Machine　learning　algorithms　have　been　widely　used　in　clinical　electroencephalography　(EEG)　studies　to　help　diagnose　various　diseases　and　classify　patient　states.　The　application　of　machine　learning　algorithms　in　clinical　EEG　usually　requires　a　large　amount　of　annotated　data,　and　annotating　long-term　clinical　EEG　is　very　expensive.　Active　learning　algorithms　can　actively　query　the　user　for　labels,　and　the　number　of　samples　to　train　a　model　can　often　be　much　lower　than　the　number　required　in　normal　supervised　learning.　We　have　designed　and　developed　a　clinical　EEG　research　platform　to　support　progressive　model　construction,　enabling　researchers　to　focus　on　clinical　EEG　data　analysis　and　algorithm　design.　The　platform　provides　general　auxiliary　functions　for　various　research　tasks,　including　data　format　conversion,　EEG　visualization,　annotation,　dynamic　loading　algorithms,　and　progressive　model　construction.　In　our　experiments,　progressively　building　models　can　achieve　the　same　or　better　performance　by　labeling　only　about　18%　of　the　samples　as　compared　to　randomly　selecting　samples　for　labeling.　©　2022　IEEE.