LI-DSN：一种分层交互双流网络用于EEG解码
LI-DSN: A Layer-wise Interactive Dual-Stream Network for EEG Decoding
AI总结：
本文提出LI-DSN，通过分层交互机制解决双流网络中信息孤岛问题，引入TSIA机制和自适应融合策略，在八个EEG数据集上验证其优越的解码性能。
摘要：
电生理图（EEG）提供了一个非侵入性的窗口，用于观察大脑活动，为通过脑机接口（BCIs）理解并交互神经过程提供高时间分辨率。当前用于EEG的双流神经网络通常通过并行分支独立处理时间和空间特征，延迟其整合直到最终的后期融合。这种设计本质上导致了“信息孤岛”问题，阻碍了中间跨流细化和必要的空间-时间分解，以充分利用全部特征。我们提出了LI-DSN，一种分层交互双流网络，通过在每一层促进渐进的跨流通信，从而克服了后期融合范式的限制。LI-DSN引入了一种新的时间-空间整合注意力（TSIA）机制，该机制构建了一个空间亲和相关矩阵（SACM）以捕捉电极间的空间结构关系，并构建了一个时间通道聚合矩阵（TCAM）以在空间引导下整合余弦门控的时间动态。此外，我们采用了一种自适应融合策略，利用可学习的通道权重来优化双流特征的整合。在八个多样化的EEG数据集上进行了广泛的实验，涵盖运动想象（MI）分类、情绪识别和稳态视觉诱发电位（SSVEP），结果一致表明，LI-DSN显著优于13种最先进的（SOTA）基线模型，展示了其优越的鲁棒性和解码性能。代码将在接受后公开。
英文摘要：
Electroencephalography (EEG) provides a non-invasive window into brain activity, offering high temporal resolution crucial for understanding and interacting with neural processes through brain-computer interfaces (BCIs). Current dual-stream neural networks for EEG often process temporal and spatial features independently through parallel branches, delaying their integration until a final, late-stage fusion. This design inherently leads to an "information silo" problem, precluding intermediate cross-stream refinement and hindering spatial-temporal decompositions essential for full feature utilization. We propose LI-DSN, a layer-wise interactive dual-stream network that facilitates progressive, cross-stream communication at each layer, thereby overcoming the limitations of late-fusion paradigms. LI-DSN introduces a novel Temporal-Spatial Integration Attention (TSIA) mechanism, which constructs a Spatial Affinity Correlation Matrix (SACM) to capture inter-electrode spatial structural relationships and a Temporal Channel Aggregation Matrix (TCAM) to integrate cosine-gated temporal dynamics under spatial guidance. Furthermore, we employ an adaptive fusion strategy with learnable channel weights to optimize the integration of dual-stream features. Extensive experiments across eight diverse EEG datasets, encompassing motor imagery (MI) classification, emotion recognition, and steady-state visual evoked potentials (SSVEP), consistently demonstrate that LI-DSN significantly outperforms 13 state-of-the-art (SOTA) baseline models, showcasing its superior robustness and decoding performance. The code will be publicized after acceptance.
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