Electroencephalography　(EEG)　based　Brain　Computer　Interface　(BCI)　attracts　more　and　more　attention.　Motor　Imagery　(MI)　is　a　popular　one　among　all　the　EEG　paradigms.　Building　a　subject-independent　MI　EEG　classification　procedure　is　a　main　challenge　in　practical　applications.　Recently,　Convolutional　Neural　Network　(CNN)　has　been　introduced　and　achieved　state-of-the-art　performance　in　related　areas.　To　extract　subject-independent　features　in　MI　EEG　classification,　we　propose　the　MI3DNet,　using　a　remapped　signal　cubic　as　the　input.　Experiments　show　that　MI3DNet　has　a　higher　performance　with　fewer　parameters　and　layers.　We　also　give　a　method　to　plot　the　parameters　of　the　dense　layer,　and　explain　its　effect.　©　2020　IEEE.