The brain may be considered as a synchronized dynamic network with several coherent dynamical units. However, concerns remain whether synchronizability is a stable state in the brain networks. If so, which index can best reveal the synchronizability in brain networks? To answer these questions, we tested the application of the spectral graph theory and the Shannon entropy as alternative approaches in neuroimaging. We specifically tested the alpha rhythm in the resting-state eye closed (rsEC) and the resting-state eye open (rsEO) conditions, a well-studied classical example of synchrony in neuroimaging EEG. Since the synchronizability of alpha rhythm is more stable during the rsEC than the rsEO, we hypothesized that our suggested spectral graph theory indices (as reliable measures to interpret the synchronizability of brain signals) should exhibit higher values in the rsEC than the rsEO condition. We performed two separate analyses of two different datasets (as elementary and confirmatory studies). Based on the results of both studies and in agreement with our hypothesis, the spectral graph indices revealed higher stability of synchronizability in the rsEC condition. The k-mean analysis indicated that the spectral graph indices can distinguish the rsEC and rsEO conditions by considering the synchronizability of brain networks. We also computed correlations among the spectral indices, the Shannon entropy, and the topological indices of brain networks, as well as random networks. Correlation analysis indicated that although the spectral and the topological properties of random networks are completely independent, these features are significantly correlated with each other in brain networks. Furthermore, we found that complexity in the investigated brain networks is inversely related to the stability of synchronizability. In conclusion, we revealed that the spectral graph theory approach can be reliably applied to study the stability of synchronizability of state-related brain networks.

The Head Mounted Projection Display (HMPD) is a growing interest area in HCI. Although various aspects of HMPDs have been investigated, there is not enough information regarding the effect of HMPDs (i.e., head referenced static and dynamic displays while a user is in motion and standing) on visual attentional performance. For this purpose, we conducted a user study (N=18) with three experimental conditions (control, standing, walking) and two visual perceptual tasks (with dynamic and static displays). Significant differences between conditions were only found for the task with dynamic display; accuracy was lower in walking condition compared to the other two conditions. Our work contributes an empirical investigation of the effect of HMPDs on visual attentional performance by providing data-driven benchmarks for developing graphical user interface design guidelines for HMPDs.