Graph embedding is an effective method for deriving low-dimensional representations of graph data. The power of graph deep learning methods to characterize electroencephalogram (EEG) graph embedding is still in question. We designed a novel graph variational auto-encoder (GVAE) method to extract nodal features of brain functional connections. A new decoder model for the GVAEs network is proposed, which considers the node neighborhood of the reconstructed adjacency matrix. The GVAE is applied and tested on 3 biometric databases which contain 64 to 9 channels’ EEG recordings. For all datasets, promising results with more than 95% accuracy and considerably low computational cost are achieved compared to state-of-the-art user identification methods. The proposed GVAE is robust to a limited number of nodes and stable to users’ task performance. Moreover, we developed a traditional variational auto-encoder to demonstrate that more accurate features can be obtained when observing EEG-based brain connectivity from a graph perspective.

图嵌入是导出图数据的低维表示的有效方法。图深度学习方法在表征脑电图 (EEG) 图嵌入方面的能力仍然存在问题。我们设计了一种新颖的图变分自动编码器 (GVAE) 方法来提取大脑功能连接的节点特征。提出了一种新的 GVAEs 网络解码器模型，该模型考虑了重构邻接矩阵的节点邻域。GVAE 在包含 64 到 9 个通道的 EEG 记录的 3 个生物识别数据库上应用和测试。对于所有数据集，与最先进的用户识别方法相比，获得了超过 95% 的准确率和相当低的计算成本的有希望的结果。所提出的 GVAE 对有限数量的节点具有鲁棒性，并且对用户的任务性能稳定。