To　effectively　balance　the　convergence　speed　and　steady-state　error　of　online　blind　source　separation,　this　paper　develops　an　online　blind　source　separation　method　with　adaptive　step　size　based　on　an　equivariant　adaptive　separation　via　independence　(EASI)　algorithm.　First,　we　construct　a　separation　indicator　from　the　convergence　condition　of　EASI　that　can　reveal　the　separation　degree　of　mixed　signals.　Next,　a　new　forgetting　factor　suitable　for　non-stationary　cases　is　designed　to　reduce　the　error　accumulation　of　previous　data,　and　the　separation　indicator　can　be　adaptively　updated.　To　automatically　adjust　the　step　size　according　to　separation　degree,　a　nonlinear　mapping　between　the　separation　indicator　and　step　size　is　constructed.　Finally,　numerical　and　experimental　case　studies　are　provided　to　evaluate　the　performance　of　the　proposed　method,　which　comparison　of　results　proves　to　be　more　effective.　The　results　of　numerical　case　studies　show　that　the　step　size　of　the　proposed　method　can　be　adaptively　adjusted　in　the　separation　process　and　the　proposed　method　can　effectively　balance　convergence　speed　and　steady-state　error　in　both　time-invariant　and　time-varying　cases.　The　results　of　experimental　case　studies　show　that　the　proposed　method　has　higher　estimation　accuracy.