{
  "id": 7626159,
  "title": "FUSE: FUsing EEG-MEG in a Shared Embedding via self-supervised learning for BCI",
  "url": "https://urgent.news/2026/09/15/fuse-fusing-eeg-meg-in-a-shared-embedding-via-self-supervised",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-15T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.09.750152v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Combining complementary neurophysiological modalities offers a promising strategy for improving motor imagery (MI) brain-computer interfaces (BCIs), but learning shared representations across modalities remains largely unexplored. Here, we propose a two-phase deep learning framework for multimodal EEG-MEG decoding that explicitly decouples representation learning from downstream classification.…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}