Reconstructing visual stimulus images is a significant task in neural
decoding, and up to now, most studies consider the functional magnetic
resonance imaging (fMRI) as the signal source. However, the fMRI-based image
reconstruction methods are difficult to widely applied because of the
complexity and high cost of the acquisition equipments. Considering the
advantages of low cost and easy portability of the electroencephalogram (EEG)
acquisition equipments, we propose a novel image reconstruction method based on
EEG signals in this paper. Firstly, to satisfy the high recognizability of
visual stimulus images in fast switching manner, we build a visual stimuli
image dataset, and obtain the EEG dataset by a corresponding EEG signals
collection experiment. Secondly, the deep visual representation model(DVRM)
consisting of a primary encoder and a subordinate decoder is proposed to
reconstruct visual stimuli. The encoder is designed based on the
residual-in-residual dense blocks to learn the distribution characteristics
between EEG signals and visual stimulus images, while the decoder is designed
based on the deep neural network to reconstruct the visual stimulus image from
the learned deep visual representation. The DVRM can fit the deep and multiview
visual features of human natural state and make the reconstructed images more
precise. Finally, we evaluate the DVRM in the quality of the generated images
on our EEG dataset. The results show that the DVRM have good performance in the
task of learning deep visual representation from EEG signals and generating
reconstructed images that are realistic and highly resemble the original
images.

本文提出了一种基于脑电图 (EEG) 信号的图像重建方法，利用低成本、易携带的 EEG 设备构建了视觉刺激图像数据集，并建立了深度视觉表示模型 (DVRM) 来从 EEG 信号学习视觉刺激图像的分布特征，并通过深度神经网络在人类自然状态下还原高度逼真的图像。实验证明，DVRM 在学习 EEG 信号生成深度视觉表示和生成逼真的重建图像任务中具有出色的性能。