Page 56 - JSOM Spring 2025
P. 56

41.  Bockbrader MA, Francisco G, Lee R, et al. Brain computer inter-  57.  Daly JJ, Huggins JE. Brain-computer interface: current and emerg-
             faces in rehabilitation medicine. PM R. 2018;10(9 Suppl 2):S233–  ing rehabilitation applications.  Arch Phys Med Rehabil. 2015;
             S243. doi:10.1016/j.pmrj.2018.05.028               96(3 Suppl):S1–S7. doi:10.1016/j.apmr.2015.01.007
          42.  Rasheed S. A review of the role of machine learning techniques to-  58.  Young, MJ, Lin DJ, Hochberg LR. Brain-computer interfaces in
             wards brain–computer interface applications. Machine Learning   neurorecovery and neurorehabilitation. Semin Neurol. 2021;41
             & Knowledge Extraction. 2021;3(4):835–862. doi.org/10.3390/  (2):206–216. doi:10.1055/s-0041-1725137
             make3040042                                     59.  Oganesian LL, Shanechi MM. Brain–computer interfaces for neu-
          43.  Drigas A, Sideraki A. Brain neuroplasticity leveraging virtual re-  ropsychiatric disorders. Nat Rev Bioeng. 2024;2:653–670. doi.
             ality and brain–computer interface technologies. Sensors (Basel).   org/10.1038/s44222-024-00177-2
             2024;24(17):5725. doi:10.3390/s24175725         60.  Al-Taleb MKH, Purcell M, Fraser M, Petric-Gray N, Vuckovic
          44.  Martin-Sanchez F, Maojo  V. Biomedical informatics and the   A.  Home used,  patient  self-managed,  brain-computer  interface
             convergence of nano-bio-info-cogno (NBIC) technologies. Yearb   for the management of central neuropathic pain post spinal cord
             Med Inform. 2009:134–142.                          injury: usability study.  J Neuroeng Rehabil. 2019;16:128. doi:
          45.  Krucoff MO, Rahimpour S, Slutzky MW, Edgerton VR, Turner   10.1186/s12984-019-0588-7
             DA. Enhancing nervous system recovery through neurobiologics,   61.  King BJ, Read GJM, Salmon PM. Identifying risk controls for
             neural interface training, and neurorehabilitation. Front Neuro-  future advanced brain-computer interfaces: a prospective risk
             sci. 2016;10:584. doi:10.3389/fnins.2016.00584     assessment approach using work domain analysis. Appl Ergon.
          46.  Chen Y, Wang F, Li T, et al. Several inaccurate or erroneous con-  2023;111:104028. doi:10.1016/j.apergo.2023.104028
             ceptions and misleading propaganda about brain-computer in-  62.  Patrick-Krueger KM, Burkhart I, Contreras-Vidal JL. The state of
             terfaces. Front Hum Neurosci. 2024;18:1391550. doi:10.3389/  clinical trials of implantable brain–computer interfaces. Nat Rev
             fnhum.2024.1391550                                 Bioeng. 2025;3:50–67. doi:10.1038/s44222-024-00239-5
          47.  Lorach H, Galvez A, Spagnolo V, et al. Walking naturally after spi-  63.  Drew L. United States sets the pace for implantable brain- computer
             nal cord injury using a brain–spine interface. Nature. 2023;618   interfaces. Nature. 2024;634(8032):S8-S10. doi:10.1038/d41586-
             (7963):126–133. doi:10.1038/s41586-023-06094-5     024-03046-5
          48.  Jebari K. Brain-machine interface and human enhancement – an   64.  Boulingre M, Portillo-Lara R, Green RA. Biohybrid neural in-
             ethical  review.  Neuroethics.  2013;6(4):617–625. doi:10.1007/  terfaces: improving the biological integration of neural implants.
             s12152-012-9176-2                                  Chem  Commun  (Camb).  2023;59(100):14745–14758.  doi:10.
          49.  Flesher SN, Downey JE, Weiss JM et al, A brain-computer inter-  1039/d3cc05006h
             face that evokes tactile sensations improves robotic arm control.   65.  Luo J, Xue N, Chen J.  A review: research progress of neural
             Science. 2021;372(6544):831–836. doi:10.1126/science.abd0380  probes for brain research and brain–computer interface. Biosen-
          50.  Greenspon CM, Valle G, Shelchkova ND, et al. Evoking stable   sors (Basel). 2022;12(12):1167. doi:10.3390/bios12121167
             and precise tactile sensations via multi-electrode intracortical   66.  Burwell S, Sample M, Racine E. Ethical aspects of brain com-
             microstimulation of the somatosensory cortex.  Nat Biomed   puter interfaces: a scoping review. BMC Med Ethics. 2017;18:60.
             Eng. Published online December 6, 2024. doi:10.1038/s41551-   doi:10.1186/s12910-017-0220-y
             024-01299-z                                     67.  Klein, E. Informed consent in implantable BCI research: identify-
          51.  Valle G, Alamri AH, Downey JE, et al. Tactile edges and motion   ing risks and exploring meaning. Sci Eng Ethics. 2016;22:1299–
             via patterned microstimulation of the human somatosensory   1317. doi:10.1007/s11948-015-9712-7
             cortex.  Science.  2025;387(6731):315–322. doi:10.1126/science.  68.  Khan S, Aziz T. Transcending the brain: is there a cost to hack-
             adq5978                                            ing the nervous system? Brain Commun. 2019;1(1):fcz015. doi:
          52.  Dretsch MN, Neff D, Caserta R, Deagle E, Hoge CW, Adler AB.   10.1093/braincomms/fcz015
             Rates of behavioral health conditions and health risk behaviors   69.  Drew L. The brain-reading devices helping paralysed people to
             in operators and support personnel in U.S. Special Operations   move, talk and touch.  Nature.  2022;604(7906):416–419. doi:
             Forces. Psychiatry. 2020;83(4):358–374. doi:10.1080/00332747  10.1038/d41586-022-01047-w
             .2020.1768787                                   70.  Tracey I, Flower R. The warrior in the machine: neuroscience goes
          53.  Frueh BC, Madan A, Fowler JC, et al. “Operator syndrome”: a   to war. Nat Rev Neurosci. 2014;15(12):825–834. doi:10.1038/
             unique constellation of medical and behavioral health-care needs   nrn3835
             of military special operation forces. Int J Psychiatry Med. 2020;   71.  Rao RP, Stocco A, Bryan M, et al. A direct brain-to-brain inter-
             55(4):281–295. doi:10.1177/0091217420906659        face in humans.  PLoS One. 2014;9(11):e111332. doi:10.1371/
          54.  Mudgal SK, Sharma S, Chaturvedi J, Sharma A. Brain-computer in-  journal.pone.0111332
             terface advancement in neurosciences: applications and issues. In-  72.  Vakilipour P, Fekrvand S. Brain-to-brain interface technology: a
             terdisciplinary Neurosurgery. 2020;20:100694. doi.org/10.1016/   brief history, current state, and future goals. Int J Dev Neurosci.
             j.inat.2020.100694                                 2024;84:351–367. doi:10.1002/jdn.10334
          55.  Popa LL, Chira D, Strilciuc S, et al. Non-invasive systems appli-  73.  Scharre P. Artificial intelligence: risks and opportunities for SOF.
             cation in traumatic brain injury rehabilitation. Brain Sci. 2023;   In: Davis ZS, Gac F, Rager C, Reiner P, Snow J, eds. Strategic La-
             x113(11):1594. doi.org/10.3390/brainsci13111594    tency Unleashed: The Role of Technology in a Revisionist Global
          56.  Hampson RE, Song D, Robinson BS, et al. Developing a hippocam-  Order and the Implications for Special Operations Forces. Center
             pal neural prosthetic to facilitate human memory encoding and re-  for Global Security Research, Lawrence Livermore National Lab-
             call. J Neural Eng. 2018;15(3):036014. doi:10.1088/1741-2552/  oratory; 2021.
             aaaed7
                                                             PMID: 40042891; DOI: 10.55460/FA29-NVKE

















          54  |  JSOM   Volume 25, Edition 1 / Spring 2025
   51   52   53   54   55   56   57   58   59   60   61