Source　localization　of　focal　electrical　activity　from　scalp　electroencephalogram　(sEEG)　signal　is　generally　modeled　as　an　inverse　problem　that　is　highly　ill-posed.　In　this　paper,　a　novel　source　localization　method　is　proposed　to　model　the　EEG　inverse　problem　using　spatio-temporal　long-short　term　memory　recurrent　neural　networks　(LSTM).　The　network　model　consists　of　two　parts,　sEEG　encoding　and　source　decoding,　to　model　the　sEEG　signal　and　receive　the　regression　of　source　location.　As　there　does　not　exist　enough　annotated　sEEG　signals　correspond　to　specific　source　locations,　simulated　data　is　generated　with　forward　model　using　finite　element　method　(FEM)　to　act　as　a　part　of　training　signals.　A　framework　for　source　localization　is　proposed　to　estimate　the　source　position　based　on　simulated　training　data.　Experiments　are　done　on　simulated　testing　data.　The　results　on　simulated　data　exhibit　good　robustness　on　noise　signal,　and　the　proposed　network　solves　the　EEG　inverse　problem　with　spatio-temporal　deep　network.　The　result　show　that　the　proposed　method　overcomes　the　highly　ill-posed　linear　inverse　problem　with　data　driven　learning.