<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "JATS-journalpublishing1-3.dtd">
<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">omna</journal-id><journal-title-group><journal-title xml:lang="ru">Омский научный вестник</journal-title><trans-title-group xml:lang="en"><trans-title>Omsk Scientific Bulletin</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1813-8225</issn><issn pub-type="epub">2541-7541</issn><publisher><publisher-name>Омский государственный технический университет</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.25206/1813-8225-2024-192-150-160</article-id><article-id custom-type="edn" pub-id-type="custom">ANKBHV</article-id><article-id custom-type="elpub" pub-id-type="custom">omna-119</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ЭЛЕКТРОНИКА, ФОТОНИКА, ПРИБОРОСТРОЕНИЕ И СВЯЗЬ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>ELECTRONICS, PHOTONICS, APPLIANCE AND COMMUNICATIONS</subject></subj-group></article-categories><title-group><article-title>Перспективные системы для управления протезами: обзор</article-title><trans-title-group xml:lang="en"><trans-title>Promising systems for controlling prosthetics: a review</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Самандари</surname><given-names>А. М.</given-names></name><name name-style="western" xml:lang="en"><surname>Samandari</surname><given-names>A. M.</given-names></name></name-alternatives><bio xml:lang="ru"><p>САМАНДАРИ Али Мирдан, aспирант </p><p>г. Белгород</p></bio><bio xml:lang="en"><p>SAMANDARI Ali Mirdan, Graduate Student</p><p>Belgorod</p></bio><email xlink:type="simple">aliofphysics777ali@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru">Белгородский государственный национальный исследовательский университет<country>Россия</country></aff><aff xml:lang="en">Belgorod State National Research University<country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2024</year></pub-date><pub-date pub-type="epub"><day>30</day><month>12</month><year>2024</year></pub-date><volume>0</volume><issue>4</issue><fpage>150</fpage><lpage>160</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Самандари А.М., 2024</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="ru">Самандари А.М.</copyright-holder><copyright-holder xml:lang="en">Samandari A.M.</copyright-holder><license license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://onv.omgtu.ru/jour/article/view/119">https://onv.omgtu.ru/jour/article/view/119</self-uri><abstract><p>Люди с ограниченными возможностями в условиях стремительной научно-технической революции надеются, что она преодолеет лишь оказание им поддержки и найдет подходящие решения, чтобы вести нормальную жизнь. Взаимодействие наук между собой учитывает проблему физических недостатков и, в частности, потерю как верхних, так и нижних конечностей. Современные протезы являются продуктом пересечения науки и технологической революции и все еще находятся на пути своего становления, поскольку содержат исполнительные механизмы, которые могут управляться сигналами мозга по принципу нейроинтерфейсов. Методы нейровизуализации, такие как электромиография, функциональная инфракрасная спектроскопия и электроэнцефалография, являются превосходными методами управления этими современными протезами, которые можно смоделировать по двум функциям, а именно по независимой работе и гибридной работе. В свете этих данных статья рассматривает эти системы в их индивидуальных и гибридных состояниях. Кроме того, в статье указывается, какой из этих методов может быть выбран в качестве предпочтительной системы. Область применения методологии исследования ограничена методами нейровизуализации в отношении сценариев неврологической реабилитации и восстановления утраченных функций. Обзор имеет три направления. Первое направление собирает, обобщает и оценивает информацию из соответствующих исследований, опубликованных за последнее десятилетие. Второе представляет важные результаты предыдущих экспериментальных результатов в этой области в отношении текущих исследований. Исследование было проведено систематически, чтобы предоставить всем экспертам и ученым полное представление и основанные на доказательствах методы управления протезами. Третья часть заключается в выявлении широкой области знаний, требующей дальнейшего изучения, и отслеживании последовательности научных достижений в этих системах и возможности интеграции между собой для создания наиболее перспективной системы управления протезами.</p></abstract><trans-abstract xml:lang="en"><p>People with disabilities in the enormous scientific-technological revolution hope that it will overshadow the provision of assistance and find suitable solutions for them to lead their normal lives. The intersection of sciences among themselves took into account the problem of physical disabilities and, in particular, the loss of both upper and lower limbs. Modern prostheses are the product of the intersection of science and the technological revolution, which are still in the ladders of modernity and development due to they contain operators that can be controlled by brain signals according to the principle of neurainterfaces. Neuroimaging techniques such as electromyography, functional infrared spectroscopy and electroencephalography are the superior methods of controlling these modern prostheses can be modelled on two functions, namely independent work and hybrid work. In light of these data the article takes upon itself these systems in their individual and hybrid states. In addition, this article indicates which of these techniques is the most worthy in creating the preferred system. The scope of the research methodology limited to neuroimaging techniques towards scenarios of neurological rehabilitation and restoration of lost functions. The review has three axes. The first axis collects, summarizes and evaluates information from relevant studies published over the last decade. The second axis presents important results from previous experimental results in this field in relation to current research. This study was systematically conducted to provide a rich image and evidence-based evidence of prosthetic control techniques to all experts and scientists. The third axis is to identify a wide area of knowledge that requires further investigation, and follow-up the succession of scientific events of these systems towards the possibility of integration among themselves to create the most promising system for controlling prostheses.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>инвалидность</kwd><kwd>электроэнцефалография</kwd><kwd>электромиография</kwd><kwd>функциональная инфракрасная спектроскопия</kwd><kwd>гибридный интерфейс мозгкомпьютер</kwd><kwd>система управления</kwd><kwd>операторы</kwd><kwd>протезы</kwd></kwd-group><kwd-group xml:lang="en"><kwd>disability</kwd><kwd>electroencephalography</kwd><kwd>electromyography</kwd><kwd>functional near infrared spectroscopy</kwd><kwd>hybrid brain-computer interface</kwd><kwd>control system</kwd><kwd>operators</kwd><kwd>prostheses</kwd></kwd-group><funding-group xml:lang="en"><funding-statement>The work is carried out with the financial support of the Ministry of Education and Science of the Russian Federation within the framework of the federal project «Training of Personnel and Scientific Foundation for Electronic Industry» of the Russian Federation State Programme «Scientific and Technological Development of the Russian Federation». Subsidy Agreement No. 075-02-2024-1533.</funding-statement></funding-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Neelum Y. S., Zareena K., syed Usama A. fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees // Sensors. 2022. Vol. 22. 726. DOI: 10.3390/s22030726.</mixed-citation><mixed-citation xml:lang="en">Neelum Y. S., Zareena K., syed Usama A. fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees // Sensors. 2022. Vol. 22. 726. DOI: 10.3390/s22030726.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Asadullayev R. G., Afonin A. N., Shchetinina E. S. Recognition of patterns of motor activity by a neural network based on continuous optical tomography FNIRS data // Economics. Information technologies. 2021.Vol. 48, no. 4 P. 735–746. DOI: 10.52575/2687-0932-2021-48-4-735-746. EDN: NFDBUX.</mixed-citation><mixed-citation xml:lang="en">Asadullayev R. G., Afonin A. N., Shchetinina E. S. Recognition of patterns of motor activity by a neural network based on continuous optical tomography FNIRS data // Economics. Information technologies. 2021.Vol. 48, no. 4 P. 735–746. DOI: 10.52575/2687-0932-2021-48-4-735-746. EDN: NFDBUX.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Hramov A. E., Maksimenko V. A., Pisarchik A. N. Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain state // Physics Reports. 2021. Vol. 918. P. 1–133. DOI: 10.1016/j.physrep.2021.03.002.</mixed-citation><mixed-citation xml:lang="en">Hramov A. E., Maksimenko V. A., Pisarchik A. N. Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain state // Physics Reports. 2021. Vol. 918. P. 1–133. DOI: 10.1016/j.physrep.2021.03.002.</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Peksa J., Mamchur D. State-of-the-Art on Brain-Computer Interface Technology // Sensors 2023. Vol. 23. 6001. DOI: 10.3390/s23136001.</mixed-citation><mixed-citation xml:lang="en">Peksa J., Mamchur D. State-of-the-Art on Brain-Computer Interface Technology // Sensors 2023. Vol. 23. 6001. DOI: 10.3390/s23136001.</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Sergio L. N., Alex C. C., Forti R. M. [et al.]. Revealing the spatiotemporal requirements for accurate subject identification with resting-state functional connectivity: a simultaneous fNIRSfMRI study // Neurophotonics. 2023. Vol. 10(1). DOI: 10.1117/1. NPh.10.1.013510.</mixed-citation><mixed-citation xml:lang="en">Sergio L. N., Alex C. C., Forti R. M. [et al.]. Revealing the spatiotemporal requirements for accurate subject identification with resting-state functional connectivity: a simultaneous fNIRSfMRI study // Neurophotonics. 2023. Vol. 10(1). DOI: 10.1117/1. NPh.10.1.013510.</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Klein F. S., Debener K. W., Kranczioch C. fMRI-based validation of continuous-wave fNIRS of supplementary motor area activation during motor execution and motor imagery // Scientific Reports. 2022. Vol. 12 (1). DOI: 10.1038/s41598-022-06519-7.</mixed-citation><mixed-citation xml:lang="en">Klein F. S., Debener K. W., Kranczioch C. fMRI-based validation of continuous-wave fNIRS of supplementary motor area activation during motor execution and motor imagery // Scientific Reports. 2022. Vol. 12 (1). DOI: 10.1038/s41598-022-06519-7.</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Deligani R. J., Borgheai S. B., McLinden J. [et al.]. Multimodal fusion of EEG-fNIRS: a mutual information-based hybrid classification framework // Biomed Opt Express. 2021. Vol. 12 (3). 1635. DOI: 10.1364/boe.413666.</mixed-citation><mixed-citation xml:lang="en">Deligani R. J., Borgheai S. B., McLinden J. [et al.]. Multimodal fusion of EEG-fNIRS: a mutual information-based hybrid classification framework // Biomed Opt Express. 2021. Vol. 12 (3). 1635. DOI: 10.1364/boe.413666.</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Asanza V., Pelaez E., Loayza F. [et al.]. Identification of lower-limb motor tasks via brain-computer interfaces: a topical overview // Sensors. 2022. Vol. 22 (5). DOI: 10.3390/s22052028.</mixed-citation><mixed-citation xml:lang="en">Asanza V., Pelaez E., Loayza F. [et al.]. Identification of lower-limb motor tasks via brain-computer interfaces: a topical overview // Sensors. 2022. Vol. 22 (5). DOI: 10.3390/s22052028.</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Khajuria A., Sharma R., Joshi D. EEG Dynamics of Locomotion and Balancing: Solution to Neuro-Rehabilitation // Clinical EEG and Neuroscience. 2024. Vol. 55 (1). P. 143–163. DOI: 10.1177/15500594221123690.</mixed-citation><mixed-citation xml:lang="en">Khajuria A., Sharma R., Joshi D. EEG Dynamics of Locomotion and Balancing: Solution to Neuro-Rehabilitation // Clinical EEG and Neuroscience. 2024. Vol. 55 (1). P. 143–163. DOI: 10.1177/15500594221123690.</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Mondini V., Sburlea A. I., Müller-Putz G. R. Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing // Scientific Reports. 2024. Vol. 14. 4714. DOI: 10.1038/s41598-024-55413-x.</mixed-citation><mixed-citation xml:lang="en">Mondini V., Sburlea A. I., Müller-Putz G. R. Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing // Scientific Reports. 2024. Vol. 14. 4714. DOI: 10.1038/s41598-024-55413-x.</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Usama A. S., Zareena K., Neelum Y. S. Control of a Prosthetic Arm using fNIRS, A Neural-Machine Interface // Data Acquisition – Recent Advances and Applications in Biomedical Engineering. 2020. DOI: 10.5772/intechopen.93565.</mixed-citation><mixed-citation xml:lang="en">Usama A. S., Zareena K., Neelum Y. S. Control of a Prosthetic Arm using fNIRS, A Neural-Machine Interface // Data Acquisition – Recent Advances and Applications in Biomedical Engineering. 2020. DOI: 10.5772/intechopen.93565.</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Afonin A. N., Asadullaev R. G., Sitnikova M. A. Analysis of data of FNIRS-tomograph for management of LIMB-protoses by means of Brain-computer interface // Scientific and Technical Volga region Bulletin. 2018. Vol. 11. P. 182–185. EDN: YTOMIP.</mixed-citation><mixed-citation xml:lang="en">Afonin A. N., Asadullaev R. G., Sitnikova M. A. Analysis of data of FNIRS-tomograph for management of LIMB-protoses by means of Brain-computer interface // Scientific and Technical Volga region Bulletin. 2018. Vol. 11. P. 182–185. EDN: YTOMIP.</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Dario F., Ning J., Hubertus R. [et al.]. The extraction of neural information from the surface EMG for the control of upperlimb prostheses: Emerging avenues and challenges // IEEE Trans. Neural Syst. Rehabil. Eng. 2014. Vol. 22, no. 4. P. 797–809. DOI: 10.1109/TNSRE.2014.2305111.</mixed-citation><mixed-citation xml:lang="en">Dario F., Ning J., Hubertus R. [et al.]. The extraction of neural information from the surface EMG for the control of upperlimb prostheses: Emerging avenues and challenges // IEEE Trans. Neural Syst. Rehabil. Eng. 2014. Vol. 22, no. 4. P. 797–809. DOI: 10.1109/TNSRE.2014.2305111.</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Becerra-Fajardo L., Minguillon J., Krob M. O. [et al.]. Firstin-human demonstration of floating EMG sensors and stimulators wirelessly powered and operated by volume conduction // Journal of NeuroEngineering and Rehabilitation. 2024. Vol. 21 (1). 4. DOI: 10.1186/s12984-023-01295-5.</mixed-citation><mixed-citation xml:lang="en">Becerra-Fajardo L., Minguillon J., Krob M. O. [et al.]. Firstin-human demonstration of floating EMG sensors and stimulators wirelessly powered and operated by volume conduction // Journal of NeuroEngineering and Rehabilitation. 2024. Vol. 21 (1). 4. DOI: 10.1186/s12984-023-01295-5.</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Osama M., Allauddin U. Design and modelling of lower prosthetic limb for additive manufacturing // Proceedings of IMEC-2022, 14th – 15th January, Karachi, Pakistan. 2022. 8 p.</mixed-citation><mixed-citation xml:lang="en">Osama M., Allauddin U. Design and modelling of lower prosthetic limb for additive manufacturing // Proceedings of IMEC-2022, 14th – 15th January, Karachi, Pakistan. 2022. 8 p.</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Satam I. A. A comprehensive study of EEG-based41control of artificial arms // Vojnotehnički glasnik / Military Technical Courier. 2023. Vol. 71, Issue 1. DOI: 10.5937/vojtehg71-41366.</mixed-citation><mixed-citation xml:lang="en">Satam I. A. A comprehensive study of EEG-based41control of artificial arms // Vojnotehnički glasnik / Military Technical Courier. 2023. Vol. 71, Issue 1. DOI: 10.5937/vojtehg71-41366.</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Tao S., Zhe Y., Guo Sh. [et al.] Review of sEMG for Robot Control: Techniques and Applicationsby // Applied Sciences. 2023.Vol. 13 (17). 9546. DOI: 10.3390/app13179546.</mixed-citation><mixed-citation xml:lang="en">Tao S., Zhe Y., Guo Sh. [et al.] Review of sEMG for Robot Control: Techniques and Applicationsby // Applied Sciences. 2023.Vol. 13 (17). 9546. DOI: 10.3390/app13179546.</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Khorasani A., Hulsizer J., Paul V. [et al.]. Myoelectric interface for neurorehabilitation conditioning to reduce abnormal leg co activation after stroke: a pilot study // Journal NeuroEngineering Rehabil. 2024. Vol. 21. 11. DOI: 10.1186/s12984-024-01305-0.</mixed-citation><mixed-citation xml:lang="en">Khorasani A., Hulsizer J., Paul V. [et al.]. Myoelectric interface for neurorehabilitation conditioning to reduce abnormal leg co activation after stroke: a pilot study // Journal NeuroEngineering Rehabil. 2024. Vol. 21. 11. DOI: 10.1186/s12984-024-01305-0.</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Asanza V., Pelaez E., Loayza F. [et al.]. Identification of lower-limb motor tasks via brain-computer interfaces: a topical overview. Sensors. 2022. Vol. 22 (5). 2028. DOI: 10.3390/s22052028.</mixed-citation><mixed-citation xml:lang="en">Asanza V., Pelaez E., Loayza F. [et al.]. Identification of lower-limb motor tasks via brain-computer interfaces: a topical overview. Sensors. 2022. Vol. 22 (5). 2028. DOI: 10.3390/s22052028.</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Abdalmalak A., Milej D., Cohenet D. [et al.]. Using fMRI to investigate the potential cause of inverse oxygenation reported in fNIRS studies of motor imagery // Neurosci Lett. 2020. Vol. 714. 134607. DOI: 10.1016/j.neulet.2019.134607.</mixed-citation><mixed-citation xml:lang="en">Abdalmalak A., Milej D., Cohenet D. [et al.]. Using fMRI to investigate the potential cause of inverse oxygenation reported in fNIRS studies of motor imagery // Neurosci Lett. 2020. Vol. 714. 134607. DOI: 10.1016/j.neulet.2019.134607.</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Wang H., Yan F., Xu T. [et al.]. Brain-Controlled Wheelchair Review: From Wet Electrode to Dry Electrode, from Single Modal to Hybrid Modal, from Synchronous to Asynchronous // IEEE Access. 2021. Vol. 9. P. 55920–55938. DOI: 10.1109/ACCESS.2021.3071599.</mixed-citation><mixed-citation xml:lang="en">Wang H., Yan F., Xu T. [et al.]. Brain-Controlled Wheelchair Review: From Wet Electrode to Dry Electrode, from Single Modal to Hybrid Modal, from Synchronous to Asynchronous // IEEE Access. 2021. Vol. 9. P. 55920–55938. DOI: 10.1109/ACCESS.2021.3071599.</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">Xu B., Wenlong L., Deping L. [et al.]. Continuous Hybrid BCI Control for Robotic Arm Using Noninvasive Electroencephalogram, Computer Vision, and Eye Tracking // Mathematics. 2022. Vol. 10, no. 4. DOI: 10.3390/math10040618.</mixed-citation><mixed-citation xml:lang="en">Xu B., Wenlong L., Deping L. [et al.]. Continuous Hybrid BCI Control for Robotic Arm Using Noninvasive Electroencephalogram, Computer Vision, and Eye Tracking // Mathematics. 2022. Vol. 10, no. 4. DOI: 10.3390/math10040618.</mixed-citation></citation-alternatives></ref><ref id="cit23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">Sun Z., H. Zihao, D. Feng [et al.]. A Novel Multimodal Approach for Hybrid Brain-Computer Interface // IEEE Access. 2020. Vol. 8. P. 89909–89918. DOI: 10.1109/ACCESS.2020.2994226.</mixed-citation><mixed-citation xml:lang="en">Sun Z., H. Zihao, D. Feng [et al.]. A Novel Multimodal Approach for Hybrid Brain-Computer Interface // IEEE Access. 2020. Vol. 8. P. 89909–89918. DOI: 10.1109/ACCESS.2020.2994226.</mixed-citation></citation-alternatives></ref><ref id="cit24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">Pichiorri F., Toppi J., de Seta V. [et al.]. Exploring high density corticomuscular networks after stroke to enable a hybrid Brain Computer Interface for hand motor rehabilitation // Journal of NeuroEngineering and Rehabilitation. 2023. Vol. 20(1). DOI: 10.1186/s12984-023-01127-6.</mixed-citation><mixed-citation xml:lang="en">Pichiorri F., Toppi J., de Seta V. [et al.]. Exploring high density corticomuscular networks after stroke to enable a hybrid Brain Computer Interface for hand motor rehabilitation // Journal of NeuroEngineering and Rehabilitation. 2023. Vol. 20(1). DOI: 10.1186/s12984-023-01127-6.</mixed-citation></citation-alternatives></ref><ref id="cit25"><label>25</label><citation-alternatives><mixed-citation xml:lang="ru">Si J., Yang Y., Xu L. [et al.]. Evaluation of residual cognition in patients with disorders of consciousness based on functional near-infrared spectroscopy // Neurophotonics. 2023. Vol. 10, no. 2. DOI: 10.1117/1.nph.10.2.025003.</mixed-citation><mixed-citation xml:lang="en">Si J., Yang Y., Xu L. [et al.]. Evaluation of residual cognition in patients with disorders of consciousness based on functional near-infrared spectroscopy // Neurophotonics. 2023. Vol. 10, no. 2. DOI: 10.1117/1.nph.10.2.025003.</mixed-citation></citation-alternatives></ref><ref id="cit26"><label>26</label><citation-alternatives><mixed-citation xml:lang="ru">Hamid H., Naseer N., Nazeer H. [et al.]. Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks // Sensors. Vol. 22 (5). 1932. DOI: 10.3390/s22051932,2022.</mixed-citation><mixed-citation xml:lang="en">Hamid H., Naseer N., Nazeer H. [et al.]. Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks // Sensors. Vol. 22 (5). 1932. DOI: 10.3390/s22051932,2022.</mixed-citation></citation-alternatives></ref><ref id="cit27"><label>27</label><citation-alternatives><mixed-citation xml:lang="ru">Mustafa A. H. H., Muhammad U. K., Deepti M. A Computationally Efficient Method for Hybrid EEG-fNIRS BCI Based on the Pearson Correlation // BioMed Research International. 2020. Vol. 2020. 1838140. 13 p. DOI: 10.1155/2020/1838140.</mixed-citation><mixed-citation xml:lang="en">Mustafa A. H. H., Muhammad U. K., Deepti M. A Computationally Efficient Method for Hybrid EEG-fNIRS BCI Based on the Pearson Correlation // BioMed Research International. 2020. Vol. 2020. 1838140. 13 p. DOI: 10.1155/2020/1838140.</mixed-citation></citation-alternatives></ref><ref id="cit28"><label>28</label><citation-alternatives><mixed-citation xml:lang="ru">Sial M. B.,Wang S., Wang X. [et al.]. A Survey on EEG – fNIRS based Non-invasive hBCIs // 2021 International Conference on Artificial Intelligence (ICAI). 2021. P. 240–245. DOI: 10.1109/ICAI52203.2021.9445246.</mixed-citation><mixed-citation xml:lang="en">Sial M. B.,Wang S., Wang X. [et al.]. A Survey on EEG – fNIRS based Non-invasive hBCIs // 2021 International Conference on Artificial Intelligence (ICAI). 2021. P. 240–245. DOI: 10.1109/ICAI52203.2021.9445246.</mixed-citation></citation-alternatives></ref><ref id="cit29"><label>29</label><citation-alternatives><mixed-citation xml:lang="ru">Wang Z., Lu Y., Yijie Z. [et al.]. Incorporating EEG and fNIRS Patterns to Evaluate Cortical Excitability and MI-BCI Performance During Motor Training // IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2023. Vol. 31. P. 2872–2882. DOI: 10.1109/TNSRE.2023.3281855.</mixed-citation><mixed-citation xml:lang="en">Wang Z., Lu Y., Yijie Z. [et al.]. Incorporating EEG and fNIRS Patterns to Evaluate Cortical Excitability and MI-BCI Performance During Motor Training // IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2023. Vol. 31. P. 2872–2882. DOI: 10.1109/TNSRE.2023.3281855.</mixed-citation></citation-alternatives></ref><ref id="cit30"><label>30</label><citation-alternatives><mixed-citation xml:lang="ru">Radha H. M., Karim A., Ali Al-Timemy H. University of Baghdad [et al.]. Recognition of Upper Limb Movements Based on Hybrid EEG and EMG Signals for Human-Robot Interaction // Iraqi Journal of Computer Communication Control and System Engineering. 2023. Vol. 23, no. 2. DOI: 10.33103/uot.ijccce. 23.2.14.</mixed-citation><mixed-citation xml:lang="en">Radha H. M., Karim A., Ali Al-Timemy H. University of Baghdad [et al.]. Recognition of Upper Limb Movements Based on Hybrid EEG and EMG Signals for Human-Robot Interaction // Iraqi Journal of Computer Communication Control and System Engineering. 2023. Vol. 23, no. 2. DOI: 10.33103/uot.ijccce. 23.2.14.</mixed-citation></citation-alternatives></ref><ref id="cit31"><label>31</label><citation-alternatives><mixed-citation xml:lang="ru">Lubo F., Haoyang L., Hongfei J. [et al.]. EEG-EMG analysis method in hybrid brain computer interface for hand rehabilitation training // Computing and Informatics. 2023. Vol. 42 (3). P. 741–761. DOI: 10.31577/cai_2023_3_741.</mixed-citation><mixed-citation xml:lang="en">Lubo F., Haoyang L., Hongfei J. [et al.]. EEG-EMG analysis method in hybrid brain computer interface for hand rehabilitation training // Computing and Informatics. 2023. Vol. 42 (3). P. 741–761. DOI: 10.31577/cai_2023_3_741.</mixed-citation></citation-alternatives></ref><ref id="cit32"><label>32</label><citation-alternatives><mixed-citation xml:lang="ru">Kwon J., Shin J., Im C. H. Toward a compact hybrid braincomputer interface (BCI): Performance evaluation of multi-class hybrid EEG-fNIRS BCIs with limited number of channels // PLoS One. 2020. Vol. 15, no. 3. DOI: 10.1371/journal.pone.0230491.</mixed-citation><mixed-citation xml:lang="en">Kwon J., Shin J., Im C. H. Toward a compact hybrid braincomputer interface (BCI): Performance evaluation of multi-class hybrid EEG-fNIRS BCIs with limited number of channels // PLoS One. 2020. Vol. 15, no. 3. DOI: 10.1371/journal.pone.0230491.</mixed-citation></citation-alternatives></ref><ref id="cit33"><label>33</label><citation-alternatives><mixed-citation xml:lang="ru">Beniczky S., Donald L. S. Electroencephalography: basic biophysical and technological aspects important for clinical applications // Epileptic Disord. 2020. Vol. 22, no. 6. DOI: 10.1684/epd.2020.1217.</mixed-citation><mixed-citation xml:lang="en">Beniczky S., Donald L. S. Electroencephalography: basic biophysical and technological aspects important for clinical applications // Epileptic Disord. 2020. Vol. 22, no. 6. DOI: 10.1684/epd.2020.1217.</mixed-citation></citation-alternatives></ref><ref id="cit34"><label>34</label><citation-alternatives><mixed-citation xml:lang="ru">Marius V. D., Hadăr A., Goga N. [et al.]. Design and implementation of an eeg-based bci prosthetic lower limb using raspberry PI 4 // U.P.B. Sci. Bull., Series C. 2023. Vol. 85, Issue. 3. P. 353–366.</mixed-citation><mixed-citation xml:lang="en">Marius V. D., Hadăr A., Goga N. [et al.]. Design and implementation of an eeg-based bci prosthetic lower limb using raspberry PI 4 // U.P.B. Sci. Bull., Series C. 2023. Vol. 85, Issue. 3. P. 353–366.</mixed-citation></citation-alternatives></ref><ref id="cit35"><label>35</label><citation-alternatives><mixed-citation xml:lang="ru">He L., Guo Sh., Bu D. [et al.]. Subject-Independent Estimation of Continuous Movements Using CNN-LSTM for a Home-Based Upper Limb Rehabilitation System // IEEE Robotics and Automation Letters. 2023. P. 1–8. DOI: 10.1109/LRA.2023.3303701.</mixed-citation><mixed-citation xml:lang="en">He L., Guo Sh., Bu D. [et al.]. Subject-Independent Estimation of Continuous Movements Using CNN-LSTM for a Home-Based Upper Limb Rehabilitation System // IEEE Robotics and Automation Letters. 2023. P. 1–8. DOI: 10.1109/LRA.2023.3303701.</mixed-citation></citation-alternatives></ref><ref id="cit36"><label>36</label><citation-alternatives><mixed-citation xml:lang="ru">Jin H. Li C., Sun L., Hu H. [et al.]. To classify twodimensional motion state of step length and walking speed by applying cerebral hemoglobin information // 2017 10th International Conference on Human System Interactions (HSI). 2017. P. 216–222. DOI: 10.1109/HSI.2017.8005032.</mixed-citation><mixed-citation xml:lang="en">Jin H. Li C., Sun L., Hu H. [et al.]. To classify twodimensional motion state of step length and walking speed by applying cerebral hemoglobin information // 2017 10th International Conference on Human System Interactions (HSI). 2017. P. 216–222. DOI: 10.1109/HSI.2017.8005032.</mixed-citation></citation-alternatives></ref><ref id="cit37"><label>37</label><citation-alternatives><mixed-citation xml:lang="ru">Yang L., Song Y., Ma K. [et al.]. A novel motor imagery EEG decoding method based on feature separation // Journal of Neural Engineering. 2021. Vol. 18. 036022. DOI:10.1088/1741-2552/abe39b.</mixed-citation><mixed-citation xml:lang="en">Yang L., Song Y., Ma K. [et al.]. A novel motor imagery EEG decoding method based on feature separation // Journal of Neural Engineering. 2021. Vol. 18. 036022. DOI:10.1088/1741-2552/abe39b.</mixed-citation></citation-alternatives></ref><ref id="cit38"><label>38</label><citation-alternatives><mixed-citation xml:lang="ru">Milanes D. H., Codorniu R. T., Baracaldo R. [et al.]. Shallow Convolutional Network Excel for Classifying Motor Imagery EEG in BCI Applications // IEEE Access. 2021. Vol. 9. P. 98275–98286. DOI: 10.1109/ACCESS.2021.3091399.</mixed-citation><mixed-citation xml:lang="en">Milanes D. H., Codorniu R. T., Baracaldo R. [et al.]. Shallow Convolutional Network Excel for Classifying Motor Imagery EEG in BCI Applications // IEEE Access. 2021. Vol. 9. P. 98275–98286. DOI: 10.1109/ACCESS.2021.3091399.</mixed-citation></citation-alternatives></ref><ref id="cit39"><label>39</label><citation-alternatives><mixed-citation xml:lang="ru">Aydin E. A. Subject-specific feature selection for near infrared spectroscopy based brain–computer interfaces // Computer Methods and Programs in Biomedicine. 2020. Vol. 195 (12). 105535. DOI: 10.1016/j.cmpb.2020.105535.</mixed-citation><mixed-citation xml:lang="en">Aydin E. A. Subject-specific feature selection for near infrared spectroscopy based brain–computer interfaces // Computer Methods and Programs in Biomedicine. 2020. Vol. 195 (12). 105535. DOI: 10.1016/j.cmpb.2020.105535.</mixed-citation></citation-alternatives></ref><ref id="cit40"><label>40</label><citation-alternatives><mixed-citation xml:lang="ru">Bin Abdul Ghaffar M. S., Khan U. S., Naseer N. [et al.]. Improved Classification Accuracy of Four Class FNIRS-BCI // 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). 2020. P. 1–5. DOI: 10.1109/ECAI50035.2020.9223258.</mixed-citation><mixed-citation xml:lang="en">Bin Abdul Ghaffar M. S., Khan U. S., Naseer N. [et al.]. Improved Classification Accuracy of Four Class FNIRS-BCI // 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). 2020. P. 1–5. DOI: 10.1109/ECAI50035.2020.9223258.</mixed-citation></citation-alternatives></ref><ref id="cit41"><label>41</label><citation-alternatives><mixed-citation xml:lang="ru">Guo W. C., Zhang X., Liu H. [et al.]. Toward an enhanced human machine interface for upper-limb prosthesis control with combined EMG and NIRS signals // IEEE Trans. HumanMach. Syst. 2017. Vol. 47, no. 4. P. 1–12. 564575. DOI: 10.1109/THMS.2016.2641389.</mixed-citation><mixed-citation xml:lang="en">Guo W. C., Zhang X., Liu H. [et al.]. Toward an enhanced human machine interface for upper-limb prosthesis control with combined EMG and NIRS signals // IEEE Trans. HumanMach. Syst. 2017. Vol. 47, no. 4. P. 1–12. 564575. DOI: 10.1109/THMS.2016.2641389.</mixed-citation></citation-alternatives></ref><ref id="cit42"><label>42</label><citation-alternatives><mixed-citation xml:lang="ru">Lin J. F. L. Dual-MEG interbrain synchronization during turn-taking verbal interactions between mothers and children // Cerebral Cortex. 2023. Vol. 33 (7). P. 4116–4134. DOI: 10.1093/cercor/bhac330.</mixed-citation><mixed-citation xml:lang="en">Lin J. F. L. Dual-MEG interbrain synchronization during turn-taking verbal interactions between mothers and children // Cerebral Cortex. 2023. Vol. 33 (7). P. 4116–4134. DOI: 10.1093/cercor/bhac330.</mixed-citation></citation-alternatives></ref><ref id="cit43"><label>43</label><citation-alternatives><mixed-citation xml:lang="ru">Maher A., Salankar N., Qaisar S. M. [et al.]. Hybrid EEGfNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learning // Journal of Applied Biomedicine. 2023. Vol. 43 (1). P. 463–475. DOI: DOI:10.1016/j.bbe.2023.05.001.</mixed-citation><mixed-citation xml:lang="en">Maher A., Salankar N., Qaisar S. M. [et al.]. Hybrid EEGfNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learning // Journal of Applied Biomedicine. 2023. Vol. 43 (1). P. 463–475. DOI: DOI:10.1016/j.bbe.2023.05.001.</mixed-citation></citation-alternatives></ref><ref id="cit44"><label>44</label><citation-alternatives><mixed-citation xml:lang="ru">Liu Z., Shore J., Wang M. [et al.]. A systematic review on hybrid EEG/fNIRS in brain-computer interface // Biomed Signal Process Control. 2021. Vol. 68. 102595. DOI:10.1016/j.bspc.2021.102595.</mixed-citation><mixed-citation xml:lang="en">Liu Z., Shore J., Wang M. [et al.]. A systematic review on hybrid EEG/fNIRS in brain-computer interface // Biomed Signal Process Control. 2021. Vol. 68. 102595. DOI:10.1016/j.bspc.2021.102595.</mixed-citation></citation-alternatives></ref><ref id="cit45"><label>45</label><citation-alternatives><mixed-citation xml:lang="ru">Xu T., Yang Y., Zhou Zh. [et al.]. Motor Imagery Decoding Enhancement Based on Hybrid EEG–fNIRS Signals // IEEE Access. 2023. Vol. 1(1). P. 1–12. DOI: 10.1109/ACCESS.2023.3289709.</mixed-citation><mixed-citation xml:lang="en">Xu T., Yang Y., Zhou Zh. [et al.]. Motor Imagery Decoding Enhancement Based on Hybrid EEG–fNIRS Signals // IEEE Access. 2023. Vol. 1(1). P. 1–12. DOI: 10.1109/ACCESS.2023.3289709.</mixed-citation></citation-alternatives></ref><ref id="cit46"><label>46</label><citation-alternatives><mixed-citation xml:lang="ru">Li R., Yang D., Fang F. [et al.]. Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, MethodologyFocused Review // Sensors. 2022. Vol. 22, no. 15. 5865. DOI: 10.3390/s22155865.</mixed-citation><mixed-citation xml:lang="en">Li R., Yang D., Fang F. [et al.]. Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, MethodologyFocused Review // Sensors. 2022. Vol. 22, no. 15. 5865. DOI: 10.3390/s22155865.</mixed-citation></citation-alternatives></ref><ref id="cit47"><label>47</label><citation-alternatives><mixed-citation xml:lang="ru">Chunfu L., Ruite G., Zhichuan T. [et al.]. Multi-channel FES gait rehabilitation assistance system based on adaptive sEMG modulation // IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2023. Vol. 31. P. 3652–3663. DOI: 10.1109/tnsre.2023.3313617.</mixed-citation><mixed-citation xml:lang="en">Chunfu L., Ruite G., Zhichuan T. [et al.]. Multi-channel FES gait rehabilitation assistance system based on adaptive sEMG modulation // IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2023. Vol. 31. P. 3652–3663. DOI: 10.1109/tnsre.2023.3313617.</mixed-citation></citation-alternatives></ref><ref id="cit48"><label>48</label><citation-alternatives><mixed-citation xml:lang="ru">Song T., Yan Z., Guo S. [et al.]. Review of sEMG for Robot Control: Techniques and Applications // Applied Sciences. 2023. Vol. 13, no. 17. DOI: 10.3390/app13179546.</mixed-citation><mixed-citation xml:lang="en">Song T., Yan Z., Guo S. [et al.]. Review of sEMG for Robot Control: Techniques and Applications // Applied Sciences. 2023. Vol. 13, no. 17. DOI: 10.3390/app13179546.</mixed-citation></citation-alternatives></ref><ref id="cit49"><label>49</label><citation-alternatives><mixed-citation xml:lang="ru">Radek M., Martina L., Michaela S. [et al.]. Advanced bioelectrical signal processing methods: Past, present, and future approach — Part III: Other biosignals // Sensors. 2021. Vol. 21 (18). 6064. DOI: 10.3390/s21186064.</mixed-citation><mixed-citation xml:lang="en">Radek M., Martina L., Michaela S. [et al.]. Advanced bioelectrical signal processing methods: Past, present, and future approach — Part III: Other biosignals // Sensors. 2021. Vol. 21 (18). 6064. DOI: 10.3390/s21186064.</mixed-citation></citation-alternatives></ref><ref id="cit50"><label>50</label><citation-alternatives><mixed-citation xml:lang="ru">Cheng X., Sie E. J., Boas D. A. [et al.]. Choosing an optimal wavelength to detect brain activity in functional nearinfrared spectroscopy // Optics Letters. 2021. Vol. 46 (4). 924. DOI: 10.1364/ol.418284.</mixed-citation><mixed-citation xml:lang="en">Cheng X., Sie E. J., Boas D. A. [et al.]. Choosing an optimal wavelength to detect brain activity in functional nearinfrared spectroscopy // Optics Letters. 2021. Vol. 46 (4). 924. DOI: 10.1364/ol.418284.</mixed-citation></citation-alternatives></ref><ref id="cit51"><label>51</label><citation-alternatives><mixed-citation xml:lang="ru">Kimoto H. F., Machida M. A wireless multi-layered EMG/ MMG/NIRS sensor for muscular activity evaluation // Sensors. 2023. Vol. 23 (3). 1539. DOI: 10.3390/s23031539.</mixed-citation><mixed-citation xml:lang="en">Kimoto H. F., Machida M. A wireless multi-layered EMG/ MMG/NIRS sensor for muscular activity evaluation // Sensors. 2023. Vol. 23 (3). 1539. DOI: 10.3390/s23031539.</mixed-citation></citation-alternatives></ref><ref id="cit52"><label>52</label><citation-alternatives><mixed-citation xml:lang="ru">Giminiani R. D., Marco C., Marco F. [et al.]. Validation of fabric-based thigh-wearable EMG sensors and oximetry for monitoring quadricep activity during strength and endurance exercises // Sensors. 2020. Vol. 17. P. 1–13. 4664. DOI: 10.3390/s20174664.</mixed-citation><mixed-citation xml:lang="en">Giminiani R. D., Marco C., Marco F. [et al.]. Validation of fabric-based thigh-wearable EMG sensors and oximetry for monitoring quadricep activity during strength and endurance exercises // Sensors. 2020. Vol. 17. P. 1–13. 4664. DOI: 10.3390/s20174664.</mixed-citation></citation-alternatives></ref><ref id="cit53"><label>53</label><citation-alternatives><mixed-citation xml:lang="ru">Daniel N., Sybilski K., Kaczmarek W. [et al.]. Relationship between EMG and fNIRS during Dynamic Movements // Sensors. 2023. Vol. 23 (11). 5004. DOI: 10.3390/s23115004.</mixed-citation><mixed-citation xml:lang="en">Daniel N., Sybilski K., Kaczmarek W. [et al.]. Relationship between EMG and fNIRS during Dynamic Movements // Sensors. 2023. Vol. 23 (11). 5004. DOI: 10.3390/s23115004.</mixed-citation></citation-alternatives></ref><ref id="cit54"><label>54</label><citation-alternatives><mixed-citation xml:lang="ru">Atzori M., Gijsberts A., Castellini C. [et al.]. Electromyography data for non-invasive naturally-controlled robotic hand prostheses // Nature. 2014. Vol. 1. DOI: 10.1038/sdata.2014.53.</mixed-citation><mixed-citation xml:lang="en">Atzori M., Gijsberts A., Castellini C. [et al.]. Electromyography data for non-invasive naturally-controlled robotic hand prostheses // Nature. 2014. Vol. 1. DOI: 10.1038/sdata.2014.53.</mixed-citation></citation-alternatives></ref><ref id="cit55"><label>55</label><citation-alternatives><mixed-citation xml:lang="ru">Dario F., Ning J., Hubertus R. [et al.]. The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges // IEEE Trans. Neural Syst. Rehabil. Eng. 2014. Vol. 22, no. 4. P. 797–809. DOI:10.1109/TNSRE.2014.2305111.</mixed-citation><mixed-citation xml:lang="en">Dario F., Ning J., Hubertus R. [et al.]. The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges // IEEE Trans. Neural Syst. Rehabil. Eng. 2014. Vol. 22, no. 4. P. 797–809. DOI:10.1109/TNSRE.2014.2305111.</mixed-citation></citation-alternatives></ref><ref id="cit56"><label>56</label><citation-alternatives><mixed-citation xml:lang="ru">Arif A., Khan M., Kashif J. [et al.]. Hemodynamic response detection using integrated EEG–fNIRS-VPA for BCI // Computers, Materials and Continua. 2021. Vol. 70., no. 1. P. 535– 555. DOI: 10.32604/cmc.2022.018318.</mixed-citation><mixed-citation xml:lang="en">Arif A., Khan M., Kashif J. [et al.]. Hemodynamic response detection using integrated EEG–fNIRS-VPA for BCI // Computers, Materials and Continua. 2021. Vol. 70., no. 1. P. 535– 555. DOI: 10.32604/cmc.2022.018318.</mixed-citation></citation-alternatives></ref><ref id="cit57"><label>57</label><citation-alternatives><mixed-citation xml:lang="ru">Kwak Y., Song W. J., Kim S. E. FGANet: FNIRSGuided Attention Network for Hybrid EEG–fNIRS BrainComputer Interfaces // IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2022. Vol. 30. P. 329–339. DOI: 10.1109/TNSRE.2022.3149899.</mixed-citation><mixed-citation xml:lang="en">Kwak Y., Song W. J., Kim S. E. FGANet: FNIRSGuided Attention Network for Hybrid EEG–fNIRS BrainComputer Interfaces // IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2022. Vol. 30. P. 329–339. DOI: 10.1109/TNSRE.2022.3149899.</mixed-citation></citation-alternatives></ref><ref id="cit58"><label>58</label><citation-alternatives><mixed-citation xml:lang="ru">Neelum Y. S., Zareena K., Usama S. [et al.]. Enhancing classification accuracy of transhumeral prosthesis: a hybrid sEMG and fNIRS approach // IEEE Access. 2021. Vol. 9. P. 113246– 113257. DOI: 10.1109/ACCESS.2021.3099973.</mixed-citation><mixed-citation xml:lang="en">Neelum Y. S., Zareena K., Usama S. [et al.]. Enhancing classification accuracy of transhumeral prosthesis: a hybrid sEMG and fNIRS approach // IEEE Access. 2021. Vol. 9. P. 113246– 113257. DOI: 10.1109/ACCESS.2021.3099973.</mixed-citation></citation-alternatives></ref><ref id="cit59"><label>59</label><citation-alternatives><mixed-citation xml:lang="ru">Nsugbe E., Phillips C., Fraser M. F. [et al.]. Gesture recognition for transhumeral prosthesis control using EMG and NIR // IET Cyber-Systems and Robotics. 2020. Vol. 2, Issue 3. P. 122–131. DOI: 10.1049/iet-csr.2020.0008.</mixed-citation><mixed-citation xml:lang="en">Nsugbe E., Phillips C., Fraser M. F. [et al.]. Gesture recognition for transhumeral prosthesis control using EMG and NIR // IET Cyber-Systems and Robotics. 2020. Vol. 2, Issue 3. P. 122–131. DOI: 10.1049/iet-csr.2020.0008.</mixed-citation></citation-alternatives></ref><ref id="cit60"><label>60</label><citation-alternatives><mixed-citation xml:lang="ru">Xiang Z., Yao L., Wang X. [et al.]. A Survey on Deep Learning-based Non-Invasive Brain Signals: Recent Advances and New Frontiers // Journal of Neural Engineering. 2020. Vol. 18 (3). DOI:10.1088/1741-2552/abc902.</mixed-citation><mixed-citation xml:lang="en">Xiang Z., Yao L., Wang X. [et al.]. A Survey on Deep Learning-based Non-Invasive Brain Signals: Recent Advances and New Frontiers // Journal of Neural Engineering. 2020. Vol. 18 (3). DOI:10.1088/1741-2552/abc902.</mixed-citation></citation-alternatives></ref><ref id="cit61"><label>61</label><citation-alternatives><mixed-citation xml:lang="ru">Moufassih M., Tarahi O., Hamou S. [et al.]. Boosting motor imagery brain-computer interface classification using multiband and hybrid feature extraction // Multimedia Tools and Applications. 2023. Vol. 83 (16). P. 1–32. DOI: 10.1007/s11042-023-17118-7.</mixed-citation><mixed-citation xml:lang="en">Moufassih M., Tarahi O., Hamou S. [et al.]. Boosting motor imagery brain-computer interface classification using multiband and hybrid feature extraction // Multimedia Tools and Applications. 2023. Vol. 83 (16). P. 1–32. DOI: 10.1007/s11042-023-17118-7.</mixed-citation></citation-alternatives></ref><ref id="cit62"><label>62</label><citation-alternatives><mixed-citation xml:lang="ru">Shelishiyah R., Dharan M., Kumar T. [et al.]. Signal Processing for Hybrid BCI Signals // Journal of Physics Conference Series. 2022. Vol. 2318 (1). 012007. DOI: 10.1088/1742-6596/2318/1/012007.</mixed-citation><mixed-citation xml:lang="en">Shelishiyah R., Dharan M., Kumar T. [et al.]. Signal Processing for Hybrid BCI Signals // Journal of Physics Conference Series. 2022. Vol. 2318 (1). 012007. DOI: 10.1088/1742-6596/2318/1/012007.</mixed-citation></citation-alternatives></ref><ref id="cit63"><label>63</label><citation-alternatives><mixed-citation xml:lang="ru">Ali M. U., Kim K. S., Kallu K. D. [et al.]. OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS–Brain Computer Interface // Bioengineering. 2023. Vol. 10, no. 5. DOI: 10.3390/bioengineering10050608.</mixed-citation><mixed-citation xml:lang="en">Ali M. U., Kim K. S., Kallu K. D. [et al.]. OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS–Brain Computer Interface // Bioengineering. 2023. Vol. 10, no. 5. DOI: 10.3390/bioengineering10050608.</mixed-citation></citation-alternatives></ref><ref id="cit64"><label>64</label><citation-alternatives><mixed-citation xml:lang="ru">Brian F. S., Charles P., Christopher H. [et al.]. The evolution of neuromodulation for chronic stroke: From neuroplasticity mechanisms to brain-computer interfaces // Neurotherapeutics. 2024. Vol. 21, Issue 3. e00337. DOI 10.1016/j.neurot.2024.e00337.</mixed-citation><mixed-citation xml:lang="en">Brian F. S., Charles P., Christopher H. [et al.]. The evolution of neuromodulation for chronic stroke: From neuroplasticity mechanisms to brain-computer interfaces // Neurotherapeutics. 2024. Vol. 21, Issue 3. e00337. DOI 10.1016/j.neurot.2024.e00337.</mixed-citation></citation-alternatives></ref><ref id="cit65"><label>65</label><citation-alternatives><mixed-citation xml:lang="ru">Na L., Rui Z., Bharath K. [et al.]. Non-invasive Techniques for Muscle Fatigue Monitoring: A Comprehensive Survey // ACM Computing Surveys. 2024. Vol. 56, Issue 9. 221. P. 1–40. DOI: 10.1145/3648679.</mixed-citation><mixed-citation xml:lang="en">Na L., Rui Z., Bharath K. [et al.]. Non-invasive Techniques for Muscle Fatigue Monitoring: A Comprehensive Survey // ACM Computing Surveys. 2024. Vol. 56, Issue 9. 221. P. 1–40. DOI: 10.1145/3648679.</mixed-citation></citation-alternatives></ref><ref id="cit66"><label>66</label><citation-alternatives><mixed-citation xml:lang="ru">Samandari А. М. Functional near-infrared spectroscopy (fNIRS) as a hybrid system: a review // Modeling, Optimization and Information Technology. 2024. Vol. 12 (1). P. 1–18. DOI: 10.26102/2310-6018/2024.44.1.005.</mixed-citation><mixed-citation xml:lang="en">Samandari А. М. Functional near-infrared spectroscopy (fNIRS) as a hybrid system: a review // Modeling, Optimization and Information Technology. 2024. Vol. 12 (1). P. 1–18. DOI: 10.26102/2310-6018/2024.44.1.005.</mixed-citation></citation-alternatives></ref><ref id="cit67"><label>67</label><citation-alternatives><mixed-citation xml:lang="ru">Marinelli A., Canepa M., Domenico D. D. [et al.]. A comparative optimization procedure to evaluate pattern recognition algorithms on hannes prosthesis // Neurocomputing. 2024. Vol. 569 (7). 127123. DOI: 10.1016/j.neucom.2023.127123.</mixed-citation><mixed-citation xml:lang="en">Marinelli A., Canepa M., Domenico D. D. [et al.]. A comparative optimization procedure to evaluate pattern recognition algorithms on hannes prosthesis // Neurocomputing. 2024. Vol. 569 (7). 127123. DOI: 10.1016/j.neucom.2023.127123.</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
