Characterizing brain connectivity during driving risk situations by transfer entropy measures of EEG

Road traffic-related injuries constitute a major problem in public health. Previous research used neuroimaging techniques to gain insights into dynamic complex systems during driving risk situations. Even so, coupling between risk behavior and changes in neural activity is still a pending matter. Information flow brings valuable evidence regarding how one brain region influences another. To have a complete understanding of neural mechanisms during driving risk situations, this study explores the potential of measuring the pattern of information transfer between brain structures in EEG for research in brain connectivity of 50 healthy participants aged between 18 and 24 years during exceeding the maximum permissible speed, having a car accident or facing risk scenes using the Honda Riding Trainer motorcycle simulator.Transfer entropy measures the directed flow of information between brain regions, revealing how one region influences another. Findings show differential brain connectivity for conditions related to risk perception and risk behavior, respectively. In addition, similar results between unintentional and urgent behaviors are found. In general, results suggest that default mode, limbic, frontoparietal and dorsal attention networks are the most related to driving risk situations. Advances in this research area would contribute to the identification of aberrant drivers and interventional strategies for reducing road fatalities.