Balancing Local and Global Dependencies in EEG Emotion Recognition via Multi-Scale Hybrid Attention

Electroencephalography (EEG)-based emotion recognition remains challenging due to the low signal-to-noise ratio, non-stationarity, strong inter-subject variability, and the distribution of affect-related information across multiple temporal resolutions. Although recent deep architectures have increasingly combined multi-scale local feature extraction with global dependency modeling, the adaptive integration of multi-scale representations before global encoding remains comparatively underexplored. To address this issue, we propose DSFConformer, an EEG emotion-recognition framework integrating multi-scale spatiotemporal convolution, Dynamic Sparse Feature Fusion (DSFF), and lightweight Transformer-based global modeling. The multi-scale encoder captures complementary local EEG dynamics at different temporal resolutions, while DSFF performs sample-adaptive feature reweighting and sparse suppression to reduce the influence of redundant or noise-sensitive responses. A Lite Transformer is subsequently employed to model global contextual dependencies with reduced computational cost. Experiments on the SEED dataset were conducted under complementary evaluation protocols. DSFConformer achieved an accuracy of 96.08% under chronological block-wise segment-level cross-validation. Under the stricter trial-wise protocol, it achieved 78.81% accuracy, statistically comparable to EEG-Conformer at 78.77%, with no significant paired difference. Ablation analyses further demonstrate that multi-scale convolution and DSFF provide the primary recognition-performance gains, whereas the Lite Transformer mainly improves computational efficiency while preserving comparable accuracy. Cross-session and leave-one-subject-out analyses further assess model robustness and generalization. Overall, the results indicate that adaptive sparse fusion of multi-scale EEG representations provides an effective approach for balancing local representation learning, noise-aware feature integration, and efficient global dependency modeling.