As　an　important　paradigm　of　spontaneous　brain-computer　interfaces　(BCIs),　motor　imagery　(MI)　has　been　widely　used　in　the　fields　of　neurological　rehabilitation　and　robot　control.　Recently,　researchers　have　proposed　various　methods　for　feature　extraction　and　classification　based　on　MI　signals.　The　decoding　model　based　on　deep　neural　networks　(DNNs)　has　attracted　significant　attention　in　the　field　of　MI　signal　processing.　Due　to　the　strict　requirements　for　subjects　and　experimental　environments,　it　is　difficult　to　collect　large-scale　and　high-quality　electroencephalogram　(EEG)　data.　However,　the　performance　of　a　deep　learning　model　depends　directly　on　the　size　of　the　datasets.　Therefore,　the　decoding　of　MI-EEG　signals　based　on　a　DNN　has　proven　highly　challenging　in　practice.　Based　on　this,　we　investigated　the　performance　of　different　data　augmentation　(DA)　methods　for　the　classification　of　MI　data　using　a　DNN.　First,　we　transformed　the　time　series　signals　into　spectrogram　images　using　a　short-time　Fourier　transform　(STFT).　Then,　we　evaluated　and　compared　the　performance　of　different　DA　methods　for　this　spectrogram　data.　Next,　we　developed　a　convolutional　neural　network　(CNN)　to　classify　the　MI　signals　and　compared　the　classification　performance　of　after　DA.　The　Fréchet　inception　distance　(FID)　was　used　to　evaluate　the　quality　of　the　generated　data　(GD)　and　the　classification　accuracy,　and　mean　kappa　values　were　used　to　explore　the　best　CNN-DA　method.　In　addition,　analysis　of　variance　(ANOVA)　and　paired　t-tests　were　used　to　assess　the　significance　of　the　results.　The　results　showed　that　the　deep　convolutional　generative　adversarial　network　(DCGAN)　provided　better　augmentation　performance　than　traditional　DA　methods:　geometric　transformation　(GT),　autoencoder　(AE),　and　variational　autoencoder　(VAE)　(p　©　2020　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.