Electroencephalogram (EEG) technology, particularly high-density EEG (HD EEG) devices, is widely used in fields such as neuroscience. HD EEG devices improve the spatial resolution of EEG by placing more electrodes on the scalp, meeting the requirements of clinical diagnostic applications such as epilepsy focus localization. However, this technique faces challenges such as high acquisition costs and limited usage scenarios. In this paper, spatio-temporal adaptive diffusion models (STADMs) are proposed to pioneer the use of diffusion models for achieving spatial SR reconstruction from low-resolution (LR, 64 channels or fewer) EEG to high-resolution (HR, 256 channels) EEG. Specifically, a spatio-temporal condition module is designed to extract the spatio-temporal features of LR EEG, which then serve as conditional inputs to guide the reverse denoising process of diffusion models. Additionally, a multi-scale Transformer denoising module is constructed to leverage multi-scale convolution blocks and cross-attention-based diffusion Transformer blocks for conditional guidance to generate subject-adaptive SR EEG. Experimental results demonstrate that the proposed method effectively enhances the spatial resolution of LR EEG and quantitatively outperforms existing methods. Furthermore, STADMs demonstrate their value by applying synthetic SR EEG to classification and source localization tasks of epilepsy patients, indicating their potential to significantly improve the spatial resolution of LR EEG.

翻译：脑电图（EEG）技术，特别是高密度脑电图（HD EEG）设备，在神经科学等领域得到广泛应用。HD EEG设备通过在头皮上放置更多电极来提高EEG的空间分辨率，满足癫痫灶定位等临床诊断应用的需求。然而，该技术面临采集成本高、使用场景有限等挑战。本文提出时空自适应扩散模型（STADMs），首次将扩散模型用于实现从低分辨率（LR，64通道或更少）EEG到高分辨率（HR，256通道）EEG的空间超分辨率重建。具体而言，设计了一个时空条件模块来提取LR EEG的时空特征，随后将其作为条件输入以指导扩散模型的反向去噪过程。此外，构建了一个多尺度Transformer去噪模块，利用多尺度卷积块和基于交叉注意力的扩散Transformer块进行条件引导，以生成主体自适应的超分辨率EEG。实验结果表明，所提方法有效提升了LR EEG的空间分辨率，且在定量评估上优于现有方法。进一步地，STADMs通过将合成的超分辨率EEG应用于癫痫患者的分类与源定位任务，证明了其应用价值，表明其有潜力显著提升LR EEG的空间分辨率。