Explainability of Recurrent Neural Networks for Enhancing P300-based Brain-Computer Interfaces
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
Record details
Published: 11 May 2026
Source: arXiv
Category: Research
Topics: Healthcare · Children & education · Transparency
Retrieved: 14 July 2026
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ethics.ai (11 May 2026), “Explainability of Recurrent Neural Networks for Enhancing P300-based Brain-Computer Interfaces,” evidence record 4580, https://ethics.ai/record/4580 (originally published by arXiv).
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