Abstract Individual differences across subjects and nonstationary characteristic of electroencephalography (EEG) limit the generalization of affective braincomputer interfaces in real-world applications. On the other hand, it is very time consuming and expensive to acquire a large number of subjectspecific labeled data for learning subject-specific models. In this paper, we propose to build personalized EEG-based affective models without labeled target data using transfer learning techniques. We mainly explore two types of subject-to-subject transfer approaches. One is to exploit shared structure underlying source domain (source subject) and target domain (target subject). The other is to train multiple individual classifiers on source subjects and transfer knowledge about classifier parameters to target subjects, and its aim is to learn a regression function that maps the relationship between feature distribution and classifier parameters. We compare the performance of five different approaches on an EEG dataset for constructing an affective model with three affective states: positive, neutral, and negative. The experimental results demonstrate that our proposed subject transfer framework achieves the mean accuracy of 76.31% in comparison with a conventional generic classifier with 56.73% in average.