Graph neural network (GNN) models are increasingly being used for the classification of electroencephalography (EEG) data. However, GNN-based diagnosis of neurological disorders, such as Alzheimer's disease (AD), remains a relatively unexplored area of research. Previous studies have relied on functional connectivity methods to infer brain graph structures and used simple GNN architectures for the diagnosis of AD. In this work, we propose a novel adaptive gated graph convolutional network (AGGCN) that can provide explainable predictions. AGGCN adaptively learns graph structures by combining convolution-based node feature enhancement with a well-known correlation-based measure of functional connectivity. Furthermore, the gated graph convolution can dynamically weigh the contribution of various spatial scales. The proposed model achieves high accuracy in both eyes-closed and eyes-open conditions, indicating the stability of learned representations. Finally, we demonstrate that the proposed AGGCN model generates consistent explanations of its predictions that might be relevant for further study of AD-related alterations of brain networks.

翻译：图神经网络（GNN）模型正越来越多地被用于脑电图（EEG）数据的分类。然而，基于GNN的神经系统疾病（如阿尔茨海默病）诊断仍是一个相对未充分探索的研究领域。以往研究主要依赖功能连接性方法推断大脑图结构，并使用简单的GNN架构进行AD诊断。在本工作中，我们提出了一种新颖的自适应门控图卷积网络（AGGCN），该网络能够提供可解释的预测。AGGCN通过将基于卷积的节点特征增强与基于相关性的功能连接度量相结合，自适应地学习图结构。此外，门控图卷积可以动态衡量不同空间尺度的贡献。所提出的模型在闭眼和睁眼两种条件下均实现了高准确率，表明学习到的表征具有稳定性。最后，我们证明所提出的AGGCN模型能够为其预测生成一致的解释，这可能对进一步研究AD相关脑网络改变具有重要意义。