  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.
