Deep　transfer　learning　(DTL)　has　developed　rapidly　in　the　field　of　motor　imagery　(MI)　on　brain-computer　interface　(BCI)　in　recent　years.　DTL　utilizes　deep　neural　networks　with　strong　generalization　capabilities　as　the　pre-training　framework　and　automatically　extracts　richer　and　more　expressive　features　during　the　training　process.　The　goal　of　this　paper　is　utilizing　the　DTL　to　classify　MI　electroencephalogram　(EEG)　signals　on　the　premise　of　a　small　data　set.　The　publicly　available　dataset　III　of　the　second　BCI　competition　is　applied　in　both　the　training　part　and　testing　part　to　evaluate　the　effectiveness　of　the　proposed　method.　Firstly　in　the　process,　finite　impulse　response　(FIR)　filter　and　wavelet　transform　threshold　denoising　method　are　used　to　remove　redundant　signals　and　artifacts　in　EEG　signals.　Then,　the　continuous　wavelet　transform　(CWT)　is　utilized　to　convert　the　one-dimensional　EEG　signal　into　a　two-dimensional　time-frequency　amplitude　representation　as　the　input　of　the　pre-trained　convolutional　neural　network　(CNN)　for　classifying　two　types　of　MI　signals.　Employing　the　input　data　of　140　trials　for　training,　the　final　classification　accuracy　rate　reaches　96.43%.　Compared　with　the　results　of　some　superior　machine　learning　models　using　the　same　data　set,　the　accuracy　and　Kappa　value　of　this　DTL　model　are　better.　Therefore,　the　proposed　scheme　of　MI　EEG　signal　classification　based　on　the　DTL　method　offers　preferably　empirical　performance.　©　2021　IEEE.