Annals of the Russian academy of medical sciencesAnnals of the Russian academy of medical sciencesВестник Российской академии медицинских наук0869-60472414-3545"Paediatrician" Publishers LLC1799410.15690/vramn17994NEUROLOGY AND NEUROSURGERY: CURRENT ISSUESАКТУАЛЬНЫЕ ВОПРОСЫ НЕВРОЛОГИИ И НЕЙРОХИРУРГИИResearch ArticleInvasive Brain–Computer Interfaces: 25 Years of Clinical Trials, Scientific and Practical IssuesИнвазивные интерфейсы мозг–компьютер: 25 лет клинических испытаний, научные и практические вопросыhttps://orcid.org/0000-0002-7826-51358088-9921MokienkoOlesya A.МокиенкоОлеся А.
Russian Federation

MD, PhD, Senior Researcher

к.м.н., старший научный сотрудн

Lesya.md@yandex.ruInstitute of Higher Nervous Activity and Neurophysiology of the RASИнститут высшей нервной деятельности и нейрофизиологии РАНPirogov Russian National Research Medical University (Pirogov Medical University)Российский национальный исследовательский медицинский университет имени Н.И. ПироговаResearch Center of NeurologyНаучный центр неврологии28122024131220247951106202413102024Copyright ©; 2024, "Paediatrician" Publishers LLCCopyright ©; 2024, Издательство "Педиатръ"2024"Paediatrician" Publishers LLCИздательство "Педиатръ"https://vestnikramn.spr-journal.ru/jour/article/view/17994

A brain-computer interface (BCI) is a system that measures brain activity and converts it in real-time into functionally useful outputs to replace, restore, enhance, supplement, and/or improve the natural outputs of the brain. In invasive BCIs, electrodes are placed intracranially for more accurate and faster data exchange between the brain and external devices. The primary medical objective of these technologies is to compensate for motor or speech function in patients with tetraparesis and anarthria. In recent years, the emergence of new neuroimplants for BCIs and the results demonstrated in clinical trials have led to a notable increase in interest in these systems from the scientific community, investors, and the public. This review compares different types of medical invasive BCIs, analyzes and discusses the achievements and unsolved problems of clinical application of these neurotechnologies, as well as possible consequences and risks of their wider use.

Интерфейс мозг–компьютер (ИМК) — это система, которая измеряет активность головного мозга и преобразует ее в режиме реального времени в функционально полезные выходные данные для замены, восстановления, усиления, дополнения и/или улучшения естественных выходных данных мозга. В инвазивных ИМК для более точного и быстрого информационного обмена между мозгом и внешними устройствами электроды размещаются интракраниально. Основное медицинское назначение данных технологий — компенсация двигательной или речевой функции у пациентов с тетрапарезом и анартрией. В последние годы на фоне появления новых типов нейроимплантатов для ИМК и результатов, продемонстрированных в клинических исследованиях, к данным системам существенно возрос интерес со стороны научного сообщества, инвесторов и общественности. Данный обзор посвящен анализу и обсуждению достижений и нерешенных проблем клинического применения технологий инвазивных ИМК, а также анализу возможных последствий и рисков более широкого использования данных нейротехнологий.

brain–computer interfacesquadriplegialocked-in syndromeинтерфейсы мозг–компьютертетраплегия«синдром запертого человека»Министерство здравоохранения Российской ФедерацииMinistry of Health of the Russian Federation122051700017-2BCIsociety.org/bci-definition/ [Internet]. BCI Definition. Available from: https://bcisociety.org/bci-definition (accessed: 30.05.2014).Karikari E, Koshechkin KA. Review on brain–computer interface technologies in healthcare. Biophys Rev. 2023;15(5):1351–1358. doi: https://doi.org/10.1007/s12551-023-01138-6Mokienko OA, Lyukmanov RKh, Bobrov PD, et al. Brain–computer interfaces for upper limb motor recovery after stroke: current status and development prospects (review). Sovremennye tehnologii v medicine. 2023;15(6):63–74. doi: https://doi.org/10.17691/stm2023.15.6.07Mokienko OA. Brain–computer interfaces with intracortical implants for motor and communication functions compensation: review of recent developments. Sovremennye tehnologii v medicine. 2024;16(1):78–89. doi: https://doi.org/10.17691/stm2024.16.1.08Mitchell P, Lee SCM, Yoo PE, et al. Assessment of safety of a fully implanted endovascular brain-computer interface for severe paralysis in 4 patients: the stentrode with thought-controlled digital switch (switch) study. JAMA Neurol. 2023;80(3):270–278. doi: https://doi.org/10.1001/jamaneurol.2022.4847Drew L. The rise of brain-reading technology: what you need to know. Nature. 2023;623(7986):241–243. doi: https://doi.org/10.1038/d41586-023-03423-6Eisenstein M. Seven technologies to watch in 2024. Nature. 2024;625(7996):844–848. doi: https://doi.org/10.1038/d41586-024-00173-xMoses DA, Metzger SL, Liu JR, et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. N Engl J Med. 2021;385(3):217–227. doi: https://doi.org/10.1056/NEJMoa2027540Wandelt SK, Bjånes DA, Pejsa K, et al. Representation of internal speech by single neurons in human supramarginal gyrus. Nat Hum Behav. 2024;8(6):1136–1149. doi: https://doi.org/10.1038/s41562-024-01867-yWillett FR, Avansino DT, Hochberg LR, et al. High-performance brain-to-text communication via handwriting. Nature. 2021;593(7858):249–254. doi: https://doi.org/10.1038/s41586-021-03506-2Wandelt SK, Kellis S, Bjånes DA, et al. Decoding grasp and speech signals from the cortical grasp circuit in a tetraplegic human. Neuron. 2022;110(11):1777–1787.e3. doi: https://doi.org/10.1016/j.neuron.2022.03.009Bergeron D, Iorio-Morin C, Bonizzato M, et al. Use of invasive brain–computer interfaces in pediatric neurosurgery: technical and ethical considerations. J Child Neurol. 2023;38(3–4):223–238. doi: https://doi.org/10.1177/08830738231167736Zhang Z, Chen Y, Zhao X, et al. [Ethical considerations for medical applications of implantable brain–computer interfaces]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024;41(1):177–183. doi: https://doi.org/10.7507/1001-5515.202309083Soldado-Magraner J, Antonietti A, French J, et al. Applying the ieee brain neuroethics framework to intra-cortical brain–computer interfaces. J Neural Eng. 2024;21(2). doi: https://doi.org/10.1088/1741-2552/ad3852Valeriani D, Santoro F, Ienca M. The present and future of neural interfaces. Front Neurorobot. 2022;16:953968. doi: https://doi.org/10.3389/fnbot.2022.953968Guenther FH, Brumberg JS, Wright EJ, et al. A wireless brain–machine interface for real-time speech synthesis. PLoS One. 2009;4(12):e8218. doi: https://doi.org/10.1371/journal.pone.0008218Brumberg JS, Nieto-Castanon A, Kennedy PR, Guenther FH. Brain–computer interfaces for speech communication. Speech Commun. 2010;52(4):367–379. doi: https://doi.org/10.1016/j.specom.2010.01.001Kennedy PR, Kirby MT, Moore MM, et al. Computer control using human intracortical local field potentials. IEEE Trans Neural Syst Rehabil Eng. 2004;12(3):339–344. doi: https://doi.org/10.1109/TNSRE.2004.834629Kennedy PR, Bakay RA, Moore MM, et al. Direct control of a computer from the human central nervous system. IEEE Trans Rehabil Eng. 2000;8(2):198–202. doi: https://doi.org/10.1109/86.847815Flesher SN, Downey JE, Weiss JM, et al. A brain–computer interface that evokes tactile sensations improves robotic arm control. Science. 2021;372(6544):831–836. doi: https://doi.org/10.1126/science.abd0380Shah NP, Willsey MS, Hahn N, et al. A brain-computer typing interface using finger movements. Int IEEE EMBS Conf Neural Eng. 2023;2023:10.1109/ner52421.2023.10123912. doi: https://doi.org/10.1109/ner52421.2023.10123912Hochberg LR, Serruya MD, Friehs GM, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature. 2006;442(7099):164–171. doi: https://doi.org/10.1038/nature04970Hochberg LR, Bacher D, Jarosiewicz B, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature. 2012;485(7398):372–375. doi: https://doi.org/10.1038/nature11076Downey JE, Brane L, Gaunt RA, et al. Motor cortical activity changes during neuroprosthetic-controlled object interaction. Sci Rep. 2017;7(1):16947. doi: https://doi.org/10.1038/s41598-017-17222-3Aflalo T, Kellis S, Klaes C, et al. Neurophysiology. decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science. 2015;348(6237):906–910. doi: https://doi.org/10.1126/science.aaa5417Handelman DA, Osborn LE, Thomas TM, et al. Shared control of bimanual robotic limbs with a brain–machine interface for self-feeding. Front Neurorobot. 2022;16:918001. doi: https://doi.org/10.3389/fnbot.2022.918001Bouton CE, Shaikhouni A, Annetta NV, et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature. 2016;533(7602):247–250. doi: https://doi.org/10.1038/nature17435Ajiboye AB, Willett FR, Young DR, et al. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. Lancet. 2017;389(10081):1821–1830. doi: https://doi.org/10.1016/S0140-6736(17)30601-3Pandarinath C, Nuyujukian P, Blabe CH, et al. High performance communication by people with paralysis using an intracortical brain–computer interface. Elife. 2017;6:e18554. doi: https://doi.org/10.7554/eLife.18554Nuyujukian P, Albites Sanabria J, Saab J, et al. Cortical control of a tablet computer by people with paralysis. PLoS One. 2018;13(11):e0204566. doi: https://doi.org/10.1371/journal.pone.0204566Simeral JD, Hosman T, Saab J, et al. Home use of a percutaneous wireless intracortical brain–computer interface by individuals with tetraplegia. IEEE Trans Biomed Eng. 2021;68(7):2313–2325. doi: https://doi.org/10.1109/TBME.2021.3069119Jarosiewicz B, Sarma AA, Bacher D, et al. Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface. Sci Transl Med. 2015;7(313):313ra179. doi: https://doi.org/10.1126/scitranslmed.aac7328Bacher D, Jarosiewicz B, Masse NY, et al. Neural point-and-click communication by a person with incomplete locked-in syndrome. Neurorehabil Neural Repair. 2015;29(5):462–471. doi: https://doi.org/10.1177/1545968314554624Chaudhary U, Vlachos I, Zimmermann JB, et al. Spelling interface using intracortical signals in a completely locked-in patient enabled via auditory neurofeedback training. Nat Commun. 2022;13(1):1236. doi: https://doi.org/10.1038/s41467-022-28859-8Willett FR, Kunz EM, Fan C, et al. A high-performance speech neuroprosthesis. Nature. 2023;620(7976):1031–1036. doi: https://doi.org/10.1038/s41586-023-06377-xGilja V, Pandarinath C, Blabe CH, et al. Clinical translation of a high-performance neural prosthesis. Nat Med. 2015;21(10):1142–1145. doi: https://doi.org/10.1038/nm.3953Collinger JL, Wodlinger B, Downey JE, et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet. 2013;381(9866):557–564. doi: https://doi.org/10.1016/S0140-6736(12)61816-9Wodlinger B, Downey JE, Tyler-Kabara EC, et al. Ten-dimensional anthropomorphic arm control in a human brain–machine interface: difficulties, solutions, and limitations. J Neural Eng. 2015;12(1):016011. doi: https://doi.org/10.1088/1741-2560/12/1/016011Chadwick EK, Blana D, Simeral JD, et al. Continuous neuronal ensemble control of simulated arm reaching by a human with tetraplegia. J Neural Eng. 2011;8(3):034003. doi: https://doi.org/10.1088/1741-2560/8/3/034003Moly A, Costecalde T, Martel F, et al. An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic. J Neural Eng. 2022;19(2):026021. doi: https://doi.org/10.1088/1741-2552/ac59a0Wang W, Collinger JL, Degenhart AD, et al. An electrocorticographic brain interface in an individual with tetraplegia. PLoS One. 2013;8(2):e55344. doi: https://doi.org/10.1371/journal.pone.0055344Benabid AL, Costecalde T, Eliseyev A, et al. An exoskeleton controlled by an epidural wireless brain–machine interface in a tetraplegic patient: a proof-of-concept demonstration. Lancet Neurol. 2019;18(12):1112–1122. doi: https://doi.org/10.1016/S1474-4422(19)30321-7Vansteensel MJ, Pels EGM, Bleichner MG, et al. Fully implanted brain–computer interface in a locked-in patient with ALS. N Engl J Med. 2016;375(21):2060–2066. doi: https://doi.org/10.1056/NEJMoa1608085Davis KC, Meschede-Krasa B, Cajigas I, et al. Design-development of an at-home modular brain–computer interface (BCI) platform in a case study of cervical spinal cord injury. J Neuroeng Rehabil. 2022;19(1):53. doi: https://doi.org/10.1186/s12984-022-01026-2Metzger SL, Littlejohn KT, Silva AB, et al. A high-performance neuroprosthesis for speech decoding and avatar control. Nature. 2023;620(7976):1037–1046. doi: https://doi.org/10.1038/s41586-023-06443-4Pels EGM, Aarnoutse EJ, Leinders S, et al. Stability of a chronic implanted brain–computer interface in late-stage amyotrophic lateral sclerosis. Clin Neurophysiol. 2019;130(10):1798–1803. doi: https://doi.org/10.1016/j.clinph.2019.07.020Cajigas I, Davis KC, Meschede-Krasa B, et al. Implantable brain-computer interface for neuroprosthetic-enabled volitional hand grasp restoration in spinal cord injury. Brain Commun. 2021;3(4):fcab248. doi: https://doi.org/10.1093/braincomms/fcab248Angrick M, Luo S, Rabbani Q, et al. Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS. Sci Rep. 2024;14(1):9617. doi: https://doi.org/10.1038/s41598-024-60277-2Luo S, Angrick M, Coogan C, et al. Stable decoding from a speech BCI enables control for an individual with ALS without recalibration for 3 months. Adv Sci (Weinh). 2023;10(35):e2304853. doi: https://doi.org/10.1002/advs.202304853Oxley TJ, Yoo PE, Rind GS, et al. Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks in severe paralysis: first in-human experience. J Neurointerv Surg. 2021;13(2):102–108. doi: https://doi.org/10.1136/neurintsurg-2020-016862Brannigan JFM, Fry A, Opie NL, et al. Endovascular brain–computer interfaces in poststroke paralysis. Stroke. 2024;55(2):474–483. doi: https://doi.org/10.1161/STROKEAHA.123.037719Ho E, Hettick M, Papageorgiou D, et al. The layer 7 cortical interface: a scalable and minimally invasive brain–computer interface platform. bioRxiv. 2022; doi: https://doi.org/10.1101/2022.01.02.474656Jung T, Zeng N, Fabbri JD, et al. Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain–computer interface device. bioRxiv. 2024;2024.05.17.594333. doi: https://doi.org/10.1101/2024.05.17.594333Donoghue JP, Nurmikko A, Black M, et al. Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia. J Physiol. 2007;579(Pt 3):603–611. doi: https://doi.org/10.1113/jphysiol.2006.127209Rubin DB, Ajiboye AB, Barefoot L, et al. Interim safety profile from the feasibility study of the braingate neural interface system. Neurology. 2023;100(11):e1177–е1192. doi: https://doi.org/10.1212/WNL.0000000000201707Clinicaltrials.gov/study/NCT06429735 [Internet]. Study Details. Available from: https://clinicaltrials.gov/study/NCT06429735 (accessed: 30.05.2024).Rainey S, Maslen H, Savulescu J. When thinking is doing: responsibility for BCI-mediated action. AJOB Neurosci. 2020;11(1):46–58. doi: https://doi.org/10.1080/21507740.2019.1704918Petrosyan A, Voskoboinikov A, Sukhinin D, et al. Speech decoding from a small set of spatially segregated minimally invasive intracranial EEG electrodes with a compact and interpretable neural network. J Neural Eng. 2022;19(6). doi: https://doi.org/10.1088/1741-2552/aca1e1Valle G, Katic Secerovic N, Eggemann D, et al. Biomimetic computer-to-brain communication enhancing naturalistic touch sensations via peripheral nerve stimulation. Nat Commun. 2024;15(1):1151. doi: https://doi.org/10.1038/s41467-024-45190-6Soghoyan G, Biktimirov A, Matvienko Y, et al. Peripheral nerve stimulation enables somatosensory feedback while suppressing phantom limb pain in transradial amputees. Brain Stimul. 2023;16(3):756–758. doi: https://doi.org/10.1016/j.brs.2023.04.017