A　major　challenge　in　motor　imagery　Brain-Computer　Interfaces　(MI-BCIs)　arises　from　domain　shift　due　to　large　individual　differences.　Currently,　most　cross-subject　MI-BCI　decoding　methods　rely　on　transfer　learning　to　extract　subject-shared　features　or　align　data　distributions.　However,　these　methods　typically　require　all　unlabeled　data　from　the　target　subjects　or　labeled　calibration　data,　which　is　unavailable　in　practical　applications.　To　address　this,　we　propose　a　brain　lateralization-guided　subject　adaptive　network,　BLSAN,　to　enhance　model　generalization　through　local-global　adversarial　training.　Specifically,　two　separate　adversarial　networks　for　left　and　right　hemispheres　are　designed　to　reduce　local　differences,　and　features　extracted　from　both　hemispheres　are　combined　for　global　adversarial　training.　Additionally,　we　design　a　confidence-based　pseudo　label　generation　method　to　enhance　model　discriminability.　We　validate　the　effectiveness　of　our　approach　on　two　public　MI　datasets,　BCI　Competition　IV　2a　and　2b,　only　with　some　unlabeled　calibration　data,　which　improves　the　practicality　of　MI-BCIs.