EEG_GLT-Net: Optimising EEG Graphs for Real-time Motor Imagery Signals Classification

📄 arXiv: 2404.11075v1 📥 PDF

作者: Htoo Wai Aung, Jiao Jiao Li, Yang An, Steven W. Su

分类: cs.LG, cs.AI, eess.SP

发布日期: 2024-04-17

💡 一句话要点

提出EEG_GLT算法以优化脑电图信号分类

🎯 匹配领域: 支柱七：动作重定向 (Motion Retargeting)

关键词: 脑电图 图神经网络 运动想象 邻接矩阵 脑机接口 实时分类 机器学习

📋 核心要点

  1. 现有的EEG信号分类方法在准确性和计算效率上存在不足，尤其是在实时应用中。
  2. 本文提出的EEG_GLT算法通过创新的邻接矩阵构建方法，优化了EEG通道间的关系建模。
  3. 实验结果显示，EEG_GLT在平均准确率上超越PCC方法13.39%，并显著降低了计算复杂度。

📝 摘要（中文）

脑机接口需要将脑电图（EEG）信号准确转化为可执行命令。图神经网络（GCN）因其能够捕捉EEG通道间的空间关系而在EEG运动想象信号分类中表现出色。本文提出的EEG图彩票（EEG_GLT）算法创新性地构建了EEG通道的邻接矩阵，无需预先了解通道间关系，且可针对个体和GCN模型架构进行定制。研究表明，EEG_GLT矩阵在平均准确率上超越了传统的皮尔逊相关系数（PCC）方法13.39%。此外，EEG_GLT方法在保持或提升准确率的同时，计算复杂度降低了97%。

🔬 方法详解

问题定义：本文旨在解决现有EEG信号分类方法在准确性和计算效率上的不足，尤其是在实时应用场景中，传统方法难以满足高效性要求。

核心思路：EEG_GLT算法通过创新性地构建邻接矩阵，避免了对通道间关系的预先假设，使得算法能够针对不同个体和模型架构进行优化。

技术框架：该方法主要包括邻接矩阵的构建、图神经网络的训练和分类过程。通过对EEG信号的图结构建模，提升了分类性能。

关键创新：EEG_GLT算法的最大创新在于其邻接矩阵的构建方式，显著优于传统的PCC方法，能够自适应不同的EEG信号特征。

关键设计：在设计中，采用了基本的GCN配置，并通过调整邻接矩阵的构建策略，优化了模型的训练过程，确保了高效的计算和准确的分类。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，EEG_GLT算法在平均准确率上超越传统PCC方法13.39%，并在计算复杂度上降低了97%。即使在基本的GCN配置下，EEG_GLT矩阵的表现也优于复杂的GCN设置，显示出其卓越的性能。

🎯 应用场景

该研究在脑机接口、医疗监测和人机交互等领域具有广泛的应用潜力。EEG_GLT算法的高效性和准确性使其适合实时脑电图信号分类，能够为相关技术的发展提供支持，推动智能医疗和辅助技术的进步。

📄 摘要（原文）

Brain-Computer Interfaces connect the brain to external control devices, necessitating the accurate translation of brain signals such as from electroencephalography (EEG) into executable commands. Graph Neural Networks (GCN) have been increasingly applied for classifying EEG Motor Imagery signals, primarily because they incorporates the spatial relationships among EEG channels, resulting in improved accuracy over traditional convolutional methods. Recent advances by GCNs-Net in real-time EEG MI signal classification utilised Pearson Coefficient Correlation (PCC) for constructing adjacency matrices, yielding significant results on the PhysioNet dataset. Our paper introduces the EEG Graph Lottery Ticket (EEG_GLT) algorithm, an innovative technique for constructing adjacency matrices for EEG channels. It does not require pre-existing knowledge of inter-channel relationships, and it can be tailored to suit both individual subjects and GCN model architectures. Our findings demonstrated that the PCC method outperformed the Geodesic approach by 9.65% in mean accuracy, while our EEG_GLT matrix consistently exceeded the performance of the PCC method by a mean accuracy of 13.39%. Also, we found that the construction of the adjacency matrix significantly influenced accuracy, to a greater extent than GCN model configurations. A basic GCN configuration utilising our EEG_GLT matrix exceeded the performance of even the most complex GCN setup with a PCC matrix in average accuracy. Our EEG_GLT method also reduced MACs by up to 97% compared to the PCC method, while maintaining or enhancing accuracy. In conclusion, the EEG_GLT algorithm marks a breakthrough in the development of optimal adjacency matrices, effectively boosting both computational accuracy and efficiency, making it well-suited for real-time classification of EEG MI signals that demand intensive computational resources.