用几何与电极关系建模提升脑电超分辨率生成质量
Geometry- and Relation-Aware Diffusion for EEG Super-Resolution
- 引入拓扑感知嵌入和动态电极关系图，增强空间生成的生理合理性
- 在多个脑电数据集上生成质量显著提升，下游任务性能改善明显
- 适合需要高精度脑电重建的应用场景，如情绪识别与癫痫检测
近期脑电图（EEG）空间超分辨率方法虽通过直接预测缺失信号或适配基于潜在扩散的生成模型提升了质量，但普遍缺乏对生理空间结构的感知，限制了空间生成效果。为此，我们提出TopoDiff，一种几何与关系感知的扩散模型用于EEG空间超分辨率。受人类专家解读脑电空间模式的启发，TopoDiff利用源自脑电拓扑表示的拓扑感知图像嵌入，为空间生成提供全局几何上下文，同时构建随时间动态演化的电极间关系图，以编码电极间关联。该设计形成一个空间有根基的超分辨率框架，在涵盖情绪识别（SEED/SEED-IV）、运动想象（PhysioNet MI/MM）及癫痫检测（TUSZ）等多类应用的多个脑电数据集上，均实现生成保真度显著提升，并带来下游任务性能的明显改善。
原文摘要 · Abstract (English)
Recent electroencephalography (EEG) spatial super-resolution (SR) methods, while showing improved quality by either directly predicting missing signals from visible channels or adapting latent diffusion-based generative modeling to temporal data, often lack awareness of physiological spatial structure, thereby constraining spatial generation performance. To address this issue, we introduce TopoDiff, a geometry- and relation-aware diffusion model for EEG spatial super-resolution. Inspired by how human experts interpret spatial EEG patterns, TopoDiff incorporates topology-aware image embeddings derived from EEG topographic representations to provide global geometric context for spatial generation, together with a dynamic channel-relation graph that encodes inter-electrode relationships and evolves with temporal dynamics. This design yields a spatially grounded EEG spatial super-resolution framework with consistent performance improvements. Across multiple EEG datasets spanning diverse applications, including SEED/SEED-IV for emotion recognition, PhysioNet motor imagery (MI/MM), and TUSZ for seizure detection, our method achieves substantial gains in generation fidelity and leads to notable improvements in downstream EEG task performance.
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