MAST-GCN: Multi-Scale Adaptive Spatial-Temporal Graph Convolutional Network for EEG-Based Depression Recognition
H Lu, Z You, Y Guo, X Hu - IEEE Transactions on Affective …, 2024 - ieeexplore.ieee.org
Recently, depression recognition through EEG has gained significant attention. However,
two challenges have not been properly addressed in prior automated depression …
two challenges have not been properly addressed in prior automated depression …
HEMAsNet: A Hemisphere Asymmetry Network Inspired by the Brain for Depression Recognition From Electroencephalogram Signals
J Shen, K Li, H Liang, Z Zhao, Y Ma… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
Depression is a prevalent mental disorder that affects a significant portion of the global
population. Despite recent advancements in EEG-based depression recognition models …
population. Despite recent advancements in EEG-based depression recognition models …
Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition
K Shao, R Wang, Y Hao, L Hu, M Chen - arXiv preprint arXiv:2406.16968, 2024 - arxiv.org
Depression recognition based on physiological signals such as functional near-infrared
spectroscopy (fNIRS) and electroencephalogram (EEG) has made considerable progress …
spectroscopy (fNIRS) and electroencephalogram (EEG) has made considerable progress …