Assessment of resting-state functional connectivity (FC) has become an important tool in studying brain disease mechanisms. Conclusions from previous resting-state investigations were based upon the hypothesis which assumed that the FC was constant throughout a period of task-free time. However, emerging evidence suggests that it may change over time. Here we investigate the dynamic FC based on the 64 electrodes EEG (electroencephalogram) of 25 healthy subjects in eyes closed (EC) and eyes open (EO) resting-state. A data-driven approach based on independent component analysis, standardized low-resolution tomography analysis, sliding time window, and graph theory are employed. Dynamic changes of FC over time with EC and EO in the visual network, the default mode network etc. are discovered. And the principal component analysis is used to the concatenated dynamic FC matrixes for finding meaningful FC patterns. Our results have complemental the traditional stationary analyses, and revealed novel insights in choosing the type of resting condition in experimental design and EEG clinical research.