Perceptogram: Reconstructing Visual Percepts 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. In this work, we attempt to discover EEG features that represent perceptual and semantic visual categories, using a simple pipeline. Notably, the high temporal resolution of EEG allows us to go beyond static semantic maps as obtained from fMRI. We show (a) 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. (b) Mapping the decoded latents back to EEG using a linear encoder isolates CLIP-relevant EEG spatiotemporal features. (c) By using other latent spaces representing lower-level image features, we obtain similar time-courses of texture/hue-related information. We thus use our framework, Perceptogram, to probe EEG signals at various levels of the visual information hierarchy.