Abstract
Brain-Computer Interfaces (BCIs) rely on accurately decoding electroencephalography (EEG) motor imagery (MI) signals for effective device control. Graph Neural Networks (GNNs) outperform Convolutional Neural Networks (CNNs) in this regard, by leveraging the spatial relationships between EEG electrodes through adjacency matrices. The EEG_GLT-Net framework, featuring the state-of-the-art EEG_GLT adjacency matrix method, has notably enhanced EEG MI signal classification, evidenced by an average accuracy of 83.95% across 20 subjects on the PhysioNet dataset. This significantly exceeds the 76.10% accuracy rate achieved using the Pearson Correlation Coefficient (PCC) method within the same framework. In this research, we advance the field by applying a Reinforcement Learning (RL) approach to the classification of EEG MI signals. Our innovative method empowers the RL agent, enabling not only the classification of EEG MI data points with higher accuracy, but effective identification of EEG MI data points that are less distinct. We present the EEG_RL-Net, an enhancement of the EEG_GLT-Net framework, which incorporates the trained EEG GCN Block from EEG_GLT-Net at an adjacency matrix density of 13.39% alongside the RL-centric Dueling Deep Q Network (Dueling DQN) block. The EEG_RL-Net model showcases exceptional classification performance, achieving an unprecedented average accuracy of 96.40% across 20 subjects within 25 milliseconds. This model illustrates the transformative effect of the RL in EEG MI time point classification.
Abstract (translated)
脑-计算机界面（BCIs）依赖于准确解码脑电图（EEG）运动想象（MI）信号来实现有效的设备控制。图神经网络（GNNs）在这方面表现出比卷积神经网络（CNNs）更好的性能，通过利用EEG电极之间的邻接矩阵关系。特别是，基于最先进的EEG_GLT邻接矩阵方法的EEG_GLT-Net框架在 PhysioNet 数据集上的平均准确率达到了83.95%，远高于使用皮尔逊相关系数（PCC）方法达到的76.10%。在这项研究中，我们通过应用强化学习（RL）方法来对EEG MI信号进行分类。我们创新的方法使RL代理不仅能够高精度地分类EEG MI数据点，而且能够有效识别那些不太明显的EEG MI数据点。我们提出了EEG_RL-Net，是EEG_GLT-Net框架的增强版，在距离为13.39%的邻接矩阵密度下加入了训练后的EEG GCN块，并添加了以RL为中心的双对深深Q网络（Dueling DQN）块。EEG_RL-Net模型展示了出色的分类性能，在20个受试者上的平均准确率达到了96.40%，在25毫秒内实现了前所未有的平均准确率。这个模型突出了RL在EEG MI时间点分类中的 transformative 作用。
URL
https://arxiv.org/abs/2405.00723
