Tri-Stream Disentangled Network with Adaptive Dual-Space Fusion for Electroencephalography-Based Image Reconstruction on THINGS-EEG2.

Reconstructing visual stimuli from non-invasive electroencephalography (EEG) requires mapping short, noisy neural responses to meaningful visual representations despite limited spatial sampling. Existing EEG-to-image approaches employ diverse neural representations and generative strategies, necessitating reproducible workflows that disentangle structural and semantic contributions. This protocol presents the Tri-stream Disentangled Network (TDE-Net), a computational framework for EEG-based image reconstruction. The protocol uses preprocessed THINGS-EEG2 recordings and organizes EEG information into a raw EEG anchor, a sample-specific functional graph stream, and a multi-scale temporal semantic stream. The graph stream captures frequency-domain relationships among EEG channels via graph convolution and cross-trial contrastive learning, while the semantic stream extracts task-relevant features via adaptive spatial weighting and multi-scale temporal convolutions. Structural and semantic features are mapped separately to variational autoencoder (VAE) and Contrastive Language-Image Pre-training (CLIP) latent spaces, combined using development-set-selected fusion weights, and decoded using Stable UnCLIP. Across 10 participants, TDE-Net achieved a mean CLIP pairwise identification accuracy of 0.8135, with complementary metrics demonstrating structural and feature-space correspondence. Ablation and representation-level analyses further characterized the contributions of individual streams. This protocol provides a reproducible framework for investigating complementary EEG representations and diffusion-based visual reconstruction.