Graph Theory tools for characterize Motor/Imaginary Movements in EEG

MAURO GRANADO; ROMAN BARAVALLE; OSVALDO A. ROSSO; NATALI GUISANDE; FERNANDO MONTANI

Buenos Aires

Conferencia; 27th International Conference on Statistical Physics, StatPhys 27; 2019

IUPAP

The EEG is a non-invasive technique recorded on the scalp that often has a poor relationship to the spiking activity of individual neurons. Measures of the relative contribution of EEG oscillations are particularly useful to investigate the emergent properties of the rhythmic activities of the brain. The EEG records the electrical activity of the brain; sensory stimulation or motor output shows different oscillations bands including theta (∈ [4, 8) Hz), alpha (∈ [8, 13) Hz), beta (∈ [13, 31) Hz), and gamma ranges (∈ [31, 50) Hz). The different rhythms of the brain activity are of functional importance to understand how information is processed in the mammalian brain. In previous works we investigate the hypothesis that neural processes associated with visuomotor integration or imaginary task are related to a higher amount of complexity in certain frequency bands. In order to do so, we used the Bandt-Pompe (BP) permutation methodology for the evaluation of the probability distribution function (PDF) associated with the EEG time series considering the different rhythmic oscillations bands. Based on the quantification of the ordinal ?structures? present in the EEG signals and their local influence on the associated probability distribution, we incorporate the time series? own temporal causality through an algorithm of easy implementation and computation.In our current study, we quantify the network connectivity strength across electrodes using an advanced symbolic formalism for assessing the probability distribution. We estimate the causal BP PDF associated to the different electrodes sites, and for the different frequency bands, to measure the interconnectivity across electrodes using the Jensen-Shannon Divergence between them. In this way obtain a representation of the brain complex network dynamics when executing different visuo-motor tasks. We evaluate the most relevant interconnectivity for frequency bands considering different pair of electrodes during different motor type activities, and when imagining that the subjects perform the activity. Interestingly the node centrality provides us a measure that could discriminate between imaginary and realized movements. Finally our results are contrasted to traditional network quantifiers emphasizing the advantages of our current approach that takes into account the causality of the signal.