Brain　computer　interface　(BCI)　technology　can　help　the　disabled　to　achieve　the　recovery　of　neural　function　by　using　the　Motor　Imagery　Electroencephalogram　(MI-EEG)　based　rehebilitation　system.　However,　it　is　difficult　to　acquire　a　large　amount　of　available　EEG　data,　transfer　learning　technology　provides　an　effective　method,　and　the　source　domain　selection　is　one　of　key　issues.　In　this　study,　we　develop　a　novel　parameter　transfer　learning　method　based　on　VGG-16　convolutional　neural　network　(CNN)　for　MI　classification.　First,　the　number　of　fall　MI-EEG　signals　are　augmented　with　the　sliding　window　method,　and　the　short-time　Fourier　transformation　(STFT)　is　applied　to　obtain　the　time-frequency　spectrum　images　(TFSI).　Then,　the　VGG-16　CNN　is　pre-trained　with　TFSI　of　source　domain,　which　is　divided　into　five　blocks..　The　parameters　of　the　pre-trained　CNN　are　transferred　to　the　target　network　though　a　new　transfer　strategy,　i.e.　utilization　of　the　data　of　part　subjects　from　target　domain　to　fine-tune　the　five　blocks　in　turn.　Finally,　the　fine-tuned　CNN　is　used　for　MI　classification　of　the　rest　subjects　in　target　domain.　This　work　is　evaluated　with　a　public　dataset,　the　best　classification　accuracy　of　this　study　is　96.59%.　The　results　show　that　the　high　correlation　between　the　source　domain　and　the　target　domain　is　better　than　using　the　domains　with　low　correlation,　and　the　proposed　transfer　strategy　is　efficiency.