Transfer　learning,　as　a　new　machine　learning　methodology,　may　solve　problems　in　related　but　different　domains　by　using　existing　knowledge,　and　it　is　often　applied　to　transfer　training　data　from　another　domain　for　model　training　in　the　case　of　insufficient　training　data.　In　recent　years,　an　increasing　number　of　researchers　who　engage　in　brain-computer　interface　(BCI),　have　focused　on　using　transfer　learning　to　make　most　of　the　available　electroencephalogram　data　from　different　subjects,　effectively　reducing　the　cost　of　expensive　data　acquisition　and　labeling　as　well　as　greatly　improving　the　learning　performance　of　the　model.　This　paper　surveys　the　development　of　transfer　learning　and　reviews　the　transfer　learning　approaches　in　BCI.　In　addition,　according　to　the　＇what　to　transfer＇　question　in　transfer　learning,　this　review　is　organized　into　three　contexts:　instance-based　transfer　learning,　parameter-based　transfer　learning,　and　feature-based　transfer　learning.　Furthermore,　the　current　transfer　learning　applications　in　BCI　research　are　summarized　in　terms　of　the　transfer　learning　methods,　datasets,　evaluation　performance,　etc.　At　the　end　of　the　paper,　the　questions　to　be　solved　in　future　research　are　put　forward,　laying　the　foundation　for　the　popularization　and　in-depth　research　of　transfer　learning　in　BCI.　©　2022,　Shanghai　Jiao　Tong　University.