Cross-sample entropy for the study of coordinated brain activity in calm and distress conditions with electroencephalographic recordings

Traditionally, the brain has been studied as an ensemble of independent structures with determined functions. However, it has been demonstrated that the brain operates as a network in which all regions are interconnected. Apart from physical links, the brain presents functional associations between non-physically connected regions that work synchronized in a common mental process. For this reason, the study of functional connectivity is essential to reveal new insights about brain’s behavior. In this work, a nonlinear functional connectivity metric called cross-sample entropy is applied for the first time to emotions recognition. Concretely, it has been computed for the detection of distress because of being one of the most influencing emotions in developed societies with several negative implications for health. Results reveal a strong coordinated activity between channels in central, parietal and occipital areas in each brain hemisphere separately, and also in the inter-hemispheric interactions among the same regions. Moreover, an augmented amount of similar dynamics under distress conditions in all brain regions with respect to a calm state reveals an increase in self-coordination of brain activity in distressful situations.