Surface　electrical　impedance　myography　(sEIM)　is　important　for　evaluating　muscle　imbalance　and　muscle　diseases.　The　mixed　signals　of　impedance　in　the　subcutaneous　multilayer　tissue　captured　by　surface　electrodes　contain　diverse-redundant　components.　Taking　the　mixed　signals　captured　by　sEIM　as　the　blind　signals　and　the　muscle　layer　impedance　as　the　source　signal,　this　paper　proposed　a　method　for　separating　muscle　layer　impe-dance　based　on　the　impedance　equivalent　analysis　and　blind　source　separation,　in　order　to　improve　the　sensitivity　of　sEIM　detecting　changes　in　target　muscle　state.　Firstly,　a　limb　multilayer　cylindrical　finite　element　model　for　simulation　was　constructed.　Secondly,　a　sensitivity　method　was　employed　to　calculate　the　impedance　contribution　of　each　tissue　layer　for　excluding　redundant　weak　signals,　which　was　equated　to　a　blind　source　separation　problem　targeting　the　muscle　layer.　Finally,　the　separation　effects　of　independent　component　analysis,　principal　component　analysis,　and　equivariant　adaptive　separation　via　independence　(EASI)　were　compared　by　numerical　simulation　and　in　vivo　experiments　to　obtain　the　optimal　solution　and　verify　its　feasibility.　The　results　show　that　the　correlation　coefficient　is　above　0.98,　the　noise　immunity　is　approximately　0.8,　and　the　error　cross　talking　(ECT)　converges　to　0.876　using　the　EASI-based　method　for　separating　muscle　layer　impedance.　The　separated　muscle　layer　impedance　in　the　in　vivo　experiments　is　consistent　with　the　law　of　human　impedance　characteristics,　indicating　the　method　for　separating　muscle　layer　impedance　with　EASI　can　better　enhance　the　sensitivity　of　sEIM　to　detect　changes　in　the　target　muscle　state.　©　2022,　Editorial　Department,　Journal　of　South　China　University　of　Technology.　All　right　reserved.