Electroencephalogram　(EEG)-based　emotion　recognition　is　a　feasible　method　to　improve　human-robot　interaction　(HRI)　systems.　However,　most　existing　methods　fail　to　generalize　EEG　data　from　existing　users　or　from　same　user　collected　on　different　time,　which　limits　the　widespread　use　of　HRI　systems.　To　this　end,　we　propose　a　joint　distribution　adaptation　network　(JDAN)　model　for　multi-source　EEG-based　emotion　recognition.　Specifically,　A　deep　neural　network　is　firstly　used　to　extract　the　deep　features　of　EEG.　We　then　propose　two　alignment　stages:　the　joint　distribution　alignment　and　multi-classifier　alignment.　The　former　can　reduce　the　joint　feature　and　label　distribution　discrepancy　between　each　pair　of　source　and　target　domains,　while　the　latter　reduce　the　variance　in　the　multi-source　distribution.　We　evaluate　our　method　on　the　public　dataset　SEED.　Experiments　prove　that　our　JDAN　outperforms　support　vector　machine　and　a　simple　deep　neural　network　on　both　cross-subject　and　cross-day　settings,　demonstrating　its　effectiveness　in　tackling　multi-source　EEG-based　emotion　recognition.　©　2021　IEEE.