Principles of neurorehabilitation based on the brain-computer interface and biologically adequate control of the exoskeleton

Frolov, Alexander A.; Biryukova, E. V.; Bobrov, Pavel; Мокиенко, О. А.; Platonov, A K; Pryanichnikov, Valentin; Черникова, Л. А.

doi:10.1134/s0362119713020035

Cited by 32 publications

(10 citation statements)

References 64 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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“…Однако эффективность такого идеомоторного тренинга может быть далеко не полной из-за отсутствия объективного контроля со стороны пациента за яркостью и интенсивностью своих мысленных представлений движения в силу их субъективной природы. В этой связи особенно перспективными становятся реабилитаци-онные подходы, поддерживающие идеомоторную тренировку посредством технологии интерфейсов мозг-компьютер (ИМК) [7,8]. Эта технология позволяет электронно-вычислительными средствами детектировать проявление мысленных представлений движения в специфических изменениях характеристик отрезков электроэнцефалограммы (ЭЭГ), например, в виде десинхронизации сенсомоторного ритма, и в реальном времени трансформировать их в сигналы обратной связи (ОС) для пациента.…”

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Investigation of Characteristics of a Motor-Imagery Brain–Computer Interface with Quick-Response Tactile Feedback

Lukoyanov

Gordleeva

Grigorev

et al. 2018

Moscow Univ. Biol.Sci. Bull.

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“…Exoskeletons used successfully for neurorehabilitation involve devices reproducing the movements of the upper limb joints [8] and devices focused on simulation of walking. Among the latter, we can distinguish exoskeletons performing biomechanically correct movements in the hip and knee joints of the patient such as LOPES (lower extremity powered exoskeleton) [ Despite the fact that currently there are several types of BCI known [12,13], the most popular types of noninvasive BCI to control exoskeletons are the so-called synchronous BCIs based on registration of the operator EEG response to the external stimulus environment.…”

mentioning

confidence: 99%

Exoskeleton Control System Based on Motor-Imaginary Brain–Computer Interface

Gordleeva¹,

Lukoyanov²,

Mineev³

et al. 2017

Sovrem Tehnol Med

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The aim of the investigation was to develop the neuro-integrated control system for a lower-limb robotic exoskeleton (RE) using brain-computer interface (BCI) technology based on recognition of EEG patterns evoked by motor imagery of limb movement.Materials and Methods. The proposed neuro-integrated RE control system based on BCI technology consists of three main modules: EEG signal recording module, EEG signal classifier and the software for transmission of commands to RE. EEG patterns evoked by motor imagery are recognized by the classifier based on linear discriminant analysis that uses the features identified by spatial filtering applying CSP method for all types of commands pairwise. The proposed algorithms for classification of motor imagery patterns and user training techniques make it possible to reliably distinguish several (up to 4) different commands. After training and testing the classifier, the operator may proceed to control the external device, i.e. the lower-limb RE. RE control software has been developed for easy system customization. The software has a simple graphical user interface and allows the user to change the mapping of RE patterns and commands in the operation process.Results. As a result of testing in 14 healthy volunteers, the average accuracy of lower limb exoskeleton control based on the developed motor imagery BCI for three commands was found to average 70% in three sessions.Conclusion. The developed RE control system based on BCI technology offers fairly high accuracy for three commands. The operators successfully learn to practice motor imagery and operate the BCI contour, even if they have no previous experience of work with brainmachine interfaces.

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Principles of neurorehabilitation based on the brain-computer interface and biologically adequate control of the exoskeleton

Cited by 32 publications

References 64 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

Investigation of Characteristics of a Motor-Imagery Brain–Computer Interface with Quick-Response Tactile Feedback

Exoskeleton Control System Based on Motor-Imaginary Brain–Computer Interface

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