Аннотация:Background: Emotional intelligence (EI) is the ability to accurately perceive, understand, reason about emotional information, regulate emotions effectively and apply this knowledge to improve cognitive and behavioural performance. EI is related to important aspects of social and interpersonal communication, mental health, and academic achievement. Although some study is investigating thebrain mechanisms underlying EI (Barbey et al., 2014; Qiao et al., 2021) there are insufficient data on the relationship between spontaneous resting-state brain activity and EI. The goal of this preliminary study is to investigate the relationship between brain functional connectivity at rest and EI.Methods: The sample included 44 people (20 female), aged 18 to 25 years. EI was measured with the Russian adaptation of MSCEIT (Sergienko & Vetrova, 2017). EEG data were collected at rest withthe 64 active electrodes. Sources reconstruction, graph metrics, and synchronization measures were done using the MNE Python. The statistical processing was completed in the R language for statisticalcomputing. The following large-scale topological network metrics were computed: the characteristic path length, the averaged clustering coefficient and the modularity. The analysis was carried outseparately for the theta (4-8 Hz), alpha (full 8–13 Hz, low 8–10 Hz,high 10-12 Hz), beta 1 (13-20 Hz) and beta 2 (20-30 Hz) frequency bands.Results: Statistically significant correlations with EI were found forthe following graph metrics:1. characteristic path length:subscale 'Perceiving emotions' in the alpha range (r=0.31,p=0.04),section A in the full alpha (r=0.37, p=0.013) and in the high alpha range (r=0.32, p=0.032).2. clustering coefficient:section B in the full alpha range (r=0.36, p=0.016),section E in the beta 1 (r= - 0.35, p=0.021)3. modularity:section A in the full alpha range (r= - 0.30, p=0.048), and in the theta (r= 0.33, p=0.03),section G in the in the theta (r= - 0.32, p=0.037),section H in the full alpha range (r= - 0.34, p=0.025),subscale 'Perceiving emotions' in the full alpha range (r= - 0.31,p=0.042),subscale 'Managing emotions' in the high alpha range (r= - 0.39,p=0.009).Conclusions: The significant correlations between the functional connectivity and EI concentrated mainly in the alpha and theta bands. In general, in this preliminary study, we found significantcorrelations with the ability to perceive emotional information and regulate emotional processes.The results are discussed according to the network neuroscience approach to communication dynamics within the brain.