To address the limitations of existing unimodal recognition methods for depression ( e. g. , EEG(Electroencephalogram), facial expressions, text analysis), which are susceptible to environmental noise and exhibit limited accuracy, an innovative MFF-DR-DWA(Multimodal Fusion Framework is proposed for Depression Recognition with Dynamic Weight Adjustment). The framework dynamically integrates three heterogeneous data modalities-electroencephalography ( EEG ), linguistic features, and facial expressions-while employing an adaptive weighting algorithm to optimize the contribution of each modality. Experimental results on benchmark datasets demonstrate that the proposed model achieves an accuracy of 99. 09% , an F1 -score of 99. 12% , and an AUC(Area Under Curve) of 99. 97% , significantly outperforming conventional unimodal approaches and static fusion methods. This multimodal fusion strategy effectively enhances the accuracy and robustness of depression recognition.

multimodal, dynamic weighting, multimodal fusion, depression recognition, attention mechanism