用双流图注意力与变换器融合提升脑电分类精度
Dual Stream Graph Transformer Fusion Networks for Enhanced Brain Decoding
- 双路设计：空间流用图注意力捕捉脑区连接，时序流用变换器学时间模式
- 跨被试测试中准确率提升，标准差降低，稳定性更强
- 适合脑机接口、神经科学等领域研究者参考
本文提出一种专为任务态脑磁图（MEG）数据分类设计的双流图-变换器融合架构（DS-GTF）。空间流将输入表示为图结构，通过图注意力网络（GAT）提取空间模式，采用TopK和阈值邻接两种方法初始化图邻接矩阵。时序流则接收拼接的时间窗内MEG数据，由变换器编码器学习新的时序表征。两路学习到的空间与时间表征在输出层前融合。实验表明，该模型在多个被试上的分类性能优于其他对比模型，且标准差显著降低。
原文摘要 · Abstract (English)
This paper presents the novel Dual Stream Graph-Transformer Fusion (DS-GTF) architecture designed specifically for classifying task-based Magnetoencephalography (MEG) data. In the spatial stream, inputs are initially represented as graphs, which are then passed through graph attention networks (GAT) to extract spatial patterns. Two methods, TopK and Thresholded Adjacency are introduced for initializing the adjacency matrix used in the GAT. In the temporal stream, the Transformer Encoder receives concatenated windowed input MEG data and learns new temporal representations. The learned temporal and spatial representations from both streams are fused before reaching the output layer. Experimental results demonstrate an enhancement in classification performance and a reduction in standard deviation across multiple test subjects compared to other examined models.
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