Deep　learning　has　been　applied　to　the　recognition　of　motor　imagery　electroencephalograms　(MI-EEG)　in　brain-computer　interface,　and　the　performance　results　depend　on　data　representation　as　well　as　neural　network　structure.　Especially,　MI-EEG　is　so　complex　with　the　characteristics　of　non-stationarity,　specific　rhythms,　and　uneven　distribution;　however,　its　multidimensional　feature　information　is　difficult　to　be　fused　and　enhanced　simultaneously　in　the　existing　recognition　methods.　In　this　paper,　a　novel　channel　importance　(NCI)　based　on　time-frequency　analysis　is　proposed　to　develop　an　image　sequence　generation　method　(NCI-ISG)　for　enhancing　the　integrity　of　data　representation　and　highlighting　the　contribution　inequalities　of　different　channels　as　well.　Each　electrode　of　MI-EEG　is　converted　to　a　time-frequency　spectrum　by　utilizing　short-time　Fourier　transform;　the　corresponding　part　to　8-30　Hz　is　combined　with　random　forest　algorithm　for　computing　NCI;　and　it　is　further　divided　into　three　sub-images　covered　by　alpha　(8-13　Hz),　beta(1)　(13-21　Hz),　and　beta(2)　(21-30　Hz)　bands;　their　spectral　powers　are　further　weighted　by　NCI　and　interpolated　to　2-dimensional　electrode　coordinates,　producing　three　main　sub-band　image　sequences.　Then,　a　parallel　multi-branch　convolutional　neural　network　and　gate　recurrent　unit　(PMBCG)　is　designed　to　successively　extract　and　identify　the　spatial-spectral　and　temporal　features　from　the　image　sequences.　Two　public　four-class　MI-EEG　datasets　are　adopted;　the　proposed　classification　method　respectively　achieves　the　average　accuracies　of　98.26%　and　80.62%　by　10-fold　cross-validation　experiment;　and　its　statistical　performance　is　also　evaluated　by　multi-indexes,　such　as　Kappa　value,　confusion　matrix,　and　ROC　curve.　Extensive　experiment　results　show　that　NCI-ISG　+　PMBCG　can　yield　great　performance　on　MI-EEG　classification　compared　to　state-of-the-art　methods.　The　proposed　NCI-ISG　can　enhance　the　feature　representation　of　time-frequency-space　domains　and　match　well　with　PMBCG,　which　improves　the　recognition　accuracies　of　MI　tasks　and　demonstrates　the　preferable　reliability　and　distinguishable　ability.