Rehabilitation of post stroke patients using a bioengineering system "brain-computer interface + exoskeleton"

Котов, С. В.; Турбина, Л. Г.; Bobrov, Pavel; Frolov, Alexander A.; Pavlova, O. G.; Курганская, М. Е.; Бирюкова, Е. В.

doi:10.17116/jnevro201411412266-71

Cited by 16 publications

(5 citation statements)

References 23 publications

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“…Working with exoskel etal devices supplements this scheme, in addition to the visually observed action, with the generation of proprioceptive afferentation from the passively work ing muscles in synchronization with the voluntary effort for movement. Successful attempts in this direc tion aimed at implementing BCI training technologies have already been made, by Russian researchers as well [57,58].…”

Section: Ideomotor Training In Combination With Brain-mentioning

confidence: 99%

Neurophysiological foundations and practical realizations of the brain–machine interfaces in the technology in neurological rehabilitation

Kaplan

2016

Hum Physiol

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The brain-computer interface (BCI) technology, based on the registration and interpretation of EEG, has recently become one of the most popular developments in neuroscience and psychophysiology. This is due not only to the intended future use of these technologies in many areas of practical human activity, but also to the fact that BCI is a completely new paradigm in psychophysiology, which allows testing hypoth eses about the possibilities of the human brain to the development of skills of interaction with the outside world without the mediation of the motor system, i.e., only with the help of voluntary modulation of EEG generators. This paper examines the theoretical and experimental basis, the current state, and the prospects of development of training, communicational, and assisting complexes based on BCI to control them without muscular effort on the basis of decoding mental commands detected in the EEG of patients with severely impaired speech and motor system.

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Section: Ideomotor Training In Combination With Brain-mentioning

confidence: 99%

Neurophysiological foundations and practical realizations of the brain–machine interfaces in the technology in neurological rehabilitation

Kaplan

2016

Hum Physiol

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“…Significant gains in hand function were found for the FMA, ARAT and Jebsen-Taylor Test, after 7-10 weeks of training. This protocol was previously used by their group for training patients post-stroke in multiple studies [86][87][88]. The second study in CP [89] showed a decrease (improvement) in a serial reaction time task with the non-dominant hand, after only three sessions of BCI-NFT (8 min each) using visual feedback.…”

Section: Discussionmentioning

confidence: 99%

Determining optimal mobile neurofeedback methods for motor neurorehabilitation in children and adults with non-progressive neurological disorders: a scoping review

Behboodi

Lee

Hinchberger

et al. 2022

J NeuroEngineering Rehabil

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Background Brain–computer interfaces (BCI), initially designed to bypass the peripheral motor system to externally control movement using brain signals, are additionally being utilized for motor rehabilitation in stroke and other neurological disorders. Also called neurofeedback training, multiple approaches have been developed to link motor-related cortical signals to assistive robotic or electrical stimulation devices during active motor training with variable, but mostly positive, functional outcomes reported. Our specific research question for this scoping review was: for persons with non-progressive neurological injuries who have the potential to improve voluntary motor control, which mobile BCI-based neurofeedback methods demonstrate or are associated with improved motor outcomes for Neurorehabilitation applications? Methods We searched PubMed, Web of Science, and Scopus databases with all steps from study selection to data extraction performed independently by at least 2 individuals. Search terms included: brain machine or computer interfaces, neurofeedback and motor; however, only studies requiring a motor attempt, versus motor imagery, were retained. Data extraction included participant characteristics, study design details and motor outcomes. Results From 5109 papers, 139 full texts were reviewed with 23 unique studies identified. All utilized EEG and, except for one, were on the stroke population. The most commonly reported functional outcomes were the Fugl-Meyer Assessment (FMA; n = 13) and the Action Research Arm Test (ARAT; n = 6) which were then utilized to assess effectiveness, evaluate design features, and correlate with training doses. Statistically and functionally significant pre-to post training changes were seen in FMA, but not ARAT. Results did not differ between robotic and electrical stimulation feedback paradigms. Notably, FMA outcomes were positively correlated with training dose. Conclusion This review on BCI-based neurofeedback training confirms previous findings of effectiveness in improving motor outcomes with some evidence of enhanced neuroplasticity in adults with stroke. Associative learning paradigms have emerged more recently which may be particularly feasible and effective methods for Neurorehabilitation. More clinical trials in pediatric and adult neurorehabilitation to refine methods and doses and to compare to other evidence-based training strategies are warranted.

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“…A combination of two elements -a trainer for the development of motor skills, transcranial magnetic SEC 2019 will allow people to recover more effectively and faster. Robotic devices will allow increasing, first of all, the intensity and duration of passive movements, and stimulation by means of biofeedback promotes restoration of brain integrative activity [9]. In addition, similar studies with a similar design were carried out by scientists from Canada in 2019, as a result of which it was found that this collaboration really helps people in recovery [10].…”

Section: Development Of a Hardware-software Complex For Stroke Rehabimentioning

confidence: 99%

Justification of a New Solution in the Field of Stroke Rehabilitation

Gareev

Kulemin

2020

KEG

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This research consists in the creation of a hardware-software complex for the rehabilitation of stroke patients. The device is designed for rehabilitation of patients after acute disorders of cerebral blood circulation with severe neurotraumas. The device is based on two existing rehabilitation technologies: the method of transcranial brain stimulation with the help of a magnetic neurostimulator, and the development of hand motility with the help of gloves. The combination of these two methods will allow patients to recover effectively, even at home, after they was suffering from neurotrauma. Keywords: stroke, ischemic, rehabilitation, biofeedback, paresis, brain, computer, mechanized glove, hardware, and software complex, CNS diseases, integration, TMS, magnetic neurostimulator

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Rehabilitation of post stroke patients using a bioengineering system "brain-computer interface + exoskeleton"

Cited by 16 publications

References 23 publications

Neurophysiological foundations and practical realizations of the brain–machine interfaces in the technology in neurological rehabilitation

Neurophysiological foundations and practical realizations of the brain–machine interfaces in the technology in neurological rehabilitation

Determining optimal mobile neurofeedback methods for motor neurorehabilitation in children and adults with non-progressive neurological disorders: a scoping review

Justification of a New Solution in the Field of Stroke Rehabilitation

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