<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE root>
<article 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" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">I.P. Pavlov Journal of Higher Nervous Activity</journal-id><journal-title-group><journal-title xml:lang="en">I.P. Pavlov Journal of Higher Nervous Activity</journal-title><trans-title-group xml:lang="ru"><trans-title>Журнал высшей нервной деятельности им. И.П. Павлова</trans-title></trans-title-group></journal-title-group><issn publication-format="print">0044-4677</issn><issn publication-format="electronic">3034-5316</issn><publisher><publisher-name xml:lang="en">The Russian Academy of Sciences</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">652072</article-id><article-id pub-id-type="doi">10.31857/S0044467724050042</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>ФИЗИОЛОГИЯ ВЫСШЕЙ НЕРВНОЙ (КОГНИТИВНОЙ) &#13;
ДЕЯТЕЛЬНОСТИ ЧЕЛОВЕКА</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>ФИЗИОЛОГИЯ ВЫСШЕЙ НЕРВНОЙ (КОГНИТИВНОЙ) ДЕЯТЕЛЬНОСТИ ЧЕЛОВЕКА</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Lower limb muscle activity during neurointerface control: neurointerface based on motor imagery of walking</article-title><trans-title-group xml:lang="ru"><trans-title>Активность мышц нижних конечностей... В условиях управления нейроинтерфейсом: нейроинтерфейс, основанный на воображении ходьбы</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Bobrova</surname><given-names>E. V.</given-names></name><name xml:lang="ru"><surname>Боброва</surname><given-names>Е. В.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>eabobrovy@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Reshetnikova</surname><given-names>V. V.</given-names></name><name xml:lang="ru"><surname>Решетникова</surname><given-names>В. В.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>eabobrovy@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Grishin</surname><given-names>A. A.</given-names></name><name xml:lang="ru"><surname>Гришин</surname><given-names>А. А.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>eabobrovy@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Vershinina</surname><given-names>E. A.</given-names></name><name xml:lang="ru"><surname>Вершинина</surname><given-names>Е. А.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>eabobrovy@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Bogacheva</surname><given-names>I. N.</given-names></name><name xml:lang="ru"><surname>Богачёва</surname><given-names>И. Н.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>eabobrovy@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Chsherbakova</surname><given-names>N. A.</given-names></name><name xml:lang="ru"><surname>Щербакова</surname><given-names>Н. А.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>eabobrovy@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Isaev</surname><given-names>M. R.</given-names></name><name xml:lang="ru"><surname>Исаев</surname><given-names>М. Р.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>eabobrovy@gmail.com</email><xref ref-type="aff" rid="aff2"/><xref ref-type="aff" rid="aff3"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Bobrov</surname><given-names>P. D.</given-names></name><name xml:lang="ru"><surname>Бобров</surname><given-names>П. Д.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>eabobrovy@gmail.com</email><xref ref-type="aff" rid="aff2"/><xref ref-type="aff" rid="aff3"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Gerasimenko</surname><given-names>Y. P.</given-names></name><name xml:lang="ru"><surname>Герасименко</surname><given-names>Ю. П.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>eabobrovy@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Pavlov Institute of Physiology, Russian Academy of Sciences</institution></aff><aff><institution xml:lang="ru">ФГБУН Институт физиологии РАН им. И.П. Павлова</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences</institution></aff><aff><institution xml:lang="ru">Институт высшей нервной деятельности и нейрофизиологии РАН</institution></aff></aff-alternatives><aff-alternatives id="aff3"><aff><institution xml:lang="en">Institute of Translational Medicine of Pirogov of Russian National Research Medical University</institution></aff><aff><institution xml:lang="ru">РНИМУ им. Н.И. Пирогова Минздрава России</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2024-11-26" publication-format="electronic"><day>26</day><month>11</month><year>2024</year></pub-date><volume>74</volume><issue>5</issue><fpage>591</fpage><lpage>605</lpage><history><date date-type="received" iso-8601-date="2025-02-02"><day>02</day><month>02</month><year>2025</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2024, Russian Academy of Sciences</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2024, Российская академия наук</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="en">Russian Academy of Sciences</copyright-holder><copyright-holder xml:lang="ru">Российская академия наук</copyright-holder></permissions><self-uri xlink:href="https://innoscience.ru/0044-4677/article/view/652072">https://innoscience.ru/0044-4677/article/view/652072</self-uri><abstract xml:lang="en"><p>The question of the activity of muscles that provide the realization of imaginary movement is essential in the rehabilitation of motor disorders using neurointerfaces. The literature data on this issue are contradictory. The paper analyzes the EMG activity of the shin and thigh muscles of 40 healthy volunteers when working with a neurointerface based on kinesthetic motor imagery of walking in place and supplemented with the «Biokin» robotic limb movement device (mechanotherapy), activated in case of successful motor imagery. It is shown that working with a neurointerface, on average for subjects, leads to an increase in muscle activity when motor imagery of walking compared to rest, and activation of the mechanical training device (AM) further increases muscle activity, with its effect being more pronounced in the muscles of the leg from which motor imagery of walking begins. The nature of muscle reactions to the task of motor imagery of walking is individual. AM when working with a neurointerface, the number of subjects with pronounced EMG activity increases, as does the number of significant correlations between the activity of the muscles of the lower limbs. Thus, the use of neurointerfaces based on motor imagery of walking and the addition of AM as feedback allows activating the muscles of the lower extremities, which is important in clinical practice in the rehabilitation of movements.</p></abstract><trans-abstract xml:lang="ru"><p>При реабилитации двигательных нарушений с использованием нейроинтерфейсов существенен вопрос об активности мышц, которые обеспечивают реализацию воображаемого движения. Сведения в литературе об этом противоречивы. В работе проведен анализ ЭМГ-активности мышц голени и бедра 40 здоровых добровольцев при работе с нейроинтерфейсом, основанным на кинестетическом воображении ходьбы на месте и дополненным робототехническим устройством перемещения конечностей «Биокин» (механотерапия), активируемым в случае успешного воображения движений. Показано, что работа с нейроинтерфейсом в среднем по всем участникам эксперимента приводит к увеличению активности мышц при воображении ходьбы по сравнению с покоем, а активация механотренажера (АМ) дополнительно увеличивает мышечную активность, причем ее влияние в большей степени выражено в мышцах той ноги, с которой начинается воображение ходьбы. Характер реакций мышц на задачу воображения ходьбы индивидуален. С АМ при работе с нейроинтерфейсом количество участников эксперимента с выраженной ЭМГ-активностью увеличивается, как и количество значимых корреляционных связей между активностью мышц нижних конечностей. Таким образом, использование нейроинтерфейсов, основанных на воображении ходьбы, и АМ в качестве обратной связи позволяет активизировать мышцы нижних конечностей, что важно в клинической практике при реабилитации движений.</p></trans-abstract><kwd-group xml:lang="en"><kwd>neurointerface</kwd><kwd>mechanical training device</kwd><kwd>EMG activity</kwd><kwd>motor imagery</kwd><kwd>walking</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>нейроинтерфейс</kwd><kwd>механотренажер</kwd><kwd>ЭМГ-активность</kwd><kwd>воображение движений</kwd><kwd>ходьба</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution xml:lang="ru">Правительство РФ</institution></institution-wrap><institution-wrap><institution xml:lang="en">Government of the Russian Federation</institution></institution-wrap></funding-source><award-id>1021062411782-5-3.1.8</award-id></award-group></funding-group></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Боброва Е.В., Решетникова В.В., Вершининa Е.А., Гришин А.А., Фролов А.А., Герасименко Ю.П. Межполушарная асимметрия и личностные характеристики пользователя мозг-компьютерного интерфейса при воображении движений рук. ДАН. 2020. 495(6): 558–561.</mixed-citation></ref><ref id="B2"><label>2.</label><mixed-citation>Боброва Е.В., Решетникова В.В., Вершинина Е.А., Гришин А.А., Исаев М.Р., Бобров П.Д., Герасименко Ю.П. Зависимость обучения управлению мозг-компьютерным интерфейсом от личностных характеристик. Доклады РАН. Науки о жизни. 2022. 507(1): 68–73.</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>Боброва Е.В., Решетникова В.В., Волкова К.В., Фролов А.А. Влияние эмоциональной устойчивости на успешность обучения управлению системой «интерфейс мозг-компьютер». Журнал высш.нервн. деятельности им. И.П.Павлова. 2017. 67 (4): 485–492.</mixed-citation></ref><ref id="B4"><label>4.</label><mixed-citation>Боброва Е.В., Решетникова В.В., Гришин А.А., Вершинина Е.А., Исаев М.Р., Пляченко Д.Р., Бобров П.Д., Герасименко Ю.П. Анализ мозговой и мышечной активности при управлении кортико-спинальным нейроинтерфейсом. Журнал высш.нервн. деятельности им. И.П.Павлова. 2023. 73(4): 510–523.</mixed-citation></ref><ref id="B5"><label>5.</label><mixed-citation>Боброва Е.В., Решетникова В.В., Фролов А.А., Герасименко Ю.П. Воображение движений нижних конечностей для управления системами «интерфейс мозг-компьютер». Журнал высш.нервн. деятельности им. И.П. Павлова. 2019. 69(5): 529–540.</mixed-citation></ref><ref id="B6"><label>6.</label><mixed-citation>Моисеев С.А., Городничев Р.М. Пространственно-временные паттерны кортико-мышечного взаимодействия при локомоции. Журнал высш.нервн. деятельности им. И.П.Павлова. 2023. 73(5): 666–679.</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>Моисеев С.А. Пространственно-временные паттерны межмышечного взаимодействия при локомоциях, вызванных чрескожной электрической стимуляцией спинного мозга. Ж. эвол. биохим. и физиол. 2022. 58(6): 549–557.</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>Решетникова В.В., Боброва Е.В., Вершинина Е.А., Гришин А.А., Фролов А.А., Герасименко Ю.П. Зависимость успешности воображения движений правой и левой руки от личностных характеристик пользователей. Журнал высш.нервн. деятельности им. И.П.Павлова.. 2021. 71(6): 830–839.</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>Baniqued P.D.E., Stanyer E.C., Awais M., Alazmani A., Jackson A.E., Mon-Williams M.A., Mushtaq F., Holt R.J. Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review. J Neuroeng Rehabil. 2021. 18(1): 15.</mixed-citation></ref><ref id="B10"><label>10.</label><mixed-citation>Barria P., Pino A., Tovar N., Gomez-Vargas D., Baleta K., Díaz C.A.R., Múnera M., Cifuentes C.A. BCI-based control for ankle exoskeleton T-FLEX: Comparison of visual and haptic stimuli with stroke survivors. Sensors. 2021. 21: 6431.</mixed-citation></ref><ref id="B11"><label>11.</label><mixed-citation>Belda-Lois J.-M., Mena-del Horno S., Bermejo-Bosch I., Moreno J.C., Pons J.L., Farina D., Iosa M., Molinari M., Tamburella F., Ramos A., Caria A., Solis- Escalante T., Brunner C., Rea M. Rehabilitation of gait after stroke: a review towards a top-down approach. J. Neuroeng. Rehabil. 2011. 8: 66.</mixed-citation></ref><ref id="B12"><label>12.</label><mixed-citation>Biswas P., Dodakian L., Wang P.T., Johnson C.A., See J., Chan V., Chou C., Lazouras W., McKenzie A.L., Reinkensmeyer D.J., Nguyen D.V., Cramer S.C., Do A.H., Nenadic Z. A single-center, assessor-blinded, randomized controlled clinical trial to test the safety and efficacy of a novel brain-computer interface controlled functional electrical stimulation (BCI-FES) intervention for gait rehabilitation in the chronic stroke population. BMC Neurol. 2024. 24(1): 200.</mixed-citation></ref><ref id="B13"><label>13.</label><mixed-citation>Bobrova E.V., Reshetnikova V.V., Vershinina E.A., Grishin A.A., Bobrov P.D., Frolov A.A., Gerasimenko Y.P. Success of hand movement imagination depends on personality traits, brain asymmetry, and degree of handedness. Brain Sciences. 2021. 11: 853.</mixed-citation></ref><ref id="B14"><label>14.</label><mixed-citation>Carrere L.C., Taborda M., Ballario C., Tabernig C. Effects of brain-computer interface with functional electrical stimulation for gait rehabilitation in multiple sclerosis patients: preliminary findings in gait speed and event-related desynchronization onset latency. J Neural Eng. 2021.18(6): 066023.</mixed-citation></ref><ref id="B15"><label>15.</label><mixed-citation>Cervera M.A., Soekadar S.R., Ushiba J., Millán J.D.R., Liu M., Birbaumer N., Garipelli G. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Ann Clin Transl Neurol. 2018. 5(5): 651–663.</mixed-citation></ref><ref id="B16"><label>16.</label><mixed-citation>Cheron G., Duvinage M., De Saedeleer C., Castermans T., Bengoetxea A., Petieau M., Seetharaman K., Hoellinger T., Dan B., Dutoit T., Sylos L.F., Lacquaniti F., Ivanenko Y. From spinal central pattern generators to cortical network: integrated BCI for walking rehabilitation. Neural Plast. 2012. 2012: 375148.</mixed-citation></ref><ref id="B17"><label>17.</label><mixed-citation>Choi J., Kim K.T., Jeong J.H., Kim L., Lee S.J., Kim H. Developing a motor imagery-based real-time asynchronous hybrid BCI controller for a lower-limb exoskeleton. Sensors (Basel). 2020. 20(24): 7309.</mixed-citation></ref><ref id="B18"><label>18.</label><mixed-citation>Choi J., Kim K.T., Jeong J.H., Kim L., Lee S.J., Kim H. Developing a motor imagery-based real-time asynchronous hybrid BCI controller for a lower-limb exoskeleton. Sensors. 2020. 20: 7309.</mixed-citation></ref><ref id="B19"><label>19.</label><mixed-citation>Chung E., Lee B.H., Hwang S. Therapeutic effects of brain-computer interface-controlled functional electrical stimulation training on balance and gait performance for stroke: A pilot randomized controlled trial. Medicine (Baltimore). 2020. 99(51): e22612.</mixed-citation></ref><ref id="B20"><label>20.</label><mixed-citation>Colucci A., Vermehren M., Cavallo A., Angerhöfer C., Peekhaus N., Zollo L., Kim W.S., Paik N.J., Soekadar S.R. Brain-computer interface-controlled exoskeletons in clinical neurorehabilitation: ready or not? Neurorehabil Neural Repair. 2020. 36(12): 747–756.</mixed-citation></ref><ref id="B21"><label>21.</label><mixed-citation>Decety J., Jeannerod M., Durozard D., Baverel G. Central activation of autonomic effectors during mental simulation of motor actions in man. J Physiol. 1993. 461: 549–563.</mixed-citation></ref><ref id="B22"><label>22.</label><mixed-citation>Dickstein R., Gazit-Grunwald M., Plax M., Dunsky A., Marcovitz E. EMG activity in selected target muscles during imagery rising on tiptoes in healthy adults and poststroke hemiparetic patients. J. Mot. Behav. 2005. 37: 475–483.</mixed-citation></ref><ref id="B23"><label>23.</label><mixed-citation>Do A.H., Wang P.T., King C.E., Abiri A., Nenadic Z. Brain-computer interface controlled functional electrical stimulation system for ankle movement. J. Neuroeng. Rehabil. 2011. 8: 49.</mixed-citation></ref><ref id="B24"><label>24.</label><mixed-citation>Do A.H., Wang P.T., King C.E., Chun S.N., Nenadic Z. Brain-computer interface controlled robotic gait orthosis. Journal of NeuroEngineering and Rehabilitation. 2013. 10: 111</mixed-citation></ref><ref id="B25"><label>25.</label><mixed-citation>Donati A., Shokur S., Morya E., Campos D., Moioli R., Gitti C., Augusto P., Tripodi S., Pires C., Pereira G., Brasil F., Gallo S., Lin A., Takigami A., Aratanha M., Bleuler H., Cheng G., Rudolph A., Nicolelis M. Long-term training with a brain-machine interface-based gait protocol induces partial neurological recovery in paraplegic patients. Scientific Reports. 2016. 6: 30383.</mixed-citation></ref><ref id="B26"><label>26.</label><mixed-citation>Epstein M.L. The relationship of mental imagery and mental rehearsal to performance of a motor task. Journal of Sport Psychology. 1980. 2(3): 211–220.</mixed-citation></ref><ref id="B27"><label>27.</label><mixed-citation>Ferrero L., Ortiz M., Quiles V., Iáñez E., Azorín J.M. Improving motor imagery of gait on a brain–computer interface by means of virtual reality: A case of study. IEEE Access. 2021. 9: 49121–49130.</mixed-citation></ref><ref id="B28"><label>28.</label><mixed-citation>Ferrero L., Quiles V., Ortiz M., Iáñez E., Gil-Agudo Á., Azorín J.M. Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton. iScience. 2023. 26(5): 106675.</mixed-citation></ref><ref id="B29"><label>29.</label><mixed-citation>Gandevia S.C., Wilson L.R., Inglis J.T., Burke D. Mental rehearsal of motor tasks recruits α-motoneurones but fails to recruit human fusimotor neurones selectively. The Journal of Physiology. 1997. 505: 259–266.</mixed-citation></ref><ref id="B30"><label>30.</label><mixed-citation>García-Cossio E., Severens M., Nienhuis B., Duysens J., Desain P., Keijsers N., Farquhar J. Decoding sensorimotor rhythms during robotic-assisted treadmill walking for brain computer interface (BCI) applications. PLoS One. 2015. 10(12): e0137910.</mixed-citation></ref><ref id="B31"><label>31.</label><mixed-citation>Geiger D.E., Behrendt F., Schuster-Amft C. EMG muscle activation pattern of four lower extremity muscles during stair climbing, motor imagery, and robot-assisted stepping: a cross-sectional study in healthy individuals. Biomed Res Int. 2019. 2019: 9351689.</mixed-citation></ref><ref id="B32"><label>32.</label><mixed-citation>Gentili R., Papaxanthis C., Pozzo T. Improvement and generalization of arm motor performance through imagery practice. Neuroscience. 2006. 137: 761–772.</mixed-citation></ref><ref id="B33"><label>33.</label><mixed-citation>Gerardin E., Sirigu A., Lehéricy S., Poline J.B., Gaymard B., Marsault C., Agid Y., Le Bihan D. Partially overlapping neural networks for real and imagined hand movements. Cereb Cortex. 2000. 10(11): 1093–1104.</mixed-citation></ref><ref id="B34"><label>34.</label><mixed-citation>Guillot A., Lebon F., Rouffet D., Champely S., Doyon J., Collet C. Muscular responses during motor imagery as a function of muscle contraction types. International Journal of Psychophysiology. 2007. 66(1): 18–27.</mixed-citation></ref><ref id="B35"><label>35.</label><mixed-citation>Hashimoto R., Rothwell J. Dynamic changes in corticospinal excitability during motor imagery. Exp Brain Res. 1999. 125: 75–81.</mixed-citation></ref><ref id="B36"><label>36.</label><mixed-citation>Herbert R.D., Dean C., Gandevia S.C. Effects of real and imagined training on voluntary muscle activation during maximal isometric contractions. Acta Physiol. Scand. 1998. 163: 361–368.</mixed-citation></ref><ref id="B37"><label>37.</label><mixed-citation>Ivanenko Y.P., Cappellini G., Dominici N., Poppele R.E., Lacquaniti F. Modular control of limb movements during human locomotion. J Neurosci. 2007. 27(41): 11149–11161.</mixed-citation></ref><ref id="B38"><label>38.</label><mixed-citation>Jackson P.L., Lafleur M.F., Malouin F., Richards C.L., Doyon J. Functional cerebral reorganization following motor sequence learning through mental practice with motor imagery. Neuroimage. 2003. 20: 1171–1180.</mixed-citation></ref><ref id="B39"><label>39.</label><mixed-citation>Jacobson E. Electrical measurements of neuromuscular states during mental activities. Am. J. Physiol. 1931. 96: 115–121.</mixed-citation></ref><ref id="B40"><label>40.</label><mixed-citation>Jacobson E. Electrophysiology of mental activities. Am. J. Psychol. 1932. 44: 677–694.</mixed-citation></ref><ref id="B41"><label>41.</label><mixed-citation>Jeunet C., Glize B., McGonigal A., Batail J.-M., Micoulaud-Franchi J.-A. Using EEG-based brain computer interface and neurofeedback targeting sensorimotor rhythms to improve motor skills: Theoretical background, applications and prospects. Neurophysiologie Clinique. 2019. 49: 125–136.</mixed-citation></ref><ref id="B42"><label>42.</label><mixed-citation>Jeunet C., N’Kaoua B., Lotte F. Advances in user-training for mental-imagery-based BCI control: Psychological and cognitive factors and their neural correlates. Prog. Brain. Res. 2016. 228: 3–35.</mixed-citation></ref><ref id="B43"><label>43.</label><mixed-citation>Khan H., Naseer N., Yazidi A., Eide P.K., Hassan H.W., Mirtaheri P. Analysis of human gait using hybrid EEG-fNIRS-Based BCI system: A review. Front Hum Neurosci. 2021. 14: 613254.</mixed-citation></ref><ref id="B44"><label>44.</label><mixed-citation>King C.E., Wang P.T., Chui L.A., Do A.H., Nenadic Z. Operation of a brain-computer interface walking simulator for individuals with spinal cord injury. Journal of NeuroEngineering and Rehabilitation. 2013. 10: 77.</mixed-citation></ref><ref id="B45"><label>45.</label><mixed-citation>King C.E., Wang P.T., McCrimmon C.M., Chou C.C.Y., Do A.H., Nenadic Z. Brain-computer interface driven functional electrical stimulation system for overground walking in spinal cord injury participant. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2014. 2014: 1238–1242.</mixed-citation></ref><ref id="B46"><label>46.</label><mixed-citation>King C.E., Wang P.T., McCrimmon C.M., Chou C.C.Y., Do A.H., Nenadic Z. The feasibility of a brain-computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia. J. Neuroeng. Rehabil. 2015. 12: 80.</mixed-citation></ref><ref id="B47"><label>47.</label><mixed-citation>Kucyi A., Moayedi M., Weissman-Fogel I., Hodaie M., Davis K.D. Hemispheric asymmetry in white matter connectivity of the temporoparietal junction with the insula and prefrontal cortex. PLoS One. 2012. 7(4): e35589.</mixed-citation></ref><ref id="B48"><label>48.</label><mixed-citation>Lafleur M.F., Jackson P.L., Malouin F., Richards C.L., Evans A.C., Doyon J. Motor learning procedures parallel dynamic functional changes during the execution and the imagination of sequential foot movements. Neuroimage. 2002. 16: 142–157.</mixed-citation></ref><ref id="B49"><label>49.</label><mixed-citation>Lebon F., Rouffet D., Collet C., Guillot A. Modulation of EMG power spectrum frequency during motor imagery. Neuroscience Letters. 2008. 435(3): 181–185.</mixed-citation></ref><ref id="B50"><label>50.</label><mixed-citation>Liang S., Xu J., Wang L., Zhao G. An investigation into the bilateral functional differences of the lower limb muscles in standing and walking. PeerJ. 2016. 4: e2315.</mixed-citation></ref><ref id="B51"><label>51.</label><mixed-citation>Lim V.K., Polych M.A., Holländer A., Byblow W.D., Kirk I.J., Hamm J.P. Kinesthetic but not visual imagery assists in normalizing the CNV in Parkinson’s disease. Clinical Neurophysiology. 2006. 117: 2308–2314.</mixed-citation></ref><ref id="B52"><label>52.</label><mixed-citation>Lotze M., Montoya P., Erb M., Hulsmann E., Flor H., Klose U. Activation of cortical and cerebellar motor areas during executed and imagined hand movements: an fMRI study. J. Cogn. Neurosci. 1999. 11: 491–501.</mixed-citation></ref><ref id="B53"><label>53.</label><mixed-citation>McCrimmon C.M., King C.E., Wang P.T., Cramer S.C., Nenadic Z., Do A.H. Brain-controlled functional electrical stimulation for lower-limb motor recovery in stroke survivors. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2014. 2014: 1247–1250.</mixed-citation></ref><ref id="B54"><label>54.</label><mixed-citation>McCrimmon C.M., King C.E., Wang P.T., Cramer S.C., Nenadic Z., Do A.H. Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: a safety study. Journal of NeuroEngineering and Rehabilitation. 2015. 12: 57.</mixed-citation></ref><ref id="B55"><label>55.</label><mixed-citation>Mrachacz-Kersting N., Jiang N., Stevenson A.J.T., Niazi I.K., Kostic V., Pavlovic A., Radovanovic S., Djuric-Jovicic M., Agosta F., Dremstrup K., Farina D. Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. J. Neurophysiol. 2016. 115(3): 1410–1421.</mixed-citation></ref><ref id="B56"><label>56.</label><mixed-citation>Mulder T., de Vries S., Zijlstra S. Observation, imagination and execution of an effortful movement: more evidence for a central explanation of motor imagery. Exp. Brain Res. 2005. 163: 344–351.</mixed-citation></ref><ref id="B57"><label>57.</label><mixed-citation>Mulder T., Zijlstra S., Zijlstra W., Hochstenbach J. The role of motor imagery in learning a totally novel movement. Exp. Brain Res. 2004. 154: 211–217.</mixed-citation></ref><ref id="B58"><label>58.</label><mixed-citation>Murphy T.H., Corbett D. Plasticity during stroke recovery: from synapse to behaviour. Nat Rev Neurosci. 2009. 10: 861–872.</mixed-citation></ref><ref id="B59"><label>59.</label><mixed-citation>Naito E., Kochiyama T., Kitada R., Nakamura S., Matsumura M., Yonekura Y., Sadato N. Internally simulated movement sensations during motor imagery activate cortical areas and the cerebellum. J. Neurosci. 2002. 22: 3683–3691.</mixed-citation></ref><ref id="B60"><label>60.</label><mixed-citation>Nenadic Z. Brain-computer interfaces for human gait restoration. Control Theory Technol. 2021. 19: 516–528.</mixed-citation></ref><ref id="B61"><label>61.</label><mixed-citation>Personnier P., Paizis C., Ballay Y., Papaxanthis C. Mentally represented motor actions in normal aging II. The influence of the gravito-inertial context on the duration of overt and covert arm movements. Behav Brain Res. 2008. 186(2): 273–283.</mixed-citation></ref><ref id="B62"><label>62.</label><mixed-citation>Ranganathan V.K., Siemionow V., Liu J.Z., Sahgal V., Yue G.H. From mental power to muscle power – gaining strength by using mind. Neuropsychologia. 2004. 42: 944–956.</mixed-citation></ref><ref id="B63"><label>63.</label><mixed-citation>Ren S., Wang W., Hou Z.-G., Liang X., Wang J., Shi W. Enhanced motor imagery based brain-computer interface via FES and VR for lower limbs. IEEE TNSRE. 2020. 28(8): 1846–1855.</mixed-citation></ref><ref id="B64"><label>64.</label><mixed-citation>Sebastián-Romagosa M., Cho W., Ortner R., Sieghartsleitner S., Von Oertzen T.J., Kamada K., Laureys S., Allison B.Z., Guger C. Brain–computer interface treatment for gait rehabilitation in stroke patients. Front. Neurosci. 2023. 17: 1256077.</mixed-citation></ref><ref id="B65"><label>65.</label><mixed-citation>Shaw W.A. The distribution of muscular action potentials during imaging. Psychol. Rec. 1938. 2: 195–216.</mixed-citation></ref><ref id="B66"><label>66.</label><mixed-citation>Takahashi M., Takeda K., Otaka Y., Osu R., Hanakawa T., Gouko M., Ito K. Event related desynchronization-modulated functional electrical stimulation system for stroke rehabilitation: A feasibility study. Journal of NeuroEngineering and Rehabilitation. 2012. 9: 56.</mixed-citation></ref><ref id="B67"><label>67.</label><mixed-citation>van der Meulen M., Allali G., Rieger S. W., Rieger S.W., Assal F., Vuilleumier P. The influence of individual motor imagery ability on cerebral recruitment during gait imagery. Human Brain Mapping. 2014. 35(2): 455–470.</mixed-citation></ref><ref id="B68"><label>68.</label><mixed-citation>Villiger M., Estevez N., Hepp-Reymond M.-C., Kiper D., Kollias S., Eng K., Hotz-Boendermaker S.S. Enhanced activation of motor execution networks using action observation combined with imagination of lower limb movements. 2013. PLoS ONE. 8(8): e72403.</mixed-citation></ref><ref id="B69"><label>69.</label><mixed-citation>Vossel S., Geng J.J., Fink G.R. Dorsal and ventral attention systems: distinct neural circuits but collaborative roles. Neuroscientist. 2014. 20(2): 150–159.</mixed-citation></ref><ref id="B70"><label>70.</label><mixed-citation>Wang P.T., King C.E., Chui L.A., Nenadic Z., Do A.H. BCI controlled walking simulator for a BCI driven FES device. RESNA Annual Conference. Las Vegas, Nevada. June 26 – 30, 2010.</mixed-citation></ref><ref id="B71"><label>71.</label><mixed-citation>Wehner T., Vogt S., Stadler M. Task-specific EMG-characteristics during mental training. Psychol Res. 1984. 46(4): 389–401.</mixed-citation></ref><ref id="B72"><label>72.</label><mixed-citation>Xu R., Jiang N., Mrachacz-Kersting N., Lin C., As G., Moreno J.C., Pons J.L., Member S., Dremstrup K., Farina D. A closed-loop brain–computer interface triggering an active ankle–foot orthosis for inducing cortical neural plasticity. IEEE transactions on biomedical engineering. 2014. 61(7): 2092–2101.</mixed-citation></ref><ref id="B73"><label>73.</label><mixed-citation>Yahagi S., Shimura K., Kasai T. An increase in cortical excitability with no change in spinal excitability during motor imagery. Percept. Mot. Skills. 1996. 83: 288–290.</mixed-citation></ref><ref id="B74"><label>74.</label><mixed-citation>Yue G., Cole K.J. Strength increases from the motor program: comparison of training with maximal voluntary and imagined muscle. J. Neurophysiol. 1992. 67: 1114–1123.</mixed-citation></ref></ref-list></back></article>
