Background/Objectives:　Motor　neurorehabilitation　can　be　realized　by　gradually　learning　diverse　motor　imagery　(MI)　tasks.　EEG-based　brain-computer　interfaces　(BCIs)　provide　an　effective　solution.　Nevertheless,　existing　MI　decoding　methods　cannot　balance　plasticity　for　unseen　tasks　and　stability　for　old　tasks.　This　paper　proposes　a　generative　diffusion-based　task　Incremental　Learning　(IL)　method　called　GD-TIL.　Methods:　First,　data　augmentation　is　employed　to　increase　data　diversity　by　segmenting　and　recombining　EEG　signals.　Second,　to　capture　temporal-spatial　features　(TSFs)　from　different　temporal　resolutions,　a　multi-scale　temporal-spatial　feature　extractor　(MTSFE)　is　developed　via　integrating　multiscale　temporal-spatial　convolutions,　a　dual-branch　pooling　operation,　multiple　multi-head　self-attention　mechanisms,　and　a　dynamic　convolutional　encoder.　The　proposed　self-supervised　task　generalization　(SSTG)　mechanism　introduces　a　regularization　constraint　to　guide　MTSFE　and　unified　classifier　updating,　which　combines　labels　and　semantic　similarity　between　the　augmentation　with　original　views　to　enhance　model　generalizability　for　unseen　tasks.　In　the　IL　phase,　a　prototype-guided　generative　replay　module　(PGGR)　is　used　to　generate　old　tasks＇　TSFs　by　training　a　lightweight　diffusion　model　based　on　the　prototype　and　label　of　each　task.　Furthermore,　the　generated　TSF　is　merged　with　a　new　TSF　to　fine-tune　the　convolutional　encoder　and　update　the　classifier　and　PGGR.　Finally,　GD-TIL　is　evaluated　on　a　self-collected　ADL-MI　dataset　with　two　MI　pairs　and　a　public　dataset　with　four　MI　tasks.　Results:　The　continuous　decoding　accuracy　reaches　80.20%　and　81.32%,　respectively.　The　experimental　results　exhibit　the　excellent　plasticity　and　stability　of　GD-TIL,　even　beating　the　state-of-the-art　IL　methods.　Conclusions:　Our　work　illustrates　the　potential　of　MI-based　BCI　and　generative　AI　for　continuous　neurorehabilitation.