EEG-based emotion recognition using domain adaptation network

This paper explores a fundamental problem of eliminating the differences between source subject and target subject in EEG-based emotion recognition. The major limitation of traditional classification methods is that the lack of domain adaptation and subspace alignment will degrade the performance of cross-subject emotion recognition. To address this problem, we adopt Domain Adaptation Network (DAN) for knowledge transfer, which maintains both feature discriminativeness and domain-invariance during training stage. A feed-forward neural network is constructed by augmenting a few standard layers and a gradient reversal layer. Compared with five traditional methods, DAN outperforms its counterparts and achieves the mean accuracy of 79.19%. Moreover, a visualization of the features learned by DAN is represented in this paper, which intuitively describes the transfer virtue of domain adaptation network.