BRAIN-COMPUTER INTERFACES IN THE MANAGEMENT OF PHANTOM LIMB PAIN: CURRENT CONCEPTS AND EVIDENCE

Authors

  • Muhammad Kalim Raza Assistant of the Department of Human Anatomy, Samarkand State Medical University, Samarkand, Uzbekistan
  • Alkov Ruslan Alimjonovich Student, Samarkand State Medical University, Samarkand, Uzbekistan
  • Lutfullaev Khafizullo Zaynullaevich Student, Samarkand State Medical University, Samarkand, Uzbekistan
  • Ruzikulov Xumoyunbek Kuyli ugli Student, Samarkand State Medical University, Samarkand, Uzbekistan
  • Yakubova Khadichakhon Kasymkhonovna Student, Samarkand State Medical University, Samarkand, Uzbekistan

Abstract

Brain–Computer Interface (BCI) technology has rapidly advanced, offering promising applications in neuroscience, rehabilitation, and prosthetic control. This review synthesizes recent developments and ethical considerations in BCI, with a focus on motor imagery (MI)-based systems and their role in neurorehabilitation, particularly for stroke and amputee patients. MI-BCI enables users to control external devices through imagined movements, facilitating communication and motor recovery in individuals with motor impairments or paralysis [18, 1]. Rehabilitation systems integrating BCI with robotics and virtual reality have shown efficacy in improving motor function post-stroke, although challenges remain in system usability, clinical validation, and home implementation [7]. Additionally, BCI training has demonstrated potential in reducing phantom limb pain by modulating sensorimotor brain plasticity, offering novel therapeutic avenues for amputees [18, 20]. Ethical aspects of BCI technology encompass user safety, autonomy, privacy, informed consent, and justice, with emerging concerns related to psychological effects and animal research in commercial ventures [3]. The integration of AI and brain-to-brain interfaces further complicates the ethical landscape, necessitating ongoing discourse and policy development. Signal processing techniques, including feature extraction and classification algorithms, are critical for enhancing BCI performance, with deep learning methods gaining prominence [1]. Despite technological progress practical challenges such as electrode attachment, system portability, and real-time multi-intention prediction must be addressed to facilitate daily life applications [2]. In summary, BCI technology holds transformative potential for neuroscience and rehabilitation, yet requires multidisciplinary efforts to optimize technical performance, address ethical concerns, and ensure equitable access. Continued research is essential to refine BCI systems for clinical and commercial use, improve patient outcomes, and guide responsible innovation in this evolving field.

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Published

2025-09-12

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Articles

How to Cite

BRAIN-COMPUTER INTERFACES IN THE MANAGEMENT OF PHANTOM LIMB PAIN: CURRENT CONCEPTS AND EVIDENCE. (2025). Web of Medicine: Journal of Medicine, Practice and Nursing , 3(9), 40-46. https://webofjournals.com/index.php/5/article/view/5034

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