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:利用想象视觉的大脑信号对图像进行再构造,可以增强残疾人的视力,从而推进脑-计算机界面技术。最近深层学习的进展推动了利用基因反影网络(GAN)对脑信号图像进行合成的研究领域。在这项工作中,我们提出了一个框架,用于利用小型电子脑图(EEEG)对脑活动记录的照片进行合成。这种脑活动在要求将某些对象和英语字符类别进行视觉化时,通过EEEG从主体头顶上记录。我们在拟议框架中使用对比学习方法从EEG信号中提取特征,并利用有条件GAN对提取的特征图像进行合成。我们修改损失功能,以培训GAN,使其能够利用少量图像合成128x128图像。此外,我们进行模拟研究和实验,以显示我们提议的框架相对于使用小型EEG数据集的其他状态方法的有效性。