The　brain–computer　interface　(BCI)　based　on　motor　imagery　electroencephalography　(EEG)　is　widely　used　because　of　its　convenience　and　safety.　However,　due　to　the　distributional　disparity　between　EEG　signals,　data　from　other　subjects　cannot　be　used　directly　to　train　a　subject-specific　classifier.　For　efficient　use　of　the　labeled　data,　domain　transfer　learning　and　adversarial　learning　are　gradually　applied　to　BCI　classification　tasks.　While　these　methods　improve　classification　performance,　they　only　align　globally　and　ignore　task-specific　class　boundaries,　which　may　lead　to　the　blurring　of　features　near　the　classification　boundaries.　Simultaneously,　they　employ　fully　shared　generators　to　extract　features,　resulting　in　the　loss　of　domain-specific　information　and　the　destruction　of　performance.　To　address　these　issues,　we　propose　a　novel　dual-attention-based　adversarial　network　for　motor　imagery　classification　(MI-DABAN).　Our　framework　leverages　multiple　subjects’　knowledge　to　improve　a　single　subject＇s　motor　imagery　classification　performance　by　cleverly　using　a　novel　adversarial　learning　method　and　two　unshared　attention　blocks.　Specifically,　without　introducing　additional　domain　discriminators,　we　iteratively　maximize　and　minimize　the　output　difference　between　the　two　classifiers　to　implement　adversarial　learning　to　ensure　accurate　domain　alignment.　Among　them,　maximization　is　used　to　identify　easily　confused　samples　near　the　decision　boundary,　and　minimization　is　used　to　align　the　source　and　target　domain　distributions.　Moreover,　for　the　shallow　features　from　source　and　target　domains,　we　use　two　non-shared　attention　blocks　to　preserve　domain-specific　information,　which　can　prevent　the　negative　transfer　of　domain　information　and　further　improve　the　classification　performance　on　test　data.　We　conduct　extensive　experiments　on　two　publicly　available　EEG　datasets,　namely　BCI　Competition　IV　Datasets　2a　and　2b.　The　experiment　results　demonstrate　our　method＇s　effectiveness　and　superiority.　©　2022　Elsevier　Ltd