Graph theory in the application of identifying the effect of color on visual connectivity based on EEG signal processing

The main objective of this work was to investigate the effect of color on brain dynamics during multimedia learning using effective connectivity analysis based on graph theory. EEG-based effective connectivity was computed using phase slope index (PSI) over ۱۷۱ combinations of ۱۹ electrodes of the EEG signals for six distinct frequency bands (delta, theta, alpha۱, alpha۲, beta, and gamma). Graph theory approach was applied to characterize patterns of effective connectivity from estimated PSI by determining total in-degree and out-degree flows at nodal regions of interest. The effective connectivity showed that increased interactions exist between anterior-posterior brain regions for higher frequency bands (alpha۱, alpha۲, beta, and gamma) with concurrent decrease interactions found in lower frequency bands (delta and theta) during learning content with black-and-white visualizations compared to colored visualizations. Further, graph theory analysis using a degree of connectivity demonstrated that significant higher out-degree information flows from right parietal to bilateral frontal areas in the delta; from left frontal to midline parietal and right posterior regions in the alpha۱, alpha۲ and beta during learning with colored visualizations. While significant higher out-degree information flows from (midline and right) parietal to frontal regions observed in alpha۱, alpha۲, and beta when learning content with black and-white visualizations. To conclude, the results indicate that content's color effect on brain's interaction and visual working memory potentially improves learning with top-down processing influences on selective attention and visual information for memory encoding.