{
  "id": 19553,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1876322",
  "title": "Meditation styles are highly discriminable from EEG at the subject level with limited generalization across the population: a machine-learning study",
  "summary": "Meditation has been associated with improvements in attention, emotional regulation, and mental wellbeing, motivating increasing interest in objective methods for assessing meditative states. In this study, we investigate whether EEG-based machine learning can reliably distinguish between multiple meditation styles and mind-wandering states. EEG data were recorded from experienced meditators performing three meditation styles, Shamatha, Vipassana, and Metta, together with an eyes-closed mind-wan",
  "authors": "Saqib Hayat",
  "category": "research",
  "topics": "regulation,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-14T00:00:00.000Z",
  "fetched_at": "2026-08-15T05:10:17.122Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/19553",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1876322",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}