Sarda, J., Shah, M., Vaghela, R., Vaishnani, D. and Patel, H. (2025) Hand Kinematics and Decoding Hindlimb Kinematics Using Local Field Potentials Using a Deep Neural Network Decoding Framework. In: Subasi, A., Qaisar, S.M., Bhoi, A.K. and Srinivasu, P.N., Eds., Artificial Intelligence Applications for Brain-Computer Interfaces, Elsevier, 63-79.
https://doi.org/10.1016/b978-0-443-33414-6.00004-6

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.