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

脑电图 计算机科学 图形 模式识别(心理学) 人工智能 心理学 神经科学 理论计算机科学
作者
Haifeng Lu,Zhiyang You,Yi Guo,Xiping Hu
出处
期刊:IEEE Transactions on Affective Computing [Institute of Electrical and Electronics Engineers]
卷期号:15 (4): 1985-1996 被引量:7
标识
DOI:10.1109/taffc.2024.3392904
摘要

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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