Domain Adversarial Neural Network with Reliable Pseudo-labels Iteration for cross-subject EEG emotion recognition

Abstract

Domain adaptation (DA) for electroencephalography (EEG) plays an important role in cross-subject emotion recognition. However, traditional DA methods are often limited by target domain complexities, leading to inaccurate knowledge transfer. Recent advances in subdomain adaptation, which focuses on dividing data into subdomains using pseudo-labels, have shown promise, but still rely on the quality of the generated pseudo-labels. To address this issue, we propose a novel approach, a Domain Adversarial Neural Network with Reliable Pseudo-Label Iteration (DANN-RPLI), for cross-subject emotion recognition. This method assumes that high-quality samples are close to the center and stable under perturbations. Thus, we introduced a reliable pseudo-label generation strategy with an iterative process and increased the confidence in the selected labels using perturbations. A domain adversarial network was further used to confuse subdomains, enabling a more effective cross-domain emotion representation. Our method achieved state-of-the-art results on the SEED, SEED-IV, and DEAP datasets. The superior stability of the algorithm was proven through parameter comparison experiments. Furthermore, this study reduces the impact of unreliable pseudo-labels on EEG measurements and provides a new solution for emotion recognition in practical EEG-BCI scenarios.