Acta Scientific Neurology (ASNE) (ISSN: 2582-1121)

Editorial Volume 8 Issue 11

The Brain Machine Interface: An Emerging New Technology

Anuvrat Sinha*

Consultant Neurosurgeon, Neurosurgery, Narayana Super Speciality, India

*Corresponding Author:Anuvrat Sinha, Consultant Neurosurgeon, Neurosurgery, Narayana Super Speciality, India.

Received: September 24, 2025; Published: October 01, 2025

Abstract

Brain-machine interface (BMI) is a device that translates neuronal information into commands capable of controlling external software or hardware such as a computer or robotic arm. It is an emerging technology that facilitates communication between brain and computer and has attracted a great deal of research in recent years [2]. BMIs are often used as assisted living devices for individuals with motor or sensory impairments [1],hence improving the quality of their lives.

References

  1. Lim WS., et al. “Forging a frailty-ready healthcare system to meet population ageing”. International Journal of Environmental Research and Public Health12 (2017): 1448.
  2. https://www.stroke.org/en/
  3. “Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019” 20.10 (2021): 795-820.
  4. Satue E., et al. “Incidence and risk conditions of ischemic stroke in older adults”. Acta Neurologica Scandinavica4 (2016): 250-257.
  5. Hu X., et al. “Cerebral vascular disease and neurovascular injury in ischemic stroke”. Circulation Research3 (2017): 449-471.
  6. Vitt JR., et al. “Multimodal and autoregulation monitoring in the neurointensive care unit”. Frontiers in Neurology14 (2017): 1155986.
  7. Kathner-Schaffert C., et al. “Early stroke induces long-term impairment of adult neurogenesis accompanied by hippocampal-mediated cognitive decline”. Cells12 (2019): 1654.
  8. Filler J., et al. “Risk factors for cognitive impairment and dementia after stroke: a systematic review and meta-analysis”. The Lancet Healthy Longevity1 (2024): e31-e44.
  9. Greenberg SM., et al. “2022 guideline for the management of patients with spontaneous intracerebral hemorrhage: A guideline from the American Heart Association/American Stroke Association”. Stroke7 (2022).
  10. Feigin V. “Abstracts of the 8th International Conference on Neurology and Epidemiology 2022”. Neuroepidemiology (2022): 56.
  11. Zhang F., et al. “Systemic-immune-inflammation index as a promising biomarker for predicting perioperative ischemic stroke in older patients who underwent non-cardiac surgery”. Frontiers in Aging Neuroscience 14 (2022): 865244.
  12. Skains RM., et al. “Emergency department programs to support medication safety in older adults: a systematic review and meta-analysis”. JAMA Network Open3 (2025): e250814-e250814.
  13. World Stroke Organization (WSO): Global Stroke Fact Sheet (2022).
  14. Samaniego EA., et al. “Priorities for advancements in neuroimaging in the diagnostic workup of acute stroke”. Stroke 12 (2023): 3190-3201.
  15. Chung KJ. “Improving the Reliability and Accessibility of CT Perfusion Imaging in Acute Ischemic Stroke”. (Doctoral dissertation, The University of Western Ontario (Canada)) (2023).
  16. https://www.ncbi.nlm.nih.gov/books/NBK592420/
  17. Qin N., et al. “Modulation of mitochondrial dysfunction: Mechanisms and strategies for the use of natural products to treat stroke”. Neural Regeneration Research (2017): 10-4103.
  18. Loro A., et al. “Balance rehabilitation through robot-assisted gait training in post-stroke patients: a systematic review and meta-analysis”. Brain Sciences1 (2023): 92.
  19. Wang X., et al. “Application of vagus nerve stimulation on the rehabilitation of upper limb dysfunction after stroke: a systematic review and meta-analysis”. Frontiers in Neurology14 (2023): 1189034.
  20. Carmichael JP. “Novel Approaches in Rehabilitation after Total Knee Arthroplasty (Doctoral dissertation, University of Colorado Denver, Anschutz Medical Campus) (2021).
  21. Forró T., et al. “Dysfunction of the neurovascular unit in ischemic stroke: highlights on microRNAs and exosomes as potential biomarkers and therapy”. International Journal of Molecular Sciences11 (2021): 5621.
  22. Bhatia A and Maddox TM. “Remote patient monitoring in heart failure: factors for clinical efficacy”. International Journal of Heart Failure1 (2017): 31.
  23. Antonino Francisco., et al. “Wearables and Atrial Fibrillation: Advances in Detection, Clinical Impact, Ethical Concerns, and Future Perspectives”. Cureus 1 (2025): e77404. 
  24. Ortiz-Piña M., et al. “Effects of tele-rehabilitation compared with home-based in-person rehabilitation for older adult’s function after hip fracture”. International Journal of Environmental Research and Public Health10 (2021): 5493.
  25. Jakovljevic M., et al. “Aging and global health. In Handbook of global health”. Cham: Springer International Publishing (2021).
  26. https://www.world-stroke.org/
  27. Imran A. “Why addressing digital inequality should be a priority”. The Electronic Journal of Information Systems in Developing Countries3 (2023): e12255.
  28. Elizabeth Pirraglia. et al. “Lower mortality risk in APOE4 carriers with normal cognitive ageing”. Scientific Reports (2023).
  29. Adcock AK., et al. “Trends in use, outcomes, and disparities in endovascular thrombectomy in US patients with stroke aged 80 years and older compared with younger patients”. JAMA Network Open 6 (2022): e2215869-e2215869.
  30. Sorino P. “Leveraging artificial intelligence for enhanced and human-centered healthcare solutions”. (2025).
  31. Li J., et al. “Role of Frailty in Predicting Outcomes After Stroke: A Systematic Review and Meta-Analysis”. Front Psychiatry 15 (2024): 1347476.
  32. Pan Y., et al. “Frailty and Functional Outcome After Stroke: Meta-analysis”. Age AgeingS2 (2023): afad104.107.
  33. Yu L., et al. “Pre-stroke Frailty Predicts 1-Year Mortality and Functional Decline”. Journal of Geriatric Neurology 6 (2022): 447-455.
  34. Mugisha S., et al. “Computer-Mediated Therapies for Stroke: Meta-analysis of RCTs”. arXiv Preprint (2024).
  35. Saposnik G., et al. “Effectiveness of Virtual Reality Using Wii Gaming Technology in Stroke Rehabilitation”. Stroke5 (2011): 1380-1386.
  36. Rodrigues P., et al. “Design and Evaluation of Virtual Reality-Based Telerehabilitation”. arXiv preprint arXiv (2025): 2501.06899
  37. Chen X., et al. “Stem Cell Therapy in Stroke: Meta-analysis of Randomized and Nonrandomized Trials”. Neurorehabilitation and Neural Repair 1 (2024): 22-34.
  38. Muir KW., et al. “PISCES-II: Neural Stem Cell Transplantation for Stroke Recovery”. Lancet Neurology 5 (2023): 386-398.

Citation

Citation: Ibrahim Krenawi., et al. “Computational Modeling of Neurovascular Coupling Dysfunction in Early Silent Cerebral Small Vessel Disease: A Proof-of-Concept Study".Acta Scientific Neurology 8.11 (2025): 23-32.

Copyright

Copyright: © 2025 Ibrahim Krenawi., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate32%
Acceptance to publication20-30 days

Indexed In




News and Events


Contact US