{
  "id": 4921,
  "url": "https://arxiv.org/abs/2605.04680v1",
  "title": "Multi-Level Bidirectional Biomimetic Learning for EEG-Based Visual Decoding",
  "summary": "EEG-based visual neural decoding aims to align neural responses with visual stimuli for tasks such as image retrieval. However, limited paired data and a fundamental mismatch between high-fidelity digital images and biological visual perception - distorted by retinotopic mapping and subject-specific neuroanatomy - severely impede cross-modal alignment. To address this, we propose MB2L, a Multi-Level Bidirectional Biomimetic Learning framework that incorporates structured physiological inductive ",
  "authors": "Jingtao Liu, Peiliang Gong, Chuhang Zheng, Yiheng Liu, Qi Zhu",
  "category": "research",
  "topics": "safety-alignment,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-06T09:31:35.000Z",
  "fetched_at": "2026-07-14T16:31:21.934Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4921",
  "original_url": "https://arxiv.org/abs/2605.04680v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}