In this study, we tackle a modern research challenge within the field of perceptual brain decoding, which revolves around synthesizing images from EEG signals using an adversarial deep learning framework. The specific objective is to recreate images belonging to various object categories by leveraging EEG recordings obtained while subjects view those images. To achieve this, we employ a Transformer-encoder based EEG encoder to produce EEG encodings, which serve as inputs to the generator component of the GAN network. Alongside the adversarial loss, we also incorporate perceptual loss to enhance the quality of the generated images.

翻译：在本研究中，我们针对感知脑解码领域中的一项现代研究挑战展开探讨，即利用对抗深度学习框架从EEG信号中合成图像。具体目标是通过受试者观看不同类别图像时采集的EEG记录，重建这些图像。为实现此目标，我们采用基于Transformer编码器的EEG编码器生成EEG编码特征，并将其作为GAN网络生成器模块的输入。除对抗损失外，我们还引入感知损失以提升生成图像的质量。