Although　advances　in　deep　learning　technologies　have　greatly　facilitated　the　brain　intention　decoding　from　electroencephalogram　(EEG)　in　motor　imagery　brain-computer　interfaces　(MI-BCIs),　significant　individual　differences　hinder　the　practical　cross-subject　MI-BCI　applications.　Unlike　other　existing　domain　adversarial　transfer　networks　that　focus　on　designing　different　discriminators　to　reduce　individual　differences,　inspired　by　the　motor　lateralization　phenomenon,　we　innovatively　utilize　transformer　and　the　spatiotemporal　pattern　differences　of　EEG　as　prior　knowledge　to　enhance　the　feature　discriminability　in　our　brain　decoding　adversarial　network.　In　addition,　to　address　adversarial　network　decision　boundaries　bias　toward　the　source　domain,　we　propose　a　data　augmentation　method,　EEGMix　to　rapidly　mix　and　enrich　the　target　domain　data.　With　an　adaptive　adversarial　factor,　our　decoding　model　reduces　the　differences　in　marginal　and　conditional　distribution　simultaneously.　Three　public　MI　datasets,　2a,　2b,　and　OpenBMI　verified　our　model＇s　effectiveness.　The　accuracy　achieved　77.49%,　85.19%,　and　79.37%,　superior　to　other　state-of-the-art　algorithms.