{
  "id": 19182,
  "url": "https://arxiv.org/abs/2608.13072v1",
  "title": "EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding",
  "summary": "Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology. We propose EEG-PRIME, a two-stage EEG foundation model for cross-dataset multi-task decoding. EEG-PRIME combines masked pretraining with prototype-aligned instruction tuning to enable instruction-aware and subject-invariant decoding across diverse BCI paradigms. During pretraining, an EEG encoder learns transferable repres",
  "authors": "Shuailei Zhang, Muyun Jiang, Wei Zhang, Jinbo Chen, Zhiwei Guo, Yong Li et al.",
  "category": "research",
  "topics": null,
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-13T10:35:51.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/19182",
  "original_url": "https://arxiv.org/abs/2608.13072v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}