Motor　imagery　electroencephalography　(MI-EEG)　is　usually　used　as　a　driving　signal　in　neuro-rehabilitation　systems,　and　its　feature　space　varies　with　the　recovery　progress.　It　is　required　to　endow　the　recognition　model　with　continuous　learning　and　self-updating　capability.　Broad　learning　system　(BLS)　can　be　remodeled　in　an　efficient　incremental　learning　way.　However,　its　architecture　is　intractable　to　change　automatically　to　adapt　to　new　incoming　MI-EEG　with　time-varying　and　complex　temporal-spatial　characteristics.　In　this　paper,　an　adaptive　session-incremental　BLS　(ASiBLS)　is　proposed　based　on　mutual　information　theory　and　BLS.　For　the　initial　session　data,　a　compact　temporal-spatial　feature　extractor　(CTS)　is　designed　to　acquire　the　temporal-spatial　features,　which　are　input　to　a　baseline　BLS　(bBLS).　Furthermore,　for　new　session　data,　a　mutual　information　maximization　constraint　(MIMC)　is　introduced　into　the　loss　function　of　CTS　to　make　the　features＇　probability　distribution　sufficiently　similar　to　that　of　the　previous　session,　a　new　incremental　BLS　sequence　(iBLS)　is　obtained　by　adding　a　small　number　of　nodes　to　the　previous　model,　and　so　on.　Experiments　are　conducted　based　on　the　BCI　Competition　IV-2a　dataset　with　two　sessions　and　IV-2b　dataset　with　five　sessions,　ASiBLS　achieves　average　decoding　accuracies　of　79.89%　and　87.04%,　respectively.　The　kappa　coefficient　and　forgetting　rate　are　also　used　to　evaluate　the　model　performance.　The　results　show　that　ASiBLS　can　adaptively　generate　an　optimized　and　reduced　model　for　each　session　successively,　which　has　better　plasticity　in　learning　new　knowledge　and　stability　in　retaining　old　knowledge　as　well.