STAMBRIDGE: Spectral-Temporal Amplitude-aware Mid-Feature Bridge for EEG Visual Decoding

arXiv:2605.23137 · eess.IV, cs.CV · Submitted 2026-05-22 · Read on arXiv

eess.IV, cs.CV

Submitted: 2026-05-22

Updated: 2026-09-23

Code: https://github.com/thabeatmjh/STAMBRIDGE

Terminology

• Alignment — 41× in this paper · explained in 1 episode
• Accuracy — 17× in this paper · explained in 1 episode
• Distillation — 15× in this paper · explained in 2 episodes
• Embeddings — 13× in this paper · explained in 2 episodes
• Diffusion Model — 8× in this paper · explained in 8 episodes
• Mid-Feature Semantic Bridge — 8× in this paper
• Representation Learning — 8× in this paper · explained in 2 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Modality Gap — 6× in this paper · explained in 4 episodes
• Latent Space — 5× in this paper · explained in 7 episodes
• Model-Agnostic — 5× in this paper · explained in 2 episodes
• Semantic Alignment — 5× in this paper · explained in 3 episodes
• Spectral-Temporal Amplitude-aware Modulation — 5× in this paper
• Ablation — 4× in this paper · explained in 1 episode
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Latent Diffusion Model — 4× in this paper · explained in 2 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Attention Mechanism — 3× in this paper · explained in 2 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Latent Diffusion Models — 3× in this paper · explained in 2 episodes
• RAG — 3× in this paper · explained in 3 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Multi-modal Adaptive Directional Routing — 2× in this paper
• Rapid Serial Visual Presentation — 2× in this paper
• Retrieval-Augmented Generation — 2× in this paper
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Two-Stage Framework — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Contrastive Language-Image Pre-training — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Edge Deployment — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Predictive Coding — 1× in this paper · explained in 2 episodes
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Signal-to-noise ratio (SNR) — 1× in this paper · explained in 2 episodes
• Structural Integrity — 1× in this paper · explained in 3 episodes
• Time Series Forecasting — 1× in this paper · explained in 3 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Mitigate the Gap: Investigating Approaches for Improving Cross-Modal Alignment in CLIP
• Representation Learning with Contrastive Predictive Coding
• IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

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