IEEE Trans Neural Netw Learn Syst. 2024 Jun;35(6):7312-7323. doi: 10.1109/TNNLS.2022.3202569. Epub 2024 Jun 3.
Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an 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 (DL) 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 (FC) softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and groupwise 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 toward 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 MI. A DL library for EEG task classification including the code for this study is open source at https://github.com/SuperBruceJia/ EEG-DL for scientific research.
为了开发有效和高效的脑机接口（BCI）系统，非常需要精确解码脑电图（EEG）测量的脑活动。传统的工作在不考虑电极之间拓扑关系的情况下对 EEG 信号进行分类。然而，神经科学研究越来越强调大脑动力学的网络模式。因此，电极的欧几里得结构可能不能充分反映信号之间的相互作用。为了弥补这一差距，提出了一种基于图卷积神经网络（GCN）的新型深度学习（DL）框架，以增强不同类型运动想象（MI）任务中原始 EEG 信号的解码性能，同时与电极的功能拓扑关系相配合。基于整体信号的绝对 Pearson 矩阵，构建 EEG 电极的图拉普拉斯。由图卷积层构建的 GCNs-Net 学习广义特征。随后的池化层降低维度，全连接（FC）softmax 层得出最终预测。所提出的方法已经证明对于个性化和群组预测都是收敛的。与现有研究相比，它在个体和群组水平上分别实现了最高的平均准确率，93.06%和 88.57%（PhysioNet 数据集），96.24%和 80.89%（高伽马数据集），这表明它对个体变异性具有适应性和稳健性。此外，在交叉验证的重复实验中，性能稳定可重现。我们的方法的优异性能表明，它是迈向更好的 BCI 方法的重要一步。总之，GCNs-Net 基于功能拓扑关系对 EEG 信号进行滤波，成功解码了与脑 MI 相关的特征。一个包含本研究代码的 EEG 任务分类的 DL 库在 https://github.com/SuperBruceJia/EEG-DL 上开源，供科学研究使用。
