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，推动脑机接口（BCI）技术的发展。深度学习的最新进展推动了使用生成对抗网络（GAN）从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（EEG）数据集从脑活动合成图像的框架。该脑活动通过受试者头皮脑电图记录，受试者在想象特定类别的物体和英文字符时记录。我们提出的框架采用对比学习方法从脑电图信号提取特征，并利用条件GAN从提取的特征合成图像。我们修改了GAN的损失函数，使其能够仅使用少量图像合成128×128分辨率的图像。此外，我们通过消融研究和实验，展示了所提框架在使用小型脑电图数据集时相较于其他最先进方法的有效性。