通过稀疏注意力融合多尺度脑电特征，提升解码准确率
Fusion of Multiscale Features Via Centralized Sparse-attention Network for EEG Decoding
- 设计主辅分支结构，主支用多尺度自注意力捕捉核心模式
- 辅支用稀疏交叉注意力实现高效局部交互，提升特征融合效果
- 在5个公开数据集上达领先性能，适合脑机接口研究者使用
脑电图（EEG）信号解码是将大脑活动转化为可执行指令的关键技术，为直接脑机接口和智能交互奠定基础。针对EEG信号固有的时空异质性，本文提出一种多分支并行架构，每个时间尺度配备独立的空间特征提取模块。为进一步增强多分支特征融合，提出中心化稀疏注意力网络（EEG-CSANet），采用主-辅分支结构：主分支通过多尺度自注意力建模核心时空模式，辅分支则通过稀疏交叉注意力实现高效局部交互。实验结果表明，EEG-CSANet在五个公开数据集（BCIC-IV-2A、BCIC-IV-2B、HGD、SEED、SEED-VIG）上均达到当前最优性能，准确率分别为88.54%、91.09%、97.15%、96.03%和90.56%，展现出强大的跨任务适应性与鲁棒性。此外，通过大量消融实验提升了模型可解释性。未来期望该模型能成为EEG信号解码领域的有力基线。源代码已公开于：https://github.com/Xiangrui-Cai/EEG-CSANet
原文摘要 · Abstract (English)
Electroencephalography (EEG) signal decoding is a key technology that translates brain activity into executable commands, laying the foundation for direct brain-machine interfacing and intelligent interaction. To address the inherent spatiotemporal heterogeneity of EEG signals, this paper proposes a multi-branch parallel architecture, where each temporal scale is equipped with an independent spatial feature extraction module. To further enhance multi-branch feature fusion, we propose a Fusion of Multiscale Features via Centralized Sparse-attention Network (EEG-CSANet), a centralized sparse-attention network. It employs a main-auxiliary branch architecture, where the main branch models core spatiotemporal patterns via multiscale self-attention, and the auxiliary branch facilitates efficient local interactions through sparse cross-attention. Experimental results show that EEG-CSANet achieves state-of-the-art (SOTA) performance across five public datasets (BCIC-IV-2A, BCIC-IV-2B, HGD, SEED, and SEED-VIG), with accuracies of 88.54%, 91.09%, 97.15%, 96.03%, and 90.56%, respectively. Such performance demonstrates its strong adaptability and robustness across various EEG decoding tasks. Moreover, extensive ablation studies are conducted to enhance the interpretability of EEG-CSANet. In the future, we hope that EEG-CSANet could serve as a promising baseline model in the field of EEG signal decoding. The source code is publicly available at: https://github.com/Xiangrui-Cai/EEG-CSANet
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