Motor　imagery　(MI)　decoding　is　the　core　of　an　intelligent　rehabilitation　system　in　brain　computer　interface,　and　it　has　a　potential　advantage　by　using　source　signals,　which　have　higher　spatial　resolution　and　the　same　time　resolution　compared　to　scalp　electroencephalography　(EEG).　However,　how　to　delve　and　utilize　the　personalized　frequency　characteristic　of　dipoles　for　improving　decoding　performance　has　not　been　paid　sufficient　attention.　In　this　paper,　a　novel　dipole　feature　imaging　(DFI)　and　a　hybrid　convolutional　neural　network　(HCNN)　with　an　embedded　squeeze-and-excitation　block　(SEB),　denoted　as　DFI-HCNN,　are　proposed　for　decoding　MI　tasks.　EEG　source　imaging　technique　is　used　for　brain　source　estimation,　and　each　sub-band　spectrum　powers　of　all　dipoles　are　calculated　through　frequency　analysis　and　band　division.　Then,　the　3D　space　information　of　dipoles　is　retrieved,　and　by　using　azimuthal　equidistant　projection　algorithm　it　is　transformed　to　a　2D　plane,　which　is　combined　with　nearest　neighbor　interpolation　to　generate　multi　sub-band　dipole　feature　images.　Furthermore,　a　HCNN　is　designed　and　applied　to　the　ensemble　of　sub-band　dipole　feature　images,　from　which　the　importance　of　sub-bands　is　acquired　to　adjust　the　corresponding　attentions　adaptively　by　SEB.　Ten-fold　cross-validation　experiments　on　two　public　datasets　achieve　the　comparatively　higher　decoding　accuracies　of　84.23%　and　92.62%,　respectively.　The　experiment　results　show　that　DFI　is　an　effective　feature　representation,　and　HCNN　with　an　embedded　SEB　can　enhance　the　useful　frequency　information　of　dipoles　for　improving　MI　decoding.