Here, we tested that healthy elderly (Nold) and Alzheimer’s disease (AD) individuals can be discriminated with a moderate accuracy using resting state eyes-closed electroencephalographic (rsEEG) markers. Eyes-closed rsEEG data were collected in 100 Nold and 120 AD subjects. eLORETA freeware estimated the source current density (SCD) and functional connectivity (lagged linear connectivity, LLC) in frontal, central, parietal, occipital, temporal, and limbic regions. Delta (2–4 Hz), theta (4–8 Hz), alpha 1 (8–10.5Â Hz), and alpha 2 (10.5–13 Hz) were the frequency bands of interest. Univariate (i.e., single rsEEG marker with receiver operating characteristic, ROC, curve) and multivariate (i.e., multiple rsEEG markers with artificial neural networks, ANNs) classifiers were used. The best accuracy was of 76% with univariate classifiers and 77% with multiple classifiers. The present results suggest that both univariate and multivariate rsEEG classifiers allowed a moderate classification rate between Nold and AD individuals. Interestingly, the accuracy based on multiple rsEEG markers as inputs to ANNs was similar to that obtained with a single rsEEG marker, unveiling their information redundancy for classification purposes. In future AD studies, multiple rsEEG markers should also include other classes of independent linear (i.e. directed transfer function) and nonlinear (i.e. entropy) variables to improve the classification.