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MS²-GNN: Exploring GNN-Based Multimodal Fusion Network for Depression Detection

14 references·4 citing·53 second hopsampled

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References

2018EmotionMeter: A Multimodal Framework for Recognizing Human Emotions1,274 cited
2016AVEC 2016505 cited
2020Feature-level fusion approaches based on multimodal EEG data for depression recognition351 cited
2018Detecting Depression with Audio/Text Sequence Modeling of Interviews350 cited
2016DepAudioNet299 cited
2020Emotion Recognition From Multimodal Physiological Signals Using a Regularized Deep Fusion of Kernel Machine208 cited
2017Dynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding193 cited
2019Multimodal Fusion for Objective Assessment of Cognitive Workload: A Review185 cited
2017Topic Modeling Based Multi-modal Depression Detection155 cited
2016Multimodal and Multiresolution Depression Detection from Speech and Facial Landmark Features146 cited
2020Transformer Encoder With Multi-Modal Multi-Head Attention for Continuous Affect Recognition142 cited
2021MDN: A Deep Maximization-Differentiation Network for Spatio-Temporal Depression Detection137 cited
2019Multitask Representation Learning for Multimodal Estimation of Depression Level115 cited
2014Multimodal Prediction of Affective Dimensions and Depression in Human-Computer Interactions114 cited

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2024GDDN: Graph Domain Disentanglement Network for Generalizable EEG Emotion Recognition75 cited
2023A novel study for depression detecting using audio signals based on graph neural network54 cited
2023Emotion Recognition From Few-Channel EEG Signals by Integrating Deep Feature Aggregation and Transfer Learning49 cited
2024MAST-GCN: Multi-Scale Adaptive Spatial-Temporal Graph Convolutional Network for EEG-Based Depression Recognition47 cited