Objective.　Motor　imagery　electroencephalography　(MI-EEG)　produces　one　of　the　most　commonly　used　biosignals　in　intelligent　rehabilitation　systems.　The　newly　developed　3D　convolutional　neural　network　(3DCNN)　is　gaining　increasing　attention　for　its　ability　to　recognize　MI　tasks.　The　key　to　successful　identification　of　movement　intention　is　dependent　on　whether　the　data　representation　can　faithfully　reflect　the　cortical　activity　induced　by　MI.　However,　the　present　data　representation,　which　is　often　generated　from　partial　source　signals　with　time-frequency　analysis,　contains　incomplete　information.　Therefore,　it　would　be　beneficial　to　explore　a　new　type　of　data　representation　using　raw　spatiotemporal　dipole　information　as　well　as　the　possible　development　of　a　matching　3DCNN.　Approach.　Based　on　EEG　source　imaging　and　3DCNN,　a　novel　decoding　method　for　identifying　MI　tasks　is　proposed,　called　ESICNND.　MI-EEG　is　mapped　to　the　cerebral　cortex　by　the　standardized　low　resolution　electromagnetic　tomography　algorithm,　and　the　optimal　sampling　points　of　the　dipoles　are　selected　as　the　time　of　interest　to　best　reveal　the　difference　between　any　two　MI　tasks.　Then,　the　initial　subject　coordinate　system　is　converted　to　a　magnetic　resonance　imaging　coordinate　system,　followed　by　dipole　interpolation　and　volume　down-sampling;　the　resulting　3D　dipole　amplitude　matrices　are　merged　at　the　selected　sampling　points　to　obtain　4D　dipole　feature　matrices　(4DDFMs).　These　matrices　are　augmented　by　sliding　window　technology　and　input　into　a　3DCNN　with　a　cascading　architecture　of　three　modules　(3M3DCNN)　to　perform　the　extraction　and　classification　of　comprehensive　features.　Main　results.　Experiments　are　carried　out　on　two　public　datasets;　the　average　ten-fold　CV　classification　accuracies　reach　88.73%　and　96.25%,　respectively,　and　the　statistical　analysis　demonstrates　outstanding　consistency　and　stability.　Significance.　The　4DDFMs　reveals　the　variation　of　cortical　activation　in　a　3D　spatial　cube　with　a　temporal　dimension　and　matches　the　3M3DCNN　well,　making　full　use　of　the　high-resolution　spatiotemporal　information　from　all　dipoles.