Cross-Subject Emotion Recognition Using Deep Adaptation Networks

Affective models based on EEG signals have been proposed in recent years. However, most of these models require subject-specific training and generalize worse when they are applied to new subjects. This is mainly caused by the individual differences across subjects. While, on the other hand, it is time-consuming and high cost to collect subject-specific training data for every new user. How to eliminate the individual differences in EEG signals for implementation of affective models is one of the challenges. In this paper, we apply Deep adaptation network (DAN) to solve this problem. The performance is evaluated on two publicly available EEG emotion recognition datasets, SEED and SEED-IV, in comparison with two baseline methods without domain adaptation and several other domain adaptation methods. The experimental results indicate that the performance of DAN is significantly superior to the existing methods.