In　order　to　improve　the　noninvasive　decoding　precision　of　hand　movement　parameters　from　continuous　electroencephalogram　(EEG),　the　laws　of　the　influence　of　decoding　parameters　such　as　brain　rhythms　and　channel　combination　based　on　multiple　linear　regression　models　were　investigated,　Firstly,　the　wavelet　coefficients　of　EEG　characteristic　frequencies　corresponding　to　each　channel　were　extracted.　In　addition,　brain　function　network　(BFN),　which　possessed　characteristics　of　small-world　network,　was　constructed　based　on　the　filter　threshold　on　Network　Cost　and　Spearman　correlation　coefficient　matrixes　of　wavelet　coefficients　among　the　channels.　Then,　the　influence　of　different　task　states　on　decoding　parameters　was　studied　and　analyzed　through　parameters　of　BFNs　(standard　deviation　of　average　degree,　average　path　length,　average　clustering　coefficient).　At　last,　the　decoding　parameters　of　hand　movement　were　singled　out　according　to　P　value　of　Kruskal-Wallis.　The　results　showed　that　EEG　low　and　intermediate　frequency　bands　and　the　8　channels　combination　set　have　greater　contribution　to　decode　hand　movement.　The　paper　sheds　light　on　new　ideas　for　choosing　decoding　parameters　of　subsequent　hand　movements.