Electroencephalogram (EEG) has been widely used in neurological disease detection, i.e., major depressive disorder (MDD). Recently, some deep EEG-based MDD detection attempts have been proposed and achieved promising performance. These works, however, still suffer from the following limitations, such as insufficient exploration of the EEG-based topological structure, information loss caused by high-dimensional data compression, and under-estimation of intra-class difference and inter-class similarity. To solve these issues, we propose an EEG-based MDD detection model named Self-attention Graph Pooling with Soft Label (SGP-SL). Specifically, we explore the local and global connections among EEG channels to construct an EEG-based graph in advance. By leveraging multiple self-attention graph pooling modules, the constructed graph is then gradually refined, followed by graph pooling, to aggregate information from less-important nodes to more-important ones. In this way, the feature representation with better discriminability can be learned from EEG signals. In addition, the soft label strategy is also adopted to build the loss function, aiming to further enhance the feature discriminability. Experimental results on the MODMA dataset demonstrate the superiority of the proposed method. What's more, extensive ablation studies are conducted to verify the effectiveness of the proposed elements in our model.

中文翻译：

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探索基于脑电图的拓扑结构和软标签的自注意力图池用于抑郁症检测


脑电图（EEG）已广泛应用于神经系统疾病检测，即重度抑郁症（MDD）。最近，一些基于深度脑电图的 MDD 检测尝试被提出并取得了可喜的性能。然而，这些工作仍然存在以下局限性，例如对基于脑电图的拓扑结构探索不足、高维数据压缩导致的信息丢失以及类内差异和类间相似性的低估。为了解决这些问题，我们提出了一种基于 EEG 的 MDD 检测模型，名为带有软标签的自注意力图池（SGP-SL）。具体来说，我们探索脑电图通道之间的局部和全局连接，以提前构建基于脑电图的图。通过利用多个自注意力图池化模块，构建的图逐渐细化，然后进行图池化，将信息从不太重要的节点聚合到更重要的节点。这样，就可以从脑电信号中学习到具有更好区分性的特征表示。此外，还采用软标签策略构建损失函数，旨在进一步增强特征的可辨别性。 MODMA数据集上的实验结果证明了该方法的优越性。更重要的是，进行了广泛的消融研究，以验证我们模型中提出的元素的有效性。