[论文解读] GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals

Yimin Hou, Shuyue Jia+6 more|arXiv (Cornell University)|Jun 16, 2020

EEG and Brain-Computer Interfaces参考文献 55被引用 26

一句话总结

该论文提出GCNs-Net，一种图卷积神经网络，通过建模EEG电极之间的功能拓扑关系，以提升对时间分辨运动想象（MI）信号的解码性能。通过基于EEG信号皮尔逊相关系数矩阵构建图拉普拉斯矩阵，该框架增强了特征学习能力，在PhysioNet数据集上实现了93.06%（个体水平）和88.57%（群体水平）的最先进准确率，展现出对个体差异的强鲁棒性与高可重复性。

ABSTRACT

Towards developing effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by electroencephalogram (EEG), is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes is built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and group-wise predictions. It has achieved the highest averaged accuracy, 93.06% and 88.57% (PhysioNet Dataset), 96.24% and 80.89% (High Gamma Dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance is stably reproducible among repetitive experiments for cross-validation. The excellent performance of our method has shown that it is an important step towards better BCI approaches. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain motor imagery.

研究动机与目标

• 为解决传统EEG解码方法忽略电极之间功能拓扑关系的局限性。
• 提升基于EEG的脑机接口（BCI）中运动想象（MI）分类的准确率与鲁棒性。
• 开发一种深度学习框架，以捕捉网络级脑动态，实现对时间分辨EEG信号的更好表征。
• 在个性化与群体预测设置下评估该方法的性能。

提出的方法

• 该方法基于所有电极间EEG信号的绝对皮尔逊相关系数矩阵构建图拉普拉斯矩阵。
• 应用图卷积层，通过利用电极网络的功能连接结构，学习广义的时空特征。
• 池化层在保留判别性信息的同时降低特征维度。
• 全连接的Softmax层输出四分类运动想象任务的最终分类结果。
• 模型通过原始EEG信号端到端训练，无需手工特征工程。
• 通过10折交叉验证验证框架，并在两个公开数据集（PhysioNet和High Gamma）上进行测试。

实验结果

研究问题

• RQ1 与基于欧几里得结构的方法相比，建模EEG电极之间的功能拓扑关系是否能提升运动想象解码的准确率？
• RQ2 GCNs-Net在个性化与群体分类设置下的表现如何？
• RQ3 基于图的方法是否增强了对EEG数据中个体间与试验间差异的鲁棒性？
• RQ4 在准确率与稳定性方面，GCNs-Net与最先进深度学习模型相比表现如何？

主要发现

• 在PhysioNet数据集上，GCNs-Net在个体水平平均准确率达到93.06%，优于现有方法。
• 在群体水平，该模型在PhysioNet数据集上达到88.57%的平均准确率，其中20名受试者为89.39%，100名受试者为88.14%。
• 在High Gamma数据集上，该模型在个体水平平均准确率达到96.24%，群体水平为80.89%。
• 两脚运动想象任务的分类准确率最高，达到99.42%，表明其具备强大的判别能力。
• 统计检验显示，该方法在性能上显著优于卷积神经网络（CNN）模型（p < 0.05），但与表现最佳的图基模型相比无显著差异。
• 该方法在多次交叉验证运行中表现出稳定且可重复的结果，证实了其可靠性与鲁棒性。