Perceptogram: Reconstructing Visual Percepts and Presumptive Electrode Preference from EEG

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.