Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning
Journal:
arXiv
Published Date:
Jul 3, 2024
Abstract
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, which
meet 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 (STAD) 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 are then used as conditional inputs to direct the
reverse denoising process. 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
STAD significantly enhances the spatial resolution of LR EEG and quantitatively
outperforms existing methods. Furthermore, STAD demonstrate their value by
applying synthetic SR EEG to classification and source localization tasks,
indicating their potential to substantially boost the spatial resolution of
EEG.
