Combining electric brain stimulation and source-based brain-machine interface (BMI) training in neurorehabilitation of chronic stroke

In this study, a closed-loop brain stimulation control system scheme for epilepsy seizure abatement is designed by brain-machine interface (BMI) technique. In the controller design process, the practical parametric uncertainties involving cerebral blood flow, glucose metabolism, blood oxygen level dependence, and electromagnetic disturbances in signal control are considered. An appropriate transformation is introduced to express the system in regular form for design and analysis. Then, sufficient conditions are developed such that the sliding motion is asymptotically stable. Combining Caputo fractional order definition and neural network (NN), a finite time fractional order sliding mode (FFOSM) controller is designed to guarantee reachability of the sliding mode. The stability and reachability analysis of the closed-loop tracking control system gives the guideline of parameter selection, and simulation results based on comprehensive comparisons are carried out to demonstrate the effectiveness of proposed approach.

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Residual Upper Arm Motor Function Primes Innervation of Paretic Forearm Muscles in Chronic Stroke after Brain-Machine Interface (BMI) Training

PLoS ONE ◽

10.1371/journal.pone.0140161 ◽

2015 ◽

Vol 10 (10) ◽

pp. e0140161 ◽

Cited By ~ 10

Author(s):

Marco Rocha Curado ◽

Eliana Garcia Cossio ◽

Doris Broetz ◽

Manuel Agostini ◽

Woosang Cho ◽

...

Keyword(s):

Motor Function ◽

Chronic Stroke ◽

Brain Machine Interface ◽

Upper Arm ◽

Forearm Muscles ◽

Machine Interface

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Brain–Machine Interface Induced Morpho-Functional Remodeling of the Neural Motor System in Severe Chronic Stroke

Neurotherapeutics ◽

10.1007/s13311-019-00816-2 ◽

2019 ◽

Vol 17 (2) ◽

pp. 635-650

Author(s):

Andrea Caria ◽

Josué Luiz Dalboni da Rocha ◽

Giuseppe Gallitto ◽

Niels Birbaumer ◽

Ranganatha Sitaram ◽

...

Keyword(s):

Motor System ◽

Chronic Stroke ◽

Brain Machine Interface ◽

Functional Remodeling ◽

Machine Interface

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A neurorehabilitation therapy based on a brain machine interface (BMI) for the motor function restoration of the upper limb in chronic stroke patients.

http://isrctn.com/ ◽

10.1186/isrctn10150672 ◽

2015 ◽

Author(s):

Ander Ramos-Murguialday

Keyword(s):

Motor Function ◽

Upper Limb ◽

Chronic Stroke ◽

Brain Machine Interface ◽

Stroke Patients ◽

Machine Interface

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Collecting Data and the Status of the Research Subject in Brain-Machine Interface Research in Chronic Stroke Rehabilitation

Somatechnics ◽

10.3366/soma.2019.0282 ◽

2019 ◽

Vol 9 (2-3) ◽

pp. 244-263

Author(s):

Melike Şahinol

Keyword(s):

Patient Care ◽

Stroke Rehabilitation ◽

Scientific Data ◽

Chronic Stroke ◽

Brain Machine Interface ◽

Research Subject ◽

Data Generation ◽

Actual Patient ◽

The Status ◽

Machine Interface

Brain-Machine Interface (BMI) has moved to the forefront of neuroscientific trials as it not only establishes a direct communication pathway between the brain and an external device but enables the measuring of brain activity in research subjects whereby data is collected. With special emphasis on the somatechnics of BMI, this article analyses the relationship between this data collection process and actual patient care in chronic stroke rehabilitation. In doing so, it focuses on behaviour patterns associated with the use of specific technology that enables the scientist to use certain techniques in order to establish a hierarchy among research participants. In this hierarchy the scientist assumes a superior status to the research subject, an occurrence that is partially supported by using BMI technology and partially initiated by the need to generate data. Based on ethnographic studies of BMI research in hospitals and laboratories, the article outlines how this hierarchical structure in neuroscientific research leads to ethical questions in the quality of actual patient care. It continues to explore how patient care is affected mainly as a result of the changing status of the research subject from an initial bio-technical form that produces data in real time and whose brain is understood as an epistemic object to the patient being considered a ‘perceptible’ or ‘plausible gestalt’ ( Lindemann 2009 ) and therefore a social person. Although focus lies on the status of the research subject as part of actual patient care, scientific data generation remains indispensable for medical advancement; that is curing the patient. The article draws conclusion by addressing this very problem and therewith intends to contribute empirically based insights to a socially relevant discourse on neuroscientific trials. 1

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