Combination　of　the　Motor　Imagery　EEG　(MI-EEG)　imaging　and　Deep　Convolutional　Neural　Network　is　a　prospective　recognition　method　in　brain　computer　interface.　Nowadays,　the　frequency　or　time-frequency　analysis　has　been　applied　to　each　channel　of　MI-EEG　signal　to　obtain　a　spatio-frequency　or　time-frequency　image,　and　even　the　images　from　several　channels　are　infused　to　generate　a　combined　image.　However,　the　real　position　information　of　channels　or　electrodes　is　lost　in　these　MI-EEG　images,　and　this　is　contradictory　to　the　activation　area　of　MI-tasks.　In　this　paper,　the　MI　period　and　the　frequency　band　covered　by　mu　and　beta　rhythms　are　divided　into　ten　time　windows　and　three　sub-bands,　respectively.　Then,　for　each　electrode,　Fast　Fourier　Transform　(FFT)　is　employed　to　transform　each　time　window　to　spectrum,　and　its　inverse　FFT　is　calculated　for　each　sub-band.　The　time-domain　powers　of　ten　time　windows　are　averaged　for　the　same　sub-band.　So,　three　average　powers　are　generated　as　the　time-frequency　features　of　each　electrode　of　MI-EEG.　They　are　further　arranged　to　the　electrode　coordinate　figure　by　using　Clough-Tocher　interpolation　algorithm,　and　a　complicated　image,　in　which　the　time-frequency　features　are　correctly　located　at　the　real　position　of　each　electrode,　is　obtained　to　embody　the　MI-EEG　in　detail.　Furthermore,　a　VGG　network　is　modified　to　perform　effective　recognition　for　MI-EEG　image,　and　it　is　called　mVGG.　Extensive　experiments　are　conducted　on　three　publicly　available　datasets,　and　the　10-folds　cross　validation　accuracies　of　88.62%,　92.28%　and　96.86%　are　achieved　respectively,　and　they　are　higher　than　that　of　the　state-of-the-art　imaging　methods.　Kappa　values　and　ROC　curves　demonstrate　our　method　has　lower　class　skew　and　error　costs.　The　experimental　results　show　that　the　effectiveness　of　proposed　MI-EEG　imaging　method,　and　it　is　well-matched　with　mVGG.