<p>Performing cross-subject and cross-session emotion recognition (ER) using EEG signals is challenging, since the nonstationarity characteristics and individual differences of EEG might lead to EEG feature distribution differences which may reduce the generalization ability of traditional models. Existing studies adopt domain adaptation algorithms to address this issue. However, most of them focus on global alignment without considering much fine-grained information, degenerating the emotion discrimination ability of EEG features. In this study, for cross-subject and cross-session EEG ER, we propose an unsupervised subdomain adaptation framework guided by pseudo label. In the framework, to reduce EEG distribution differences, a PMSan method is proposed to align EEG feature distribution at the subdomain level, where a multi-representation feature extraction (MFE) is introduced to capture diverse high-level representations, and a PLMMD loss is proposed to minimize subdomain discrepancy to learn multiple domain-invariant feature subspaces. In addition, a hierarchical pseudolabel reweighting method for EEG (EEGHPW) is proposed to generate hierarchical weights to reduce pseudo label noise, ensuring the performance of subdomain adaptation. Experiments on SEED and SEED-IV datasets showed that our framework can achieve significant performance improvements in both cross-subject and cross-session scenarios.</p>