Emotion recognition plays a pivotal role in human-computer interaction by enabling machines to perceive and adapt to human affective states. While neuroimaging studies [15, 20] reveal significant functional lateralization between the left and right cerebral hemispheres during emotional processing, existing EEG-based emotion recognition methods face two critical challenges: (1) difficulty in aligning cross-hemispheric semantic features, and (2) limited generalizability across subjects and scenarios. To address these issues, we propose ShareLink, a novel EEG-based framework with Shared Cross-Hemispheric Structures. Our approach introduces three key innovative modules: (1) the Dynamic Shared Hemispheric Structure (DSHS) enforces non-Euclidean hemispheric structure constraints by sharing learnable adjacency matrix parameters across the bi-hemispheres, thereby effectively aligning semantic representations and extracting more discriminative hemispheric asymmetry features; (2) the Cross-Hemisphere Attention (CHA) shares similarity matrix between the hemispheres to establish dynamic inter-hemispheric links, enhancing the model’s ability to capture interaction information while reducing parameters and mitigating overfitting risks; (3) the Shared Hemispheres Mixture-of-Experts (SHMoE) leverages multiple expert modules to abstract representations into a finite set of characteristics and employs a shared expert set to map bi-hemispheres features into a unified space, ensuring consistent and generalizable left-right hemisphere representations. Evaluated on SEED and SEED-IV datasets under cross-subject paradigms, ShareLink achieves accuracies of 80.61%±6.16% and 63.33%±8.29%, demonstrating superior cross-domain generalization. This work provides new insights into neurophysiologically inspired computational models for emotion recognition. The codes are available at: https://github.com/Huangzx1023/ShareLink .