IEEE Trans Neural Syst Rehabil Eng. 2022;30:1716-1726. doi: 10.1109/TNSRE.2022.3183023. Epub 2022 Jul 4.
In this study, we propose a graph sequence neural network (GSNN) to accurately decode patterns of motor imagery from electroencephalograms (EEGs) in the presence of distractions. GSNN aims to build subgraphs by exploiting biological topologies among brain regions to capture local and global relationships across characteristic channels. Specifically, we model the similarity between pairwise EEG channels by the adjacency matrix of the graph sequence neural network. In addition, we propose a node domain attention selection network in which the connection and sparsity of the adjacency matrix can be adjusted dynamically according to the EEG signals acquired from different subjects. Extensive experiments on the public Berlin-distraction dataset show that in most experimental settings, our model performs considerably better than the state-of-the-art models. Moreover, comparative experiments indicate that our proposed node domain attention selection network plays a crucial role in improving the sensibility and adaptability of the GSNN model. The results show that the GSNN algorithm obtained superior classification accuracy (The average value of Recall, Precision, and F-score were 80.44%, 81.07% and 80.54%) compared to the state-of-the-art models. Finally, in the process of extracting the intermediate results, the relationships between important brain regions and channels were revealed to different influences in distraction themes.
在这项研究中，我们提出了一种图序列神经网络（GSNN），旨在准确解码存在干扰时脑电（EEG）中的运动想象模式。GSNN 的目标是通过利用大脑区域之间的生物拓扑结构来构建子图，以捕获特征通道上的局部和全局关系。具体来说，我们通过图序列神经网络的邻接矩阵来对成对 EEG 通道之间的相似性进行建模。此外，我们提出了一种节点域注意力选择网络，其中邻接矩阵的连接和稀疏性可以根据来自不同受试者的 EEG 信号进行动态调整。在公共柏林干扰数据集上的广泛实验表明，在大多数实验设置中，我们的模型表现明显优于最先进的模型。此外，对比实验表明，我们提出的节点域注意力选择网络在提高 GSNN 模型的灵敏度和适应性方面发挥了关键作用。结果表明，与最先进的模型相比，GSNN 算法获得了更高的分类准确性（召回率、精度和 F 分数的平均值分别为 80.44%、81.07%和 80.54%）。最后，在提取中间结果的过程中，揭示了重要脑区和通道之间在干扰主题下的不同影响关系。
