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BACKGROUND: In Russia, a semi-quantitative CT 04 scoring system is used in the analysis of thoracic computed tomography (CT) scans of COVID-19 patients to grade the severity of lung lesions. Despite the widespread use of this approach, the scoring systems diagnostic accuracy for identification hospitalizations for patients with the disease is currently unknown. AIM: To evaluate the sensitivity, specificity, positive (PPV) and negative (NPV) predictive value of the CT 04 system for the triage of COVID-19 patients. MATERIALS AND METHODS: This retrospective study enrolled 575 patients of Moscow clinics with laboratory-verified COVID-19, aged 57.213.9 years, 55% females. All patients were examined with four consecutive chest CT scans, and the disease severity was assessed using the CT 04 scoring system. Sensitivity and specificity were calculated as conditional probabilities that a patient would experience clinical improvement or deterioration, depending on the preceding CT examination results. For the calculation of the NPV and PPV, we estimated the COVID-19 prevalence in Moscow. The data on total cases of COVID-19 from March 6 to November 28, 2020, were taken from the Rospotrebnadzor website. We used several ARIMA and EST models with different parameters to fit the data and forecast the incidence. RESULTS: The median specificity of the CT 04 scoring system was 69% (95% CI 32%, 100%), and the sensitivity was 92% (95% CI 74%, 100%). The best statistical model describing the epidemiological situation in Moscow was ARIMA (0,2,1). According to our calculations, with the predicted point prevalence of 9.6%, the values of PPV and NPV were 56% and 97%, correspondingly. CONCLUSION: The maximum Youdens index was observed for the period between the first and the second chest CT examinations when the majority of the included patients experienced clinical deterioration. The CT 04 scoring system makes it possible to safely exclude the development of pathological changes in patients with mild and moderate disease (categories CT-0 and CT-1), thereby optimizing the burden on hospitals in an unfavorable epidemic situation.

BACKGROUND:Effective and safe tools assisting triage decisions for COVID-19 patients could optimize the pressure on the healthcare system. COVID-19 often has respiratory manifestations, and medical imaging techniques provide an opportunity to assess the diseases severity. AIMS:To estimate the sensitivity and specificity of lung ultrasound for different degrees of pulmonary involvement in COVID-19 patients by a systematic review of English articles using PubMed and Google Scholar databases. Search terms included lung ultrasound, chest ultrasound, thoracic ultrasound, ultrasonography, COVID-19, SARS-CoV-2, coronavirus, diagnosis, diagnostic value, specificity, and sensitivity. Only studies addressing lung ultrasound diagnostic accuracy for patients with suspected COVID-19 using thoracic computed tomography, reverse transcription polymerase chain reaction, or laboratory data as a reference standard were included. Independent extraction of articles was performed by two authors using predefined data fields with subsequent assessment of study quality indicators. The random-effect model was used to analyze and pool lung ultrasound sensitivity and specificity across the included studies. Sixteen studies met our inclusion criteria, but only three of them divided patients into distinct and defined groups depending on the disease severity. We used the remaining studies data to assess the secondary outcomes: the values of sensitivity and specificity of lung ultrasound for COVID-19 regardless of the patients clinical status. Heterogeneity for primary and secondary outcomes was observed that remained when pooling for different scenarios (screening, assessing severity) and cohorts of participants. Lung ultrasound had the highest accuracy for confirmed COVID-19 patients with severe disease (sensitivity 87.6% 12.3%, specificity 80.5% 7.1%), and the lowest accuracy for the patients with mild disease (sensitivity 72.8% 7.1%, specificity 74.3% 2.7%). CONCLUSIONS:Lung ultrasound can be used in patients with confirmed COVID-19 to detect serious damage to the lung tissue. The diagnostic value of the method for assessing mild and moderate lung lesions is relatively low.

The objective: to predict lethal outcomes in patients with COVID-19 based on the result of chest computed tomography (chest CT) using a semi-quantitative visual scale of the pulmonary parenchyma lesion.Subjects and methods. Inclusion criteria: patients who underwent chest CT from March 2 to May 1, 2020, inclusively, upon referral of a general practitioner due to suspected community-acquired pneumonia caused by COVID-19. Chest CT was performed in 48 medical organizations providing primary medical care to the adult population in Moscow. Exclusion criteria: patients whose chest CT was not assessed by CT 0-4 score; patients who were not confirmed as COVID-19 positive.In the Russian Federation, CT 0-4 score is recommended to be used for assessment of the extent of lung parenchyma lesion in cases with suspected COVID-19.Data on fatal outcomes were received on May 4, 2020 inclusive.Results: data of 13,003 patients from the Unified Radiological Information Service were retrospectively included in the study according to inclusion and exclusion criteria. The test aimed to detect a trend of directional changes in the proportion of deceased patients among various categories using CT 0-4 score demonstrated a statistically significant result (p < 0.0001). The chance of a lethal outcome increases directionally from CT-0 to CT-4. The test for deviations from the linear trend also provided p < 0.0001, i.e. when moving to higher scores (CT-3 and CT-4), there was an accelerated increase in the risk of death. Analysis of overall survival using the Cox regression model showed that the assessed factors (age and CT 0-4 score) were statistically significantly associated with the time to death from COVID-19 (p < 0.05). The risk of death increased with age, on average, 8.6% for every 5 years (95% CI 0.8-17.0%). When transferring from one category of CT to the next one, the risk increased by 38% on the average (95% CI 17.1-62.6%). There was no statistically significant association of gender factor with overall survival (p = 0.408).The visual score of CT 0-4 recommended for use in the Russian Federation to assess lung parenchyma lesions according to chest CT data, is a predictor of a lethal outcome in patients with COVID-19. CT 0-4 score is convenient for practical use.

Motor function deficit due to stroke is one of the leading causes for disability among working-age population. The most effective evidence-based treatment strategies are task oriented exercise approaches including constrained-induced movement therapy. Robot-assisted training provides high amount of repetitions and feedback to patient. Adjuvant therapies such as mirror therapy and motor imagery show their effectiveness if used in combination with basic neurorehabilitation methods and are treatment of choice for patients with severe motor impairment. Brain-computer interfaces allow to control motor imagery as a process by giving different type of feedback (e.g. kinesthetic via exoskeleton) during training sessions. It is poorly known if kinesthetic motor imagery is more effective comparing to robot-assisted training as a part of post-stroke rehabilitation. Materials and methods: 55 patients with arm paresis >1 month after stroke were enrolled in the current study. Screening and randomization were performed. Participants underwent rehabilitation treatment where BCI controlled motor imagery training in main group and robot-assisted training in control group were included. Motor function of the paretic arm was assessed using Action Research Arm Test (ARAT) and Fugl-Meyer Assessment (FMA) before and after intervention. Results: Recovery of upper extremity motor function did not correlate with time since stroke and age of participants neither in main group, nor in control group. Correlations between change in motor scales scores and initial severity of motor deficit was shown in both groups (p

This paper addresses changes in blood flow parameters in the area behind a saccular aneurysm. The hemodynamics modelling was conducted with the ANSYS software using the Lattice Boltzmann method. We performed a computational fluid dynamics simulation of the blood flow in two vessels of 8.4 mm in diameter, one of which contained an aneurysm of 23 mm in diameter, and the other had no abnormalities. The maximum relative deviation of the blood flow velocity registered 35 mm away from the aneurysm, provided the inflow velocity was 86 cm/s at $60^{\mathrm{t}\mathrm{h}}$ ms from the start of the simulation, was 38.5% compared to the flow velocity in the normal vessel.

The project is aimed at investigating efficacy of a BCI-controlled palm exoskeleton as a tool for motor function recovery in post-stroke patients. The idea of using the system is grounded on vast amount of data supported by physiologic literature and our own findings in healthy subjects, suggesting that kinesthetic motor imagery (MI) requires activation of the brain areas involved in motion planning, execution and control. Thus, the common idea of using a MI-based BCI for neurorehabilitation is to reinforce motor imagery of intention to move with visual, proprioceptive and\or tactile feedback. Results of a four-year multi-center randomized controlled study of post-stroke motor rehabilitation procedure with BCI-controlled hand exoskeleton complex are presented. The study has the largest number of participants so far. Statistical analysis of different clinical scales used to assess motor function recovery show that incorporating the BCI+exoskeleton procedure into rehabilitation significantly improves its outcome. The analysis also revealed non-monotonical dependency of motor function recovery rate on initial motor and sensory function status, as well as on age, and BCI control accuracy. Hopefully, the reported data combined with the results obtained by other groups in the world, would provide solid evidence supporting inclusion of the BCI-based systems into rehabilitation practice.

The mechanisms of the organization of goal-directed movements in the human arm have received insufficient study. The processes of cortical reorganization on the background of learning new skills and rehabilitation after central nervous system disease are of particular interest. Studies of the motor system and recording of neurophysiological parameters determining the kinematic characteristics of movements at the cortical level use navigated transcranial magnetic stimulation. The present study of 20 healthy volunteers randomized to study and control groups assessed resting motor thresholds, cortical motor representation zones of the extensor digitorum communis muscle in terms of areas and weighted areas, and the displacement of the centers of gravity on the background of training to motor imagery using a brain–computer interface. No significant differences were found between these parameters in the study group and the control group.

Motor recovery in post{stroke and post{traumatic patients using exoskeleton controlled by the brain-computer interface (BCI) is a new and promising rehabilitation procedure. Its development is a multidisciplinary research, which requires the teamwork of experts in neurology, neurophys- iology, physics, mathematics, biomechanics and robotics. Some aspects of all these elds of study concerning the development of this rehabilita-tion procedure are described in the paper. The description includes the principles and physiological prerequisites of BCI based on motor imagery, biologically adequate principles of exoskeleton design and control and the results of clinical application.

Motor imagery can stimulate the same neuroplastic mechanisms of the brain as their actual execution. The motor imagery can be controlled via the brain–computer interface (BCI), which transforms the EEG signals of the brain appearing during the motor imagery into commands for the external device. The results of the two-stage study of the application of a non-invasive BCI for the rehabilitation of patients with marked hemiparesis resulted from a local brain injury. We have shown that the learning to manage the BCI does not depend on the duration of disease, localization of the damaged site, and the severity of neurological deficit. The results of the first stage of the study carried out in a group of 36 patients showed that the rehabilitation therapy was more effective in the group that was trained to manage the BCI (the ARAT score improved from 1 [0; 2] to 5 [0; 16], p = 0.012 in the BCI group; no significant improvement was detected in the control group). In the second phase of the study, 19 patients participated in the testing of a BCI–exoskeleton system. Rehabilitation based on this technology led to an improvement of the motor function of an arm from 2 [0; 37] to 4 [1; 45.5], p = 0.005, according to the ARAT scale, and from 72 [63; 110] to 79 [68; 115], p = 0.005, according to the Fugl-Meyer scale.

The dynamics of motor function recovery in a patient with an extensive brain lesion has been investigated during a course of neurorehabilitation assisted by a hand exoskeleton controlled by a brain–computer interface. Biomechanical analysis of the movements of the paretic arm recorded during the rehabilitation course was used for an unbiased assessment of motor function. Fifteen procedures involving hand exoskeleton control (one procedure per week) yielded the following results: (a) the velocity profile for targeted movements of the paretic hand became nearly bell-shaped; (b) the patient began to extend and abduct the hand, which was flexed and adducted at the beginning of the course; and (c) the patient started supinating the forearm, which was pronated at the beginning of the rehabilitation course. The first result is interpreted as improvement of the general level of control over the paretic hand, and the two other results are interpreted as a decrease in spasticity of the paretic hand.

The physiological mechanisms underlying the process of motor imagery have significant similarities with the mechanisms of motor control, and this can be used for the rehabilitation of patients with movement disorders. In patients with severe paresis, motor imagery may be the only method producing movement recovery. Over the last decade, this has led to increasing interest in studies of the function of motor imagery. Brain–computer interface technologies can be used monitor training with imaginary movements.

The sources of brain activity that make the maximum contribution to EEG patterns corresponding to motor imagery have been studied. The accuracy of their classification determines the efficiency of brain-computer interface (BCI) for controlling external technical devices directly by brain signals, without the involvement of muscle activity. Brain activity sources are identified by independent component analysis. The independent components providing the maximum BCI classification accuracy are considered relevant for the motor imagery task. The two most relevant sources exhibit clearly marked event-related desynchronization and synchronization of the μ-rhythm during the imagery of contra- and ipsilateral hand movements. These sources were localized by solving the inverse EEG problem with due consideration for individual geometry of the brain and its covers, as determined by magnetic resonance imaging. Each of the sources was shown to be localized in the 3a area of the primary somatosensory cortex corresponding to proprioceptive sensitivity of the contralateral hand. Their positions were close to the foci of BOLD activity obtained by fMRI.

The neurophysiological prerequisites for the development and operation of the brain-computer interfaces (BCI) that allow cerebral electrical signals alone to control external technical devices are considered. A BCI based on the discrimination of the EEG patterns related to imagery of extremity movements is described. The possibility of the rehabilitation of patients with motor disorders by means of the BCI based on motor imagery and the exoskeleton controlled by it is discussed.

The mechanisms underlying the process of motor imagery are similar to the motor control mechanisms. It can be used for motor learning in patients with movement disorders. Motor imagery may be the only one method for recovery of motor function in patients with severe paresis. It was the prerequisite of increased scientist interest in motor imagery during last decade. Brain-computer interface technology can support the motor imagery trainings.

We studied the dynamics of motor function recovery in a patient with severe brain damage in the course of neurorehabilitation using hand exoskeleton controlled by brain-computer interface. For estimating the motor function of paretic arm, we used the biomechanical analysis of movements registered during the course of rehabilitation. After 15 weekly sessions of hand exoskeleton control, the following results were obtained: a) the velocity profile of goal-directed movements of paretic hand became bell-shaped, b) the patient began to extend and abduct the hand which was flexed and adducted in the beginning of rehabilitation, and c) the patient began to supinate the forearm which was pronated in the beginning of rehabilitation. The first result is an evidence of the general improvement of the quality of motor control, while the second and third results prove that the spasticity of paretic arm has decreased.

The paper examines sources of brain activity, contributing to EEG pat- terns which correspond to motor imagery during training to control brain-computer interface. To identify individual source contribution into electroencephalogram recorded during the training Independent Component Analysis was used. Then those independent components for which the BCI system classification accuracy was at maximum were treated as relevant to performing the motor imagery tasks, since they demonstrated well exposed event related de-synchronization and event related synchronization of the sensorimotor -rhythm during imagining of contra- and ipsilateral hand movements. To reveal neurophysiological nature of these com- ponents we have solved the inverse EEG problem to locate the sources of brain activity causing these components to appear in EEG. The sources were located in hand representation areas of the primary sensorimotor cortex. Their positions practically coincide with the regions of brain activity during the motor imagination obtained in fMRI study. Individual geometry of brain and its covers provided by anatomical MR images was taken into account when localizing the sources.

Provision of a continuous, comprehensive and intensive program of motor rehabilitation to patients with spastic paresis remains a significant problem in the organization of outpatient rehabilitation. The GSC 'I-CAN' program is aimed at three main pathological components of spastic paresis: paresis, spasticity and muscle contracture. Moreover, detailed guides and a mobile application with video instructions allow the patient to perform the exercises on their own at home. The article describes the elements of the home rehabilitation program GSC 'I-CAN' and their rationale, represents an overview of clinical studies and describes our experience of working with the program.Сложность обеспечения пациентов непрерывной комплексной и интенсивной программой восстановительного лечения на амбулаторном этапе в настоящее время остается существенной проблемой организации двигательной реабилитации при центральном парезе. Программа домашней реабилитации GSC 'I-CAN' направлена на основные компоненты патологического процесса при центральном парезе: парез, спастичность и мышечную контрактуру, а подробное руководство и мобильное приложение с видеоинструкциями позволяют пациенту выполнять упражнения самостоятельно в домашних условиях. В статье описаны элементы программы домашней реабилитации GSC 'I-CAN' и их обоснование, представлен обзор клинических исследований и описан собственный опыт работы с программой.

Studied are sources of brain activity contributing to EEG patterns which correspond to motor imagery. The accuracy of their classification determines the efficiency of brain-computer interface (BCI) allowing for controlling external technical devices directly by brain signals without involving muscle activity. Sources of brain activity are identified by Independent Component Analysis. Those independent components for which the BCI classification accuracy are at maximum are treated as relevant for motor imagery task. Two of the most relevant sources demonstrate strictly exposed event related desynchronization and synchronization of mu--rhythm during imagery of contra--and ipsilateral hands. These sources are localized by solving inverse EEG problem taking into account individual geometry of brain and its covers provided by anatomical MRI images. The sources are shown to be localized in BA 3A relating to proprioceptive sensitivity of the contralateral hand. Their positions are closed to foci of BOLD activity obtained by fMRI.

Objective To study the topography and neurophysiological differences in cortical motor representations of hand flexor and extensor muscles during motor imagery (MI). Methods Seven healthy volunteers (age 27 ± 3.4) were enrolled. Navigated transcranial magnetic stimulation was performed for mapping mm. Flexor digitorum superficialis, extensor digitorum communis cortical areas at rest and during MI (fingers flexion or extension). We assessed cortical excitability changes by the dynamics of motor threshold (MT) and paired stimuli with interstimulus intervals 2 ms (SICI) and 12 ms (ICF). Results The localization of flexor and extensor “hotspots” was different with a range of 1 cm. MT decreased during MI significantly from 50% to 47% at the flexor hotspot and from 47% to 46% at the extensor hotspot compared to resting state. There was observed the decrease of ICF during MI of both movements compared to resting state. SICI was reduced during imagery of flexion and enhanced during imagery of extension movement. Conclusions Further we will train the participants to imagine hand extension with brain–computer interface with evaluation the neurophysiological difference between trained and untrained. Topographic and neurophysiological differences of cortical motor representations of differentiated movements during MI can be the basis of new non-invasive brain–computer interface approach.

To evaluate the clinical efficacy of BCI-supported mental practice and to reveal specific cognitive impairment which determine mental practice ineffectiveness and inability to perform MI.Fifty-five hemiplegic patients after first-time stroke (median age 54. 0 [44.0; 61.0], time from onset 6.0 [3.0; 13.0] month) were randomized into two groups - BCI and sham-controlled. Severity of arm paresis was measured by Fugl-Meyer Assessment of Motor Recovery after Stroke (FMA) and Action Research Arm Test (ARAT). Twelve patients from the BCI group were examined using neuropsychological testing. After assessment, patients were trained to imagine kinesthetically a movement under control of BCI with the feedback presented via an exoskeleton. Patients underwent 12 training sessions lasting up to 30 min. In the end of the study, the scores on movement scales, electroencephalographic results obtained during training sessions were analyzed and compared to the results of neuropsychological testing.Evaluation of the UL clinical assessments indicated that both groups improved on ARAT and FMA (sections A-D, H, I) but only the BCI group showed an improvement in the ARAT's grasp score (p=0.012), pinch score (p=0.012), gross movement score (p=0,002). The significant correlation was revealed between particular neuropsychological tests (Taylor Figure test, choice reaction test, Head test) and online accuracy rate.These results suggest that adding BCI control to exoskeleton-assisted physical therapy can improve post-stroke rehabilitation outcomes. Neuropsychological testing can be used for screening before mental practice admission and promote personalized rehabilitation.Цель исследования. Оценка эффективности применения интерфейса мозг-компьютер с экзоскелетом кисти (ИМК-экзоскелет) в комплексной реабилитации пациентов с последствиями нарушения мозгового кровообращения, а также минимально достаточного резерва когнитивных функций, необходимого для выполнения пациентом эффективной ментальной тренировки с парадигмой представления движения. Материал и методы. Рандомизировали 55 пациентов (медиана возраста 54,0 [44,0; 61,0] года, медиана давности инсульта 6,0 [3,0; 13,0] мес) в группы основную и контроля (имитации ИМК). Степень пареза оценивали по шкалам Фугл-Мейера и ARAT. Нейропсихологическое обследование для выявления предикторов обучения представлению движения прошли 12 пациентов из основной группы перед началом тренировок. После обследования пациенты проходили курс представления движения (разгибание кисти) под контролем ИМК с управляемым экзоскелетом кисти. В среднем пациенты прошли 10 тренировочных сессий по 30 мин каждая. По завершении тренинга повторно оценивали параметры двигательных шкал, проводили анализ электроэнцефалографических данных, полученных в ходе тренировочных сессий, а также их сопоставление с данными нейропсихологического обследования. Результаты и заключение. В обеих группах наблюдали улучшение двигательной функции руки по шкалам ARAT и Фугл-Мейера (разделы A-D, H, I). Только в группе ИМК-экзоскелет выявлено улучшение шарового захвата кисти (p=0,012), щипкового захвата пальцев кисти (p=0,012), а также крупных движений руки (p=0,002) по шкале ARAT. Выявлена достоверная корреляция достигнутого показателя качества управления ИМК с рядом результатов выполнения нейропсихологических тестов: фигура Тейлора, проба Хэда, реакция выбора.Таким образом, включение системы ИМК-экзоскелет в комплексную реабилитацию пациентов с постинсультным парезом руки достоверно улучшает ряд показателей захвата и движений в проксимальных отделах верхней конечности. Применение нейропсихологических тестов в качестве скрининга для отбора пациентов может способствовать персонифицированному применению реабилитационной технологии.

Examined are sources of brain activity, contributing to EEG patterns which correspond to motor imagery during training to control brain–computer interface (BCI). To identify individual source contribution into EEG recorded during the training, Independent Component Analysis (ICA) was employed. Those independent components, for which the BCI system classification accuracy was at maximum, were treated as relevant to performing the motor imagery tasks. Activities of the three most relevant components demonstrate well exposed event related desynchronization (ERD) and event related synchronization (ERS) of the mu-rhythm during imagining of contra- and ipsilateral hand and feet movements. To reveal neurophysiological nature of these components we solved the inverse EEG problem in order to localize the sources of brain activity causing these components to appear in EEG. Individual geometry of brain and its covers provided by anatomical MR images, was taken into account when localizing the sources. The sources were located in hand and feet representation areas of the primary somatosensory cortex (Brodmann areas 3a). Their positions were close to foci of BOLD activity obtained in fMRI study.

The mechanisms underlying the process of motor imagery are similar to the motor control mechanisms. It can be used for motor learning in patients with movement disorders. Motor imagery may be the only one method for recovery of motor function in patients with severe paresis. It was the prerequisite of increased scientist interest in motor imagery during last decade. Brain-computer interface technology can support the motor imagery trainings.

The paper examines neurophysiological basis for development and performance of brain-computer interface (BCI) that permits cerebral activity alone to control computers or other external technical devices. BCI based on the discrimination of EEG patterns related to an imagery of extremity movements is considered. The problem of BCI application to restoring of motor functions in patients with motor disabilities is discussed.

A brain-computer interface (BCI) provides a direct functional interaction between the human brain and the external device. Many kinds of signals (from electromagnetic to metabolic [23, 38, 42]) could be used in BCI. However the most widespread BCI systems are based on EEG recordings. BCI consists of a brain signal acquisition system, data processing software for feature extraction and pattern classification, and a system to transfer commands to an external device and, thus, providing feedback to an operator. The most prevalent BCI systems are based on the discrimination of EEG patterns related to execution of different mental tasks [14, 21, 24]. This approach is justified by the presence of correlation between brain signal features and tasks performed, revealed by basic research [24, 28, 30, 45]. By agreement with the BCI operator each mental task is associated with one of the commands to the external device. Then to produce the commands, the operator switches voluntary between corresponding mental tasks. If BCI is dedicated to control device movements then psychologically convenient mental tasks are motor imaginations. For example, when a patient controls by BCI the movement of a wheelchair its movement to the left can be associated with the imagination of the left arm movement and movement to the right with right arm movement. Another advantage of these mental tasks is that their performance is accompanied by the easily recognizable EEG patterns. Moreover, motor imagination is considered now as an efficient rehabilitation procedure to restore movement after paralysis [4]. Thus, namely the analysis of BCI performance based on motor imagination is the object of the present chapter.

It is shown that despite the fact that the motor imagery based brain computer interface does not rely on any particular feature of EEG signal defined a priori, system designed on the basis of EEG signal classifier is indeed controlled by the signals originating in the motor cortex. To prove this the most distinguishable EEG patterns were extracted by means of Independent Component Analysis with consequent cross-validation procedure used to select the independent components significant to the brain computer interface performance. Sources of the brain activity represented by the chosen independent components were located using single dipole approximation with individual head geometry model. These sources were found in the bottom of the central sulcus, area 3a, for each subject. These results are in good agreement with the outcome of fMRI study conducted under the same conditions.

Motor imagery is suggested to stimulate the same plastic mechanisms in the brain as a real movement. The brain-computer interface (BCI) controls motor imagery by converting EEG during this process into the commands for an external device. This article presents the results of two-stage study of the clinical use of non-invasive BCI in the rehabilitation of patients with severe hemiparesis caused by focal brain damage. It was found that the ability to control BCI did not depend on the duration of a disease, brain lesion localization and the degree of neurological deficit. The first step of the study involved 36 patients; it showed that the efficacy of rehabilitation was higher in the group with the use of BCI (the score on the Action Research Arm Test (ARAT) improved from 1 [0; 2] to 5 [0; 16] points, p = 0.012; no significant improvement was observed in control group). The second step of the study involved 19 patients; the complex BCI-exoskeleton (i.e. with the kinesthetic feedback) was used for motor imagery trainings. The improvement of the motor function of hands was proved by ARAT (the score improved from 2 [0; 37] to 4 [1; 45:5] points, p = 0.005) and Fugl-Meyer scale (from 72 [63; 110 ] to 79 [68; 115] points, p = 0.005).