In the field of affective computing (AC), coarse-grained AC has been developed and widely applied in many fields. Electroencephalogram (EEG) signals contain abundant emotional information. However, it is difficult to develop fine-grained AC due to the lack of fine-grained labeling data and suitable visualization methods for EEG data with fine labels. To achieve a fine mapping of EEG data directly to facial images, we propose a conditional generative adversarial network (cGAN) to establish the relationship between EEG data associated with emotions, a coarse label, and a facial expression image in this study. In addition, a corresponding training strategy is also proposed to realize the fine-grained estimation and visualization of EEG-based emotion. The experiments prove the reasonableness of the proposed method for the generation of fine-grained facial expressions. The image entropy of the generated image indicates that the proposed method can provide a satisfactory visualization of fine-grained facial expressions.

在情感计算（AC）领域，粗粒度AC已被开发并广泛应用于许多领域。脑电图（EEG）信号包含丰富的情绪信息。但是，由于缺乏细粒度的标记数据和带有细标记的EEG数据的合适可视化方法，因此很难开发细粒度的AC。为了实现将EEG数据直接直接映射到面部图像，我们提出了一个条件生成对抗网络（cGAN），以建立与情感相关的EEG数据，粗略标签和面部表情图像之间的关系。此外，还提出了一种相应的训练策略，以实现基于脑电图的情绪的细粒度估计和可视化。实验证明了所提方法用于生成细粒度面部表情的合理性。生成图像的图像熵表明，所提出的方法可以提供令人满意的细粒度面部表情可视化效果。