There is a recent interest in finding neurophysiological biomarkers which will facilitate the diagnosis and understanding of the neural basis of different psychiatric disorders. In this paper, we evaluated the resting-state global EEG connectivity as a potential biomarker for depressive and anxiety symptoms. For this, we evaluated a population of 119 subjects, including 75 healthy subjects and 44 patients with major depressive disorder. We calculated the global connectivity (spectral coherence) in a setup of 60 EEG channels, for six different spectral bands: theta, alpha1, alpha2, beta1, beta2, and gamma. These global connectivity scores were used to train a Support Vector Regressor to predict symptoms measured by the Beck Depression Inventory (BDI) and the Spielberger Trait Anxiety Inventory (TAI). Experiments showed a significant prediction of both symptoms, with a mean absolute error (MAE) of 8.07±6.98 and 11.52±8.7 points, respectively. Among the most discriminating features, the global connectivity in the alpha2 band (10.0-12.0Hz) presented significantly positive Spearman's correlation with the depressive (rho = 0.32, pFDR <0.01), and the anxiety symptoms (rho = 0.26, pFDR<0.01).Clinical relevance-This study demonstrates that EEG global connectivity can be used to predict depression and anxiety symptoms measured by widely used questionnaires.