BIGEM: BIDIRECTIONAL EEG–IMAGE GENERATIVE MODELING FOR PROBING VISUAL REPRESENTATIONAL GEOMETRY

Understanding the correspondence between visual stimuli and brain activity requires models that characterize the representations linking them. However, the mappings between these two modalities are typically modeled independently. We introduce BiGEM, a bidirectional generative framework that connects EEG-to-image decoding and image-to-EEG generation through a shared visual latent space. On THINGS-EEG2, BiGEM achieves 49.3% Top-1 accuracy in 200-way visual retrieval from EEG and a Pearson correlation of 0.456 between generated and measured mean EEG responses. Bidirectional analysis reveals that stimulus identity is preserved most strongly in the shared latent representation, while joint refinement improves reciprocal consistency without improving direct decoding. The learned EEG representation aligns most strongly with intermediate stages of the visual hierarchy, with early EEG responses associated with spatial phase, texture, and edge orientation. Controlled perturbations further dissociate two complementary properties of generated neural responses: perturbation severity determines response magnitude, whereas the identity of perturbation families remains recoverable from spatiotemporal response organization after severity control. This study provides a unified framework for modeling and studying the shared representations linking visual and neural signals.