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MSDSANet: Multimodal Emotion Recognition Based on Multi-Stream Network and Dual-Scale Attention Network Feature
Weitong Sun1,2,3, Xingya Yan1,2, Yuping Su3,4
1School of Digital Art, Xi'an University of Posts & Telecommunications, Xi'an 710061, China.
Abstract:
Aiming at the shortcomings of EEG emotion recognition models in feature representation granularity and spatiotemporal dependence modeling, a multimodal emotion recognition model integrating multi-scale feature representation and attention mechanism is proposed. The model consists of a feature extraction module, feature fusion module, and classification module. The feature extraction module includes a multi-stream network module for extracting shallow EEG features and a dual-scale attention module for extracting shallow EOG features. The multi-scale and multi-granularity feature fusion improves the richness and discriminability of multimodal feature representation. Experimental results on two datasets show that the proposed model outperforms the existing model.

