A Formal SPN Methodology for Single (1D) and Multiple Channels (2D) EEG Brain Activity Representations/Analysis/Diagnosis

In this paper, a syntactic approach for the Electroencephalography (EEG) signal modeling and analysis is presented. EEG is a noninvasive neuroimaging method which directly measures the brain activity in a form of time series of electrical potential oscillations. The assessment of brain dynamics and activity patterns is of crucial importance for the understanding of brain functionality. Over the years, the progress in data mining and processing techniques have contributed to the exploration of the time and frequency domain characteristics embodied in the EEG recordings. Here, a synergistic methodology that enables the analysis of the structural features of EEG and moreover, aims to combine the EEG channels' spatiotemporal dependencies utilizing the Stochastic Petri Nets model, is presented.