Motor　dysfunction　is　a　prominent　symptom　following　stroke,　primarily　attributed　to　neural　network　damage　that　disrupts　motor　control.　However,　the　precise　mechanism　underlying　motor　control　between　the　cerebral　cortex　and　the　muscle　system　after　stroke　remains　unclear.　Elucidating　this　complex　mechanism　can　provide　some　scientific　basis　for　rehabilitation　treatment　after　stroke.　The　aim　of　this　study　was　to　investigate　the　complex　multiband　functional　coupling　between　the　cerebral　cortex　and　the　muscular　system　based　on　a　novel　functional　cortical　muscle　coupling　analysis　method　to　shed　light　on　the　mechanism　of　multiband　neuromuscular　control　after　stroke.　In　this　study,　64-channel　of　electroencephalogram　(EEG)　signals　and　electromyogram　(EMG)　signals　encompassing　pectoralis　major,　upper　trapezius,　anterior　deltoids,　middle　deltoids,　posterior　deltoids,　biceps,　and　triceps　muscles　were　collected　from　13　stroke　patients　and　13　healthy　controls　during　the　forward　extension　movement.　Brain　source　localization　and　clustering　methods　were　employed　to　identify　active　cortical　regions　associated　with　the　movement,　then　a　uniform　empirical　Fourier　decomposition　of　EEG　and　EMG　signals　was　performed,　followed　by　the　assessment　of　functional　connectivity　between　active　cortical　regions　and　each　muscle　by　calculating　the　partial　transfer　entropy.　The　results　revealed　significant　differences　in　functional　connectivity　between　stroke　patients　and　healthy　controls　across　different　frequency　bands,　with　the　most　pronounced　coupling　strength　observed　in　the　β　band.　The　proposed　coupling　analysis　method　ensured　the　decomposition　of　EEG　and　EMG　signals　into　the　same　frequency　band,　mitigating　the　problem　of　modal　confounding　and　endpoint　effects　associated　with　signal　decomposition.　Furthermore,　it　effectively　eliminated　the　adverse　effects　of　indirect　coupling　on　multichannel　coupling　analysis.　Overall,　this　study＇s　coupling　analysis　provides　valuable　insights　into　the　complex　corticomuscular　coupling　after　stroke,　facilitating　a　comprehensive　understanding　of　post-stroke　neuromuscular　dynamics　by　unraveling　the　intricate　relationship　between　brain　and　muscle.　©　2023　IEEE.