Reconstructing images using brain signals of imagined visuals may provide an augmented vision to the disabled, leading to the advancement of Brain-Computer Interface (BCI) technology. The recent progress in deep learning has boosted the study area of synthesizing images from brain signals using Generative Adversarial Networks (GAN). In this work, we have proposed a framework for synthesizing the images from the brain activity recorded by an electroencephalogram (EEG) using small-size EEG datasets. This brain activity is recorded from the subject's head scalp using EEG when they ask to visualize certain classes of Objects and English characters. We use a contrastive learning method in the proposed framework to extract features from EEG signals and synthesize the images from extracted features using conditional GAN. We modify the loss function to train the GAN, which enables it to synthesize 128x128 images using a small number of images. Further, we conduct ablation studies and experiments to show the effectiveness of our proposed framework over other state-of-the-art methods using the small EEG dataset.

翻译：利用想象视觉的脑信号重建图像可为残障人士提供增强视觉，进而推动脑机接口技术的发展。近年来深度学习的发展借助生成对抗网络推动了基于脑信号合成图像的研究领域。本文提出了一种利用小型脑电图数据集从脑电信号合成图像的框架——该脑电信号记录自受试者头部 scalp，受试者在实验中需想象特定类别的物体与英文字符。该框架通过对比学习方法从脑电信号中提取特征，并利用条件生成对抗网络从提取的特征合成图像。通过改进生成对抗网络的损失函数，使其能仅在少量图像数据支撑下合成128×128分辨率图像。此外，我们通过消融实验与对比实验证明了所提框架在小型脑电图数据集上相较于其他先进方法的有效性。