EntiNeuro: Multi-Entity Visual Decoding and Video Reconstruction from EEG Signals

Natural visual scenes often contain multiple semantically distinct entities whose information must be represented simultaneously during perception. However, existing EEG-based visual decoding studies largely focus on a single dominant semantic target or overall scene content, and available EEG–video datasets do not explicitly control multi-entity compositions. Consequently, whether multiple entity semantics can be decoded from EEG and further support video reconstruction remains largely unexplored. To address this gap, we introduce EntiNeuro, a multi-entity visual decoding dataset with 624 dynamic videos across eight predefined cross-category compositions and synchronized EEG and eye-tracking recordings from 20 participants over three sessions. Based on EntiNeuro, we establish EntiBench for evaluating entity recognition and video reconstruction in multi-entity scenes. We further propose EntiRecon, a hierarchical EEG-based reconstruction framework that combines coarse entity semantics with fine-grained EEG conditioning using eye tracking as auxiliary attentional guidance. Experiments consistently show that multi-entity semantic information can be decoded from EEG. EntiRecon further improves semantic and multi-entity fidelity over existing EEG-to-video reconstruction methods. These results demonstrate the potential of EEG representations for reconstructing dynamic scenes containing multiple semantic entities.