In　the　application　of　human-robot　interaction　(HRI)　rehabilitation　exercise　controlled　by　Electromyogram　(EMG),　if　the　discrete　motion　intention　decoded　by　EMG　signals　is　within　the　range　of　electromechanical　delay　(EMD),　through　the　rehabilitation　training,　it　will　largely　enhance　the　user＇s　feeling　feedback　and　fully　activate　the　brain　plasticity.　So　the　decoding　of　hand　movement　intention　by　transient　EMG　is　investigated　to　reach　ideal　characteristics　needed　in　the　HRI.　The　high-density　EMG　signal　(HDEMG)　database　CapgMyo　was　used　to　decode　the　motion　intention　based　on　the　transient　EMG　signals　within　the　EMD　rather　than　the　whole　recorded　signals.　We　investigate　the　impact　of　the　different　decoding　window　lengths　(WL)　and　training　sets　constructing　methods　when　using　several　machine　learning　algorithms.　In　addition,　the　transient　EMG　signals　decoding　performance　of　the　sparse　multi-electrode　EMG　signal　database　NinaPro　performing　the　same　hand　movement　was　compared.　The　visual　inspection　of　the　EMG　map　was　used　to　determine　the　onset　of　HDEMG.　The　proposed　approach　was　tested　on　EMG　decoding　window　length　of　150　ms,　demonstrating　a　mean　+/-　SD　testing　performance　of　94.21%　+/-　4.84%　after　voting.　However,　it　is　worth　noting　that　sparse　EMG　signal　did　not　achieve　the　desired　decoding　accuracy.　The　result　showed　that　the　high-density　EMG　signal　could　be　used　to　decode　the　motion　intention　within　EMD　by　simple　machine　learning　algorithms,　and　extending　the　window　length　of　training　set　could　improve　the　decoding　accuracy.