Seeing is believing, however, the underlying mechanism of how human visual
perceptions are intertwined with our cognitions is still a mystery. Thanks to
the recent advances in both neuroscience and artificial intelligence, we have
been able to record the visually evoked brain activities and mimic the visual
perception ability through computational approaches. In this paper, we pay
attention to visual stimuli reconstruction by reconstructing the observed
images based on portably accessible brain signals, i.e., electroencephalography
(EEG) data. Since EEG signals are dynamic in the time-series format and are
notorious to be noisy, processing and extracting useful information requires
more dedicated efforts; In this paper, we propose a comprehensive pipeline,
named NeuroImagen, for reconstructing visual stimuli images from EEG signals.
Specifically, we incorporate a novel multi-level perceptual information
decoding to draw multi-grained outputs from the given EEG data. A latent
diffusion model will then leverage the extracted information to reconstruct the
high-resolution visual stimuli images. The experimental results have
illustrated the effectiveness of image reconstruction and superior quantitative
performance of our proposed method.

本文介绍了一种名为 NeuroImagen 的综合流程，用于从动态的脑电信号（EEG 信号）中重构高分辨率的视觉刺激图像，并通过多层次的感知信息解码提取实用信息，实验结果表明该方法在图像重构方面具有显著的效果和卓越的定量性能。