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Identification of phase transitions in simulated EEG signals

The KIV model is a biologically inspired hierarchical model that describes non-linear dynamics found in brains. Previous animal and human EEG measurements indicated the presence of jumps in the spatio-temporal EEG patterns, which are relevant to cognitive processing. The present work introduces the KIV model to simulate phase transitions in EEG signals. Phase transitions have non-stationary and intermittent characteristics, which make automated detection a very difficult task. We analyze the simulated EEG signals using various statistical methods. We describe various classification methods to identify simulated phase transitions, which will be used to automate the detection process in actual EEG signals.