Aligning What EEG Can See: Structural Representations for Brain-Vision Matching

• URL: http://arxiv.org/abs/2603.07077v1
• Date: Sat, 07 Mar 2026 07:12:08 GMT
• Title: Aligning What EEG Can See: Structural Representations for Brain-Vision Matching
• Abstract summary: Visual decoding from electroencephalography (EEG) has emerged as a highly promising avenue for non-invasive brain-computer interfaces.<n>Existing EEG-based decoding methods predominantly align brain signals with the final-layer semantic embeddings of deep visual models.<n>We introduce the concept of Neural Visibility and accordingly propose the EEG-Visible Layer Selection Strategy.
• Score: 26.36030588128271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual decoding from electroencephalography (EEG) has emerged as a highly promising avenue for non-invasive brain-computer interfaces (BCIs). Existing EEG-based decoding methods predominantly align brain signals with the final-layer semantic embeddings of deep visual models. However, relying on these highly abstracted embeddings inevitably leads to severe cross-modal information mismatch. In this work, we introduce the concept of Neural Visibility and accordingly propose the EEG-Visible Layer Selection Strategy, aligning EEG signals with intermediate visual layers to minimize this mismatch. Furthermore, to accommodate the multi-stage nature of human visual processing, we propose a novel Hierarchically Complementary Fusion (HCF) framework that jointly integrates visual representations from different hierarchical levels. Extensive experiments demonstrate that our method achieves state-of-the-art performance, reaching an 84.6% accuracy (+21.4%) on zero-shot visual decoding on the THINGS-EEG dataset. Moreover, our method achieves up to a 129.8% performance gain across diverse EEG baselines, demonstrating its robust generalizability.

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