{
  "id": 15203,
  "url": "https://arxiv.org/abs/2607.28204v1",
  "title": "Toward Annotation-Efficient Continuous Emotion Arousal Quantification via Group-Level EEG Dynamic Neural Synchrony",
  "summary": "Continuous emotional arousal quantification remains bottlenecked by time-consuming and labor-intensive manual annotation. This work investigates group-level EEG dynamic neural synchrony (DNS) as a principled signal for continuous arousal quantification that bypasses per-subject manual labeling. Using Correlated Component Analysis (CorrCA) with sliding-window computation across four EEG datasets spanning 142 subjects and over 207 hours, we systematically evaluate DNS as a group-level marker for e",
  "authors": "Guandong Pan, Yaqian Yang, Shi Chen, Yi Zheng, Yi Zhen, Hongwei Zheng, Shaoting Tang",
  "category": "research",
  "topics": "jobs-economy,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-30T13:43:40.000Z",
  "fetched_at": "2026-07-31T05:10:57.675Z",
  "source_slug": "x-arxiv-cs-hc",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/15203",
  "original_url": "https://arxiv.org/abs/2607.28204v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}