{
  "id": 4580,
  "url": "https://arxiv.org/abs/2605.10121v1",
  "title": "Explainability of Recurrent Neural Networks for Enhancing P300-based Brain-Computer Interfaces",
  "summary": "Brain-Computer Interfaces (BCIs) based on P300 event-related potentials offer promising applications in health, education, and assistive technologies. However, challenges related to inter- and intra-subject variability and the explainability of Deep Learning (DL) models limit their practical deployment. In this work, we present the Post-Recurrent Module (PRM), an additional layer designed to improve both performance and transparency, incorporated into a Recurrent Neural Network (RNN) architectur",
  "authors": "Christian Oliva, Vinicio Changoluisa, Francisco B Rodríguez, Luis F Lago-Fernández",
  "category": "research",
  "topics": "healthcare,children-education,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-11T07:36:03.000Z",
  "fetched_at": "2026-07-14T16:31:08.353Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4580",
  "original_url": "https://arxiv.org/abs/2605.10121v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}