{
  "id": 970,
  "url": "https://arxiv.org/abs/2606.20696v1",
  "title": "MindAlign: Decoding Inner Speech from fMRI Signals via Multimodal Embedding Alignment under Limited Data",
  "summary": "Decoding inner speech from non-invasive brain signals remains a fundamental challenge due to the absence of overt linguistic output, limited training data, and large inter-subject variability. Existing brain-to-text approaches often rely on task-specific decoder fine-tuning, which restricts scalability and complicates adaptation to new participants. We propose MindAlign, a decoupled two-stage brain-to-language framework that enables open-ended text generation from fMRI signals without modifying ",
  "authors": "Muxuan Liu, Ichiro Kobayashi, Satoshi Nishida",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-15T08:30:03.000Z",
  "fetched_at": "2026-07-14T14:14:54.534Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/970",
  "original_url": "https://arxiv.org/abs/2606.20696v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}