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Closed-Loop Brain–Machine–Body Interfaces for Noninvasive Rehabilitation of Movement Disorders

Overview of attention for article published in Annals of Biomedical Engineering, May 2014
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Title
Closed-Loop Brain–Machine–Body Interfaces for Noninvasive Rehabilitation of Movement Disorders
Published in
Annals of Biomedical Engineering, May 2014
DOI 10.1007/s10439-014-1032-6
Pubmed ID
Authors

Frédéric D. Broccard, Tim Mullen, Yu Mike Chi, David Peterson, John R. Iversen, Mike Arnold, Kenneth Kreutz-Delgado, Tzyy-Ping Jung, Scott Makeig, Howard Poizner, Terrence Sejnowski, Gert Cauwenberghs

Abstract

Traditional approaches for neurological rehabilitation of patients affected with movement disorders, such as Parkinson's disease (PD), dystonia, and essential tremor (ET) consist mainly of oral medication, physical therapy, and botulinum toxin injections. Recently, the more invasive method of deep brain stimulation (DBS) showed significant improvement of the physical symptoms associated with these disorders. In the past several years, the adoption of feedback control theory helped DBS protocols to take into account the progressive and dynamic nature of these neurological movement disorders that had largely been ignored so far. As a result, a more efficient and effective management of PD cardinal symptoms has emerged. In this paper, we review closed-loop systems for rehabilitation of movement disorders, focusing on PD, for which several invasive and noninvasive methods have been developed during the last decade, reducing the complications and side effects associated with traditional rehabilitation approaches and paving the way for tailored individual therapeutics. We then present a novel, transformative, noninvasive closed-loop framework based on force neurofeedback and discuss several future developments of closed-loop systems that might bring us closer to individualized solutions for neurological rehabilitation of movement disorders.

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Geographical breakdown

Country Count As %
United States 6 3%
United Kingdom 1 <1%
Czechia 1 <1%
Unknown 202 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 21%
Researcher 27 13%
Student > Master 26 12%
Student > Bachelor 20 10%
Professor > Associate Professor 12 6%
Other 39 19%
Unknown 41 20%
Readers by discipline Count As %
Engineering 43 20%
Neuroscience 35 17%
Medicine and Dentistry 17 8%
Computer Science 13 6%
Agricultural and Biological Sciences 10 5%
Other 36 17%
Unknown 56 27%