Electroencephalogram (EEG) signals are easy to record and difficult to camouflage,so EEG-based emotion recognition has attracted more and more attention.However,the diversity and individual variability of human emotion make the EEG-based emotion recognition still a difficult problem in the field of affective computing.To solve this problem,a multi-source domain adaptive dictionary learning and sparse representation approach is proposed in this study.To reduce the difference of data distribution between the source domain and the target domain,the data of all domains are projected into a shared subspace,where a common dictionary is learned.The sparse representation has the ability of class recognition according to the criteria of minimizing intra-class error and maximizing inter-class error of sparse reconstruction.In addition,each source domain adapts its domain weight to avoid negative migration.The model parameters are solved by parameter alternating optimization,and all parameters can reach the optimal solution simultaneously.The experimental results on DEAP dataset show that the proposed approach is the best among all the compared methods.