italy
;
3
Università di Pisa, Italy
Keyword(s):
Biomedical Signal Processing, Electroencephalogram, Brain Events, Fractal Intermittency, Threshold Analysis, Pattern Recognition, Complex Systems.
Related
Ontology
Subjects/Areas/Topics:
Biosignal Acquisition, Analysis and Processing
;
Human-Computer Interaction
;
Methodologies and Methods
;
Physiological Computing Systems
Abstract:
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. Rec
ent 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.
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