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Keywords: EEG signal, depression prediction, graph convolutional network, time-frequency complexity, spatial topology, brain network. Citation: Liu W, Jia K and Wang Z (2024) Graph-based EEG approach ...
Present literature in using EEG for Depression Detection has focused on exploiting either the temporal or spatial structure from EEG Data. Graph Convolutional Networks allow incorporating both in an ...
Keywords: classification, depression treatment response, EEG, graph convolutional neural networks, frequency attention. Citation: Lu Z, Wang J, Wang F and Wu Z (2023) Application of graph frequency ...
In recent years, Graph Neural Networks (GNNs) based on deep learning techniques have achieved promising results in EEG-based depression detection tasks but still have some limitations. Firstly, most ...
[1] A novel EEG-based graph convolution network for depression detection: Incorporating secondary subject partitioning and attention mechanism. Expert Systems with Applications (2024).