Img2EEG: A Scalable and Interpretable Encoding Framework for Simulating Human EEG Responses to Visual Inputs

Abstract

Understanding how visual information processing unfolds over time requires models that not only predict neural responses but also expose the representations that support them and generalize beyond sampled stimulus spaces. Here we introduce Img2EEG, a participant-specific image-to-EEG encoding framework that integrates hierarchical visual and semantic representations to generate temporally resolved multichannel EEG responses. Trained on THINGS EEG2, Img2EEG generalized to unseen images while preserving stimulus-specific and participant-specific response structure. Controlled perturbations of internal representations and visual inputs revealed distinct temporally structured contributions of visual and semantic information, and in silico experiments reproduced classic human neural responses, such as the face-sensitive N170, while enabling targeted representational interventions. Scaling Img2EEG to 1.28 million ImageNet images produced over 12 million synthetic EEG responses that supported cross-dataset visual reconstruction and improved the behavioral alignment of an artificial vision model. Img2EEG provides an interpretable and scalable framework for experimentally manipulable modeling of visual neural dynamics.