{
  "id": 10602346,
  "title": "EEG-Fusion: Failure-Informed Source-Free Expert Routing for Robust Motor Imagery EEG Decoding",
  "url": "https://urgent.news/2026/09/27/eeg-fusion-failure-informed-source-free-expert-routing-for-robust",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-27T22:00:11.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.33962v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Subject-independent motor-imagery (MI) EEG decoding can exhibit subject-level failures even when average performance appears acceptable: under subject shift, a decoder can become an overconfident near-one-class predictor. This is especially problematic in source-free deployment, where target-user labels are unavailable during adaptation and expert selection. We present \\textit{EEG-Fusion}, a…",
  "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."
}