MAST-GCN: Multi-Scale Adaptive Spatial-Temporal Graph Convolutional Network for EEG-Based Depression Recognition

Recently, depression recognition through EEG has gained significant attention. However, two challenges have not been properly addressed in prior automated depression recognition and classification studies: 1) EEG data lacks an explicit topological structure. 2) Capturing spatio-temporal features of...

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Veröffentlicht in:IEEE transactions on affective computing Jg. 15; H. 4; S. 1985 - 1996
Hauptverfasser: Lu, Haifeng, You, Zhiyang, Guo, Yi, Hu, Xiping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.10.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Affective computing
Artificial neural networks
attention
Brain modeling
Convolution
Convolutional neural networks
Depression
Depression recognition
EEG
Electroencephalography
Feature extraction
graph convolutional network
Spatiotemporal data
Topology
ISSN:1949-3045, 1949-3045
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Zusammenfassung:Recently, depression recognition through EEG has gained significant attention. However, two challenges have not been properly addressed in prior automated depression recognition and classification studies: 1) EEG data lacks an explicit topological structure. 2) Capturing spatio-temporal features of EEG signals is difficult. In this paper, we propose Multi-scale Adaptive Spatial-Temporal Graph Convolutional Network (MAST-GCN) for mining latent topological structure among EEG channels and capturing discriminative spatio-temporal features. First, we integrate Adaptive Graph Convolution (AGC) that merges the inherent graph construction method with a data-driven graph reconstruction method. The model uses attention mechanism to learn an adaptive topological structure and semantic information from different layers and classes. Second, we propose Multi-Scale Time Convolutional Layer (MS-TCL), which captures long-term dependence from EEG data. Since Graph Convolution is weak for aggregating the spatio-temporal information, we have implemented a 3D Graph Convolution (G3D) to directly capture the spatio-temporal dependencies by reconstructing the spatio-temporal graph. The experimental results demonstrate that MAST-GCN consistently outperforms state-of-the-art methods on two datasets. Furthermore, we use the gradient-based saliency maps for interpretability analysis, discovering the active brain regions and important electrode pairs related to depression.
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ISSN:1949-3045
1949-3045
DOI:10.1109/TAFFC.2024.3392904