Motor Imagery (MI) Electroencephalography (EEG) signals contain two crucial and complementary types of information: state information, which captures the global context of the task, and flow information, which captures fine-grained temporal dynamics. However, existing deep decoding models typically focus on only one of these information streams, resulting in unstable learning and sub-optimal performance. To address this, we propose the State-Flow Coordinated Network (StaFlowNet), a novel architecture that explicitly separates and coordinates state and flow information. We first employ a dual-branch design to extract the global state vector and temporal flow features separately. Critically, a novel state-modulated flow module is proposed to dynamically refine the learning of flow information. This modulated mechanism effectively integrates global context with fine-grained dynamics, thereby significantly enhancing task discriminability and decoding performance. Experiments on three public MI-EEG datasets demonstrate that StaFlowNet significantly outperforms state-of-the-art methods. Ablation studies further confirm that the state-modulated mechanism plays a crucial role in enhancing feature discriminability and overall performance.

翻译：运动想象（Motor Imagery, MI）脑电图（Electroencephalography, EEG）信号包含两类关键且互补的信息：状态信息（捕捉任务的全局上下文）和流信息（捕捉细粒度的时间动态）。然而，现有的深度解码模型通常仅关注其中一类信息流，导致学习不稳定且性能欠优。为此，我们提出了状态流协同网络（StaFlowNet），这是一种明确分离并协同状态信息与流信息的新型架构。首先采用双分支设计分别提取全局状态向量和时间流特征。关键在于，我们提出了新颖的状态调制流模块，可动态优化流信息的学习过程。该调制机制有效整合了全局上下文与细粒度动态，显著提升了任务判别力与解码性能。在三个公开MI-EEG数据集上的实验表明，StaFlowNet显著优于现有最先进方法。消融研究进一步证实，状态调制机制在增强特征判别性与整体性能中发挥了关键作用。