A dynamic eeg-based emotion recognition model for digital interaction design: A sparse spatiotemporal modeling approach

With the continuous evolution of digital interaction design toward intelligence and affective computing, the ability to accurately and stably perceive users’ emotional states during interaction has become critical for enhancing user experience and system adaptability. Electroencephalogram (EEG) signals directly reflect neural activity in the human brain and therefore hold significant potential for emotion recognition tasks. However, EEG signals are characterized by strong noise interference, substantial inter-subject variability, and continuously evolving emotional states over time, which pose challenges to spatial feature representation, long-term dependency modeling, and prediction stability. To address these issues, this paper proposes a dynamic EEG-based emotion recognition model for digital interaction design, termed IDEP-Net (Interactive Dynamic Emotion Perception Network). Motivated by the requirements of dynamic affect perception in interactive scenarios, the proposed model integrates spatial heterogeneity modeling, local spatiotemporal embedding, and low-complexity sparse global dependency learning into a unified architecture. Specifically, a channel-adaptive modulation(CAM) is introduced to explicitly capture the heterogeneous contributions of different brain regions to emotion recognition. A convolutional spatiotemporal embedding module is then employed to extract local rhythmic patterns from EEG signals. Furthermore, an importance-guided sparse attention mechanism is incorporated to model long-range dependencies across temporal segments while effectively reducing computational complexity. To enhance prediction stability in interactive scenarios, a temporal continuity constraint is added to the optimization objective, encouraging smoother and more consistent emotional predictions over time. Extensive experiments conducted on the SEED and SEED-IV datasets under cross-subject evaluation protocols demonstrate that the proposed IDEP-Net achieves superior recognition performance compared to multiple baseline methods, while significantly improving temporal stability and generalization capability. These results validate the effectiveness and robustness of the proposed model for EEG-based emotion recognition and provide a feasible and efficient technical solution for affect-aware digital interaction design.