🤖 AI Summary
This study addresses the simultaneous neural decoding of three-dimensional object identity and spatial orientation from electroencephalography (EEG) signals to enable high-fidelity 3D reconstruction based on brain activity. Inspired by the ventral and dorsal pathways of the visual system, the authors propose a dual-stream brain decoding model that separately processes identity and orientation information. By integrating circular regression with an EEG-conditioned multi-view diffusion model, the framework achieves dynamic decoding under continuous rotational conditions. This work represents the first application of a biologically inspired dual-stream architecture to EEG-based decoding, revealing the temporal coordination among ventral, dorsal, and motor-related brain regions during 3D perception. It challenges the conventional assumption of static ventral dominance and substantially advances the performance of neural decoding for 3D visual representation.
📝 Abstract
This paper explores a novel brain decoding model for 3D shape perception through a dual pathway architecture mirroring biological vision. Our bio-inspired approach implements separate decoding modules for object identity and spatial orientation, inspired by ventral and dorsal pathways, during continuous rotations. We employ circular regression for angle prediction and develop EEG-conditioned multiview diffusion for 3D reconstruction. Our approach successfully decodes both object identity and spatial orientation from EEG signals and enables 3D reconstruction from neural activity, with interpretability analyses revealing temporally structured involvement of ventral, dorsal, and motor-related channels rather than a static ventral dominance in supporting object and angle decoding.