# Complementarity-Driven Dual-Teacher Hierarchical Alignment for Zero-Shot EEG-to-Image Retrieval

**Authors:** Lukun Wu, Jie Li, Ziqi Ren, Ruiyang Xia, Yan Gao, Xinbo Gao  
**Published:** 9 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.12798) · [PDF](https://arxiv.org/pdf/2611.12798) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.12798.html)

## Key numbers

- **92.4%**: DTHA achieves 92.4% Top-1 accuracy
- **12.4**: exceeding prior state of the art by 12.4 percentage points

## Abstract

Decoding visual information from EEG is central to brain--computer interfaces and brain-inspired intelligence, and contrastive alignment with pretrained vision foundation models (VFMs) has become the dominant paradigm. However, existing methods align EEG solely with CLIP-family VFMs, leaving hierarchical visual information in neural responses under-exploited. To address this gap, we formulate a mechanism-paradigm correspondence view linking hierarchical visual processing (from appearance to semantics) to the complementary biases of cross-paradigm VFMs, with image-only models (e.g., DINO) and image--text models (e.g., CLIP) capturing distinct facets of this hierarchy. Guided by this view, we propose the Dual-Teacher Hierarchical Alignment (DTHA) framework. Concretely, a shared EEG encoder is supervised by frozen multi-blur CLIP and shallow DINOv2 teachers via dual-branch contrastive alignment, so each branch fully supervises the visual cues it emphasizes. A fused-retrieval objective learns adaptive weights to combine their complementary visual representations for zero-shot EEG-to-image retrieval. For the shared EEG encoder, we introduce the Attentive Modulated Scalp-Pattern Bank Encoder (AMSPBE), combining temporo-spatial attention with a learnable scalp-pattern bank for accurate, interpretable visual feature extraction, while modeling regional temporal dynamics and filter-bank topographies. On THINGS-EEG zero-shot retrieval, DTHA achieves 92.4% Top-1 accuracy, exceeding prior state of the art by 12.4 percentage points. Spatial, spectral, and representational-similarity analyses, together with THINGS-MEG evaluation, validate DTHA's effectiveness, biological plausibility, and cross-modality generalization. This complementarity-driven perspective shows that supervision from cross-paradigm VFMs enables more complete extraction of hierarchical visual information from brain activity.

---

Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.