UDA-DDA: Unsupervised domain adaptation with dynamic distribution alignment network for emotion recognition using EEG signals

In this paper, we address the challenge of individual variability in affective brain-computer interfaces (aBCI), which employ electroencephalogram (EEG) signals to monitor and recognize human emotional states, thereby facilitating the advancement of emotion-aware technologies. The variability in EEG data across individuals poses a significant barrier to the development of effective and widely applicable aBCI models. To mitigate this issue, we propose a novel transfer learning framework called Unsupervised Domain Adaptation (UDA) with Dynamic Distribution Alignment (UDA-DDA). This approach aligns the marginal and conditional probability distributions of source and target domains by employing maximum mean discrepancy (MMD) and conditional maximum mean discrepancy (CMMD). Firstly, we introduce a dynamic distribution alignment mechanism to adjust differences throughout training and enhance adaptation. Additionally, a pseudo-label confidence filtering module is integrated into the unsupervised process to refine pseudo-label generation and optimize the estimation of conditional distributions. In order to demonstrate the effectiveness and robustness of the proposed UDA-DDA method, extensive experiments are conducted on EEG benchmark databases (SEED, SEED-IV and DEAP). Evaluations of the algorithm’s performance in comparison with other UDA with dynamic distribution alignment network methods indicate the proposed method achieves state-of-the-art results in emotion recognition across various scenarios, including cross-subject and cross-session conditions. This advancement significantly enhances the accuracy and generalization of emotion recognition, potentially fostering the development of personalized aBCI applications. The source code is accessible at https://github.com/XuanSuTrum/UDA-DDA.