Deep　learning　has　been　applied　for　motor　imagery　electroencephalogram　(MI-EEG)　classification　in　brain-computer　system　to　help　people　who　suffer　from　serious　neuromotor　disorders.　The　inefficiency　network　and　data　shortage　are　the　primary　issues　that　the　researchers　face　and　need　to　solve.　A　novel　MI-EEG　classification　method　is　proposed　in　this　paper.　A　plain　convolutional　neural　network　(pCNN),　which　contains　two　convolution　layers,　is　designed　to　extract　the　temporal-spatial　information　of　MI-EEG,　and　a　linear　interpolation-based　data　augmentation　(LIDA)　method　is　introduced,　by　which　any　two　unrepeated　trials　are　randomly　selected　to　generate　a　new　data.　Based　on　two　publicly　available　brain-computer　interface　competition　datasets,　the　experiments　are　conducted　to　confirm　the　structure　of　pCNN　and　optimize　the　parameters　of　pCNN　and　LIDA　as　well.　The　average　classification　accuracy　values　achieve　90.27%　and　98.23%,　and　the　average　Kappa　values　are　0.805　and　0.965　respectively.　The　experiment　results　show　the　advantage　of　the　proposed　classification　method　in　both　accuracy　and　statistical　consistency,　compared　with　the　existing　methods.　©　2022,　Shanghai　Jiao　Tong　University.