Uni-Neur2Img: Unified Neural Signal-Guided Image Generation, Editing, and Stylization via Diffusion Transformers

Non-invasive Brain–Computer Interfaces (BCIs) provide a viable pathway for decoding visual perception, yet EEG-to-image translation remains constrained by low signal-to-noise ratio, temporal non-stationarity, and substantial trial- and subject-level variability. Treating EEG as a clean text-like prompt can expose the diffusion backbone to both signal and noise, motivating explicit control over neural conditioning. We propose Uni-Neur2Img, a unified Diffusion Transformer architecture and conditioning interface for generation, editing, and stylization, with separately fine-tuned task-specific weights. A dual-pathway Neural Adapter maps EEG to pooled and token conditioning spaces without discrete EEG-to-text decoding. The Neural Signal Injection Module (NSIM) combines adaptive LoRA, using EEG- and timestep-dependent gates and a rank schedule, with quality-aware cross-modal attention. A separate subject-adversarial objective regularizes EEG representations. A frozen T5/CLIP text teacher provides semantic supervision only in EEG+Text training and is omitted in EEG-only training and inference. We introduce EEG-Style, comprising 20,000 paired trials from five participants viewing 4,000 images across 1,000 styles, with a style-disjoint split. Experiments evaluate generation on EEGCVPR40, editing on LoongX, and stylization on EEG-Style. Results are reported as means over three random seeds. In pooled stylization experiments involving five participants, EEG-only conditioning outperforms text-only conditioning on five of seven metrics with the tested prompts, reducing Style Loss by 68.75% and Color Loss by 14.86%. Joint EEG+Text conditioning surpasses both individual modalities on six of seven metrics, with DINO as the exception. These findings support visually evoked EEG as style guidance and show complementary gains from EEG and text under the evaluated conditions.