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 correlation-based measure of power spectral density similarity. 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：图神经网络模型正越来越多地被用于脑电图数据的分类。然而，基于图神经网络的神经系统疾病诊断，如阿尔茨海默病，仍是一个相对未经探索的研究领域。此前的研究依赖于功能连接方法来推断脑图结构，并使用简单的图神经网络架构进行阿尔茨海默病的诊断。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，可提供可解释的预测。自适应门控图卷积网络通过将基于卷积的节点特征增强与基于相关性的功率谱密度相似性度量相结合来自适应地学习图结构。此外，门控图卷积能够动态衡量不同空间尺度的贡献。所提出模型在闭眼和睁眼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，我们证明所提出的自适应门控图卷积网络能够生成与其预测一致的可解释性解释，这可能对进一步研究阿尔茨海默病相关的脑网络改变具有意义。