融合注意力的滤波器组双视图图卷积运动想象脑电分类

Attention-fused filter bank dual-view graph convolution motor imagery EEG classification

在运动想象任务中,传统卷积神经网络难以准确表达大脑多区域协同神经活动;图卷积网络(GCN)能够在图数据中考虑节点(脑区)间的连接和关系,适于表示不同脑区的协同任务,为此提出融合注意力的滤波器组双视图GCN(AFB-DVGCN).由滤波器组构建双分支网络,提取不同频段的时域和空域信息;采用双视图图卷积空间特征提取方法实现信息互补;利用有效通道注意力机制增强特征和捕捉不同特征图的交互信息,以提高分类准确率.在公开数据集BCI Competition Ⅳ-2a和OpenBMI上的验证结果表明,AFB-DVGCN的分类性能良好,其分类准确率显著高于对比网络的分类准确率.

In motor imagery tasks,the brain often involves simultaneous activation of multiple regions,and traditional convolutional neural networks struggle to accurately represent the coordinated neural activity across these regions.Graph convolutional network GCN is suitable for representing the collaborative tasks of different brain regions by considering the connections and relationships between nodes(brain regions)in graph data.Attentionfused filter bank dual-view GCN(AFB-DVGCN)was proposed.A dual-branch network was constructed using filter banks to extract temporal and spatial information from different frequency bands.Information complementarity was achieved by a convolutional spatial feature extraction method for dual-view graphs.In order to improve the classification accuracy,the effective channel attention mechanism was utilized to enhance features and capture the interaction information between different feature maps.Validation results in the publicly available datasets BCI Competition IV-2a and OpenBMI show that AFB-DVGCN has achieved good classification performance,and the classification accuracy is significantly higher than that of the comparison networks.