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.