Visual neural decoding from EEG has improved significantly due to diffusion models that can reconstruct high-quality images from decoded latents. While recent works have focused on relatively complex architectures to achieve good reconstruction performance from EEG, less attention has been paid to the source of this information. We present a unified framework that not only enables image reconstruction from EEG using a simple linear decoder, but also isolates interpretable EEG feature maps that support visual perception. Unlike prior approaches that rely on deep, opaque models, our method leverages the inherent structure of CLIP embeddings to keep the mapping linear. We show that training a simple linear decoder from EEG to CLIP latent space, followed by a frozen pre-trained diffusion model, is sufficient to decode images with state-of-the-art reconstruction performance. Beyond reconstruction, Perceptogram enables the visualization of presumptive electrode preference and EEG patterns, revealing interpretable EEG feature maps that correspond to distinct visual attributes, such as semantic class, texture, and hue. We thus use our framework, Perceptogram, to probe EEG signals at various levels of the visual information hierarchy.

翻译：基于扩散模型能够从解码的潜在表示中重建高质量图像，从脑电图进行视觉神经解码已取得显著进展。尽管近期研究侧重于采用相对复杂的架构以实现从脑电图获得良好的重建性能，但对这些信息的来源关注较少。我们提出了一个统一框架，不仅能够使用简单的线性解码器从脑电图实现图像重建，还能分离出支持视觉感知的可解释脑电图特征图。与依赖深度不透明模型的先前方法不同，我们的方法利用CLIP嵌入的固有结构保持映射的线性特性。研究表明，训练一个从脑电图到CLIP潜在空间的简单线性解码器，再结合预训练的冻结扩散模型，足以实现具有先进重建性能的图像解码。除重建功能外，感知图能够可视化推测性电极偏好和脑电图模式，揭示与不同视觉属性（如语义类别、纹理和色调）对应的可解释脑电图特征图。因此，我们利用感知图框架在视觉信息层次结构的多个层面探究脑电图信号。