HEDN: A Hard-Easy Dual Network with Task Difficulty Assessment for EEG Emotion Recognition

Multi-source domain adaptation represents an effective approach to addressing individual differences in cross-subject EEG emotion recognition. However, existing methods treat all source domains equally, neglecting the varying transfer difficulties between different source domains and the target domain. This oversight can lead to suboptimal adaptation. To address this challenge, we propose a novel Hard-Easy Dual Network (HEDN), which dynamically identifies "Hard Source" and "Easy Source" through a Task Difficulty Assessment (TDA) mechanism and establishes two specialized knowledge adaptation branches. Specifically, the Hard Network is dedicated to handling "Hard Source" with higher transfer difficulty by aligning marginal distribution differences between source and target domains. Conversely, the Easy Network focuses on "Easy Source" with low transfer difficulty, utilizing a prototype classifier to model intra-class clustering structures while generating reliable pseudo-labels for the target domain through a prototype-guided label propagation algorithm. Extensive experiments on two benchmark datasets, SEED and SEED-IV, demonstrate that HEDN achieves state-of-the-art performance in cross-subject EEG emotion recognition, with average accuracies of 93.58\% on SEED and 79.82\% on SEED-IV, respectively. These results confirm the effectiveness and generalizability of HEDN in cross-subject EEG emotion recognition.