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Elashmawi, W.H., Ayman, A., Antoun, M., Mohamed, H., Mohamed, S.E., Amr, H., et al. (2024) A Comprehensive Review on Brain-Computer Interface (BCI)-Based Machine and Deep Learning Algorithms for Stroke Rehabilitation. Applied Sciences, 14, Article 6347.
https://doi.org/10.3390/app14146347

has been cited by the following article:

• TITLE: The Hybrid Mind in Precision Neurorehabilitation: Integrating AI-Driven Neurotechnologies and Ethical Governance

  AUTHORS: Rosimar Jose de Lima Dias

  KEYWORDS: Hybrid Mind, AI-Driven Neurorehabilitation, Brain-Computer Interfaces (BCIs), Deep Brain Stimulation (DBS), Neuroadaptive Algorithms

  JOURNAL NAME: World Journal of Neuroscience, Vol.15 No.2, May 26, 2025

  ABSTRACT: Artificial intelligence (AI) and neurotechnologies are redefining neuropsychological rehabilitation, enabling precision-guided, real-time neuromodulation. This review introduces the hybrid mind paradigm—a convergence of biological and artificial cognition—operationalized through AI-enhanced braincomputer interfaces (BCIs), deep brain stimulation (DBS), and adaptive neurofeedback. These technologies integrate closed-loop modulation and neuroadaptive algorithms to optimize neuroplasticity and functional recovery. While AI-driven systems show promise in cognitive and motor domains, translational barriers persist, including algorithmic opacity, neural data governance, and fragmented regulation. We synthesize recent evidence and outline strategic priorities: implementation of explainable AI frameworks, development of non-invasive neuromodulatory alternatives, and global harmonization of ethical standards. As AI and neuroscience converge, the hybrid mind paradigm signals a pivotal shift toward individualized, ethically guided neurorehabilitation.