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Human emotion decoding in affective brain-computer interfaces suffers a major setback due to the inter-subject variability of electroencephalography (EEG) signals. Existing approaches usually require amassing extensive EEG data of each new subject, which is prohibitively time-consuming along with poor user experience. To tackle this issue, we divide EEG representations into private components specific to each subject and shared emotional components that are universal to all subjects. According to this representation partition, we propose a plug-and-play domain adaptation method for dealing with the inter-subject variability. In the training phase, subject-invariant emotional representations and private components of source subjects are separately captured by a shared encoder and private encoders. Furthermore, we build one emotion classifier on the shared partition and subjects' individual classifiers on the combination of these two partitions. In the calibration phase, the model only requires few unlabeled EEG data from incoming target subjects to model their private components. Therefore, besides the shared emotion classifier, we have another pipeline to use the knowledge of source subjects through the similarity of private components. In the test phase, we integrate predictions of the shared emotion classifier with those of individual classifiers ensemble after modulation by similarity weights. Experimental results on the SEED dataset show that our model greatly shortens the calibration time within a minute while maintaining the recognition accuracy, all of which make emotion decoding more generalizable and practicable.