Transfer　learning　is　provided　with　potential　research　value　and　application　prospect　in　motor　imagery　electroencephalography　(MI-EEG)-based　brain-computer　interface　(BCI)　rehabilitation　system,　and　the　source　domain　classification　model　and　transfer　strategy　are　the　two　important　aspects　that　directly　affect　the　performance　and　transfer　efficiency　of　the　target　domain　model.　Therefore,　we　propose　a　parameter　transfer　learning　method　based　on　shallow　visual　geometry　group　network　(PTL-sVGG).　First,　Pearson　correlation　coefficient　is　used　to　screen　the　subjects　of　the　source　domain,　and　the　short-time　Fourier　transform　is　performed　on　the　MI-EEG　data　of　each　selected　subject　to　acquire　the　time-frequency　spectrogram　images　(TFSI).　Then,　the　architecture　of　VGG-16　is　simplified　and　the　block　design　is　carried　out,　and　the　modified　sVGG　model　is　pre-trained　with　TFSI　of　source　domain.　Furthermore,　a　block-based　frozen-fine-tuning　transfer　strategy　is　designed　to　quickly　find　and　freeze　the　block　with　the　greatest　contribution　to　sVGG　model,　and　the　remaining　blocks　are　fine-tuned　by　using　TFSI　of　target　subjects　to　obtain　the　target　domain　classification　model.　Extensive　experiments　are　conducted　based　on　public　MI-EEG　datasets,　the　average　recognition　rate　and　Kappa　value　of　PTL-sVGG　are　94.9%　and　0.898,　respectively.　The　results　show　that　the　subjects＇　optimization　is　beneficial　to　improve　the　model　performance　in　source　domain,　and　the　block-based　transfer　strategy　can　enhance　the　transfer　efficiency,　realizing　the　rapid　and　effective　transfer　of　model　parameters　across　subjects　on　the　datasets　with　different　number　of　channels.　It　is　beneficial　to　reduce　the　calibration　time　of　BCI　system,　which　promote　the　application　of　BCI　technology　in　rehabilitation　engineering.　Copyright　©2022　Journal　of　Biomedical　Engineering.　All　rights　reserved.