The　complex　brain　network　consists　of　multiple　collaborative　regions,　which　can　be　activated　to　varying　degrees　by　motor　imagery　(MI)　and　the　induced　electroencephalogram　(EEG)　recorded　by　an　array　of　scalp　electrodes　is　usually　decoded　for　driving　rehabilitation　system.　Either　all　channels　or　partially　selected　channels　are　equally　applied　to　recognize　movement　intention,　which　may　be　incompatible　with　the　individual　differences　of　channels　from　different　locations.　In　this　paper,　a　channel　importance　based　imaging　method　is　proposed,　denoted　as　CIBI.　For　each　electrode　of　MI-EEG,　the　power　over　8-30　Hz　band　is　calculated　from　discrete　Fourier　spectrum　and　input　to　random　forest　algorithm　(RF)　to　quantify　its　contribution,　namely　channel　importance　(CI);　Then,　CI　is　used　for　weighting　the　powers　of　a　and　b　rhythms,　which　are　interpolated　to　a　32　x　32　grid　by　using　CloughTocher　method　respectively,　generating　two　main　band　images　with　time-frequency-space　information.　In　addition,　a　dual　branch　fusion　convolutional　neural　network　(DBFCNN)　is　developed　to　match　with　the　characteristic　of　two　MI　images,　realizing　the　extraction,　fusion　and　classification　of　comprehensive　features.　Extensive　experiments　are　conducted　based　on　two　public　datasets　with　four　classes　of　MI-EEG,　the　relatively　higher　average　accuracies　are　obtained,　and　the　improvements　achieve　23.95%　and　25.14%　respectively　when　using　channel　importance,　their　statistical　analysis　are　also　performed　by　Kappa　value,　confusion　matrix　and　receiver　operating　characteristic.　Experiment　results　show　that　the　personalized　channel　importance　is　helpful　to　enhance　inter-class　separability　as　well　as　the　proposed　method　has　the　outstanding　decoding　ability　for　multiple　MI　tasks.(c)　2022　Nalecz　Institute　of　Biocybernetics　and　Biomedical　Engineering　of　the　Polish　Academy　of　Sciences.　Published　by　Elsevier　B.V.　All　rights　reserved.