论文标题
基于注意的图形重新系统，用于从RAW EEG信号中检测到电动机意图
Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals
论文作者
论文摘要
在先前的研究中，解码脑电图（EEG）信号尚未考虑EEG电极的拓扑关系。但是，最新的神经科学提出了大脑网络连通性。因此，可能无法通过欧几里得距离适当地测量EEG通道之间的相互作用。为了填补空白，提出了一个基于注意力的图形残差网络，即图形卷积神经网络（GCN）的新结构，以检测来自原始EEG信号的人类运动意图，在该信号中，EEG电极的拓扑结构是图形的。同时，引入了深入的残留学习，并引入了全注意结构，以解决有关原始EEG运动图像（MI）数据中更深层网络的退化问题。个人变异性是脑电图信号的重要且长期存在的挑战，已成功处理，以最先进的性能处理，在受试者水平上精确98.08％，20名受试者为94.28％。数值结果有望实现图形结构化拓扑的实现优于解码原始脑电图数据。预计这种创新的深度学习方法将需要一种通用神经科学研究和基于现实世界EEG的实践应用的通用方法，例如癫痫发作预测。
In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes. However, the latest neuroscience has suggested brain network connectivity. Thus, the exhibited interaction between EEG channels might not be appropriately measured via Euclidean distance. To fill the gap, an attention-based graph residual network, a novel structure of Graph Convolutional Neural Network (GCN), was presented to detect human motor intents from raw EEG signals, where the topological structure of EEG electrodes was built as a graph. Meanwhile, deep residual learning with a full-attention architecture was introduced to address the degradation problem concerning deeper networks in raw EEG motor imagery (MI) data. Individual variability, the critical and longstanding challenge underlying EEG signals, has been successfully handled with the state-of-the-art performance, 98.08% accuracy at the subject level, 94.28% for 20 subjects. Numerical results were promising that the implementation of the graph-structured topology was superior to decode raw EEG data. The innovative deep learning approach was expected to entail a universal method towards both neuroscience research and real-world EEG-based practical applications, e.g., seizure prediction.
