Motor　imagery　classification　has　been　widely　applied　in　constructing　brain　computer　interface　to　control　the　outside　equipment　as　an　alternative　neural　muscular　pathway.　EEG　as　the　most　popular　non-invasive　brain　signal　suffers　from　low　signal　to　noise　ratio　and　unpredictable　pattern　variation　even　for　the　same　subject.　To　improve　the　classification　accuracy　of　EEG　based　motor　imageries,　many　deep　learning　based　solutions　have　been　developed,　mainly　including　convolutional　neural　network　(CNN)　based　methods　and　recurrent　neural　network　(RNN)　based　methods.　There　is　no　unanimous　acknowledgement　of　the　most　appropriate　deep　learning　solution　for　motor　imagery　classification.　In　order　to　evaluate　the　performance　of　different　deep　learning　solutions　for　motor　imagery　classification,　a　comprehensive　comparison　study　has　been　conducted　in　this　paper.　CNN　based　method,　RNN　based　method,　temporal　convolution　network　(TCN)　based　method,　paralleled　combination　of　CNN　and　SRU　(Simple　Recurrent　Unit),　cascaded　combination　of　CNN　and　SRU　have　been　constructed　and　compared　based　on　extensive　experiments.　The　experiments　have　been　conducted　on　a　fair　basis　with　the　same　dataset,　the　same　preprocessing　of　the　data,　and　the　same　platform.　Experiments　have　shown　that　TCN　based　method　has　obtained　the　best　performance　and　the　paralleled　combination　of　CNN　and　RNN　has　obtained　the　second　best　performance,　which　inspired　us　to　explore　the　spatial-temporal　feature　learning　deep　network　solutions　for　further　improvement.　©　2020　IEEE.