As　one　of　the　most　challenging　data　analysis　tasks　in　chronic　brain　diseases,　epileptic　seizure　prediction　has　attracted　extensive　attention　from　many　researchers.　Seizure　prediction,　can　greatly　improve　patients＇　quality　of　life　in　many　ways,　such　as　preventing　accidents　and　reducing　harm　that　may　occur　during　epileptic　seizures.　This　work　aims　to　develop　a　general　method　for　predicting　seizures　in　specific　patients　through　exploring　the　time-frequency　correlation　of　features　obtained　from　multi-channel　EEG　signals.　We　convert　the　original　EEG　signals　into　spectrograms　that　represent　time-frequency　characteristics　by　applying　short-time　Fourier　transform　(STFT)　to　the　EEG　signals.　For　the　first　time,　we　propose　a　dual　self-attention　residual　network　(RDANet)　that　combines　a　spectrum　attention　module　integrating　local　features　with　global　features,　with　a　channel　attention　module　mining　the　interdependence　between　channel　mappings　to　achieve　better　forecasting　performance.　Our　proposed　approach　achieved　a　sensitivity　of　89.33%,　a　specificity　of　93.02%,　an　AUC　of　91.26%　and　an　accuracy　of　92.07%　on　13　patients　from　the　public　CHB-MIT　scalp　EEG　dataset.　Our　experiments　show　that　different　EEG　signal　prediction　segment　lengths　are　an　important　factor　affecting　prediction　performance.　Our　proposed　method　is　competitive　and　achieves　good　robustness　without　patient-specific　engineering.