sgdm-eeg-visual-cognition

# Structure-Guided Diffusion Model for EEG-Based Visual Cognition > Structure-Guided Diffusion Model (SGDM) incorporating brain anatomical information to guide the reconstruction of visual stimuli from EEG signals, improving upon standard diffusion approaches for brain-computer interface applications. ## Metadata - **Source**: arXiv:2604.22649 - **Authors**: Yongxiang Lian, Yueyang Cang, Pingge Hu - **Published**: 2026-04-24 ## Core Methodology ### Key Innovation Decoding visual information from EEG is challenging due to: - Low spatial resolution of scalp recordings - Volume conduction blurring neural sources - Individual anatomical variations affecting signal propagation SGDM addresses this by leveraging brain structure to: 1. Guide the diffusion generation process with anatomical constraints 2. Incorporate individual cortical geometry via forward models 3. Condition image generation on structural priors 4. Improve reconstruction quality over standard latent diffusion ### Technical Framework 1. **Structural Encoder**: Brain anatomy to latent conditioning vectors 2. **EEG Feature Extractor**: Temporal-spatial feature extraction 3. **Guided Diffusion Process**: Structural conditioning at each denoising step 4. **Cross-Modal Fusion**: Integration of neural and anatomical information ## Implementation Guide ### Prerequisites - Diffusers library (HuggingFace) - PyTorch for deep learning - MNE-Python for EEG processing - Forward modeling (e.g., OpenMEEG, FieldTrip) ### Step-by-Step 1. Compute individual forward model: Anatomy to sensor projection 2. Train structural encoder: Cortical regions to conditioning space 3. Extract EEG features: Spatiotemporal patterns encoding visual information 4. Fine-tune diffusion model: With structural guidance mechanism 5. Generate reconstructions: Conditioned on both EEG and structure ### Code Example ```python import torch import torch.nn as nn from diffusers import DDPMScheduler, UNet2DConditionModel class StructureGuidedDiffusion(nn.Module): """Diffusion model guided by brain structure for EEG visual reconstruction""" def __init__(self, eeg_channels, n_cortical_regions, image_size=256): super().__init__() # EEG feature extractor self.eeg_encoder = nn.Sequential( nn.Conv1d(eeg_channels, 64, kernel_size=3, padding=1), nn.ReLU(), nn.AdaptiveAvgPool1d(64), nn.Flatten(), nn.Linear(64*64, 512) ) # Structural encoder self.structural_encoder = nn.Sequential( nn.Linear(n_cortical_regions, 256), nn.ReLU(), nn.Linear(256, 512) ) # Cross-modal fusion self.fusion = nn.MultiheadAttention(embed_dim=512, num_heads=8) # Conditional diffusion UNet self.unet = UNet2DConditionModel( sample_size=image_size, in_channels=3, out_channels=3, cross_attention_dim=512 ) def forward(self, noisy_image, timestep, eeg, structural_prior): # Encode EEG and structural information eeg_features = self.eeg_encoder(eeg) struct_features = self.structural_encoder(structural_prior) # Cross-modal fusion combined = torch.stack([eeg_features, struct_features], dim=0) fused, _ = self.fusion(combined, combined, combined) conditioning = fused.mean(dim=0) # Structure-guided denoising noise_pred = self.unet(noisy_image, timestep, conditioning) return noise_pred def generate_reconstruction(model, eeg_data, structural_prior, num_steps=50): """Generate visual reconstruction from EEG with structural guidance""" scheduler = DDPMScheduler(num_train_timesteps=1000) scheduler.set_timesteps(num_steps) # Start from random noise image = torch.randn(1, 3, 256, 256) for t in scheduler.timesteps: noise_pred = model(image, t, eeg_data, structural_prior) image = scheduler.step(noise_pred, t, image).prev_sample return image ``` ## Applications - Visual BCI: Thought-to-image interfaces - Dream reconstruction: Decoding visual imagery from EEG - Perceptual decoding: Understanding visual processing - Clinical assessment: Quantifying visual perception deficits ## Pitfalls - Requires individual MRI for optimal structural guidance - High computational cost for diffusion sampling - Limited by EEG spatial resolution even with structural priors ## Related Skills - eeg-structure-guided-diffusion - eeg-3d-visual-decoding - brain-inspired-capture-evidence-driven