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

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.

Authors

Publication Details

Journal
World Journal of Neuroscience
Published
2025-01-01
DOI
https://doi.org/10.4236/wjns.2025.152010
Citations
2
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
2.86
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article

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

Rosimar Jose de Lima Dias
2 citations
World Journal of Neuroscience
EEG and Brain-Computer Interfaces
2.86
article

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

Rosimar Jose de Lima Dias
article en
2 citations

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.

World Journal of NeuroscienceVol. 15(02)
Openalex Percentile: Top 10%
EEG and Brain-Computer Interfaces
2.86
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