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      Control of an Ambulatory Exoskeleton with a Brain–Machine Interface for Spinal Cord Injury Gait Rehabilitation

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          Abstract

          The closed-loop control of rehabilitative technologies by neural commands has shown a great potential to improve motor recovery in patients suffering from paralysis. Brain–machine interfaces (BMI) can be used as a natural control method for such technologies. BMI provides a continuous association between the brain activity and peripheral stimulation, with the potential to induce plastic changes in the nervous system. Paraplegic patients, and especially the ones with incomplete injuries, constitute a potential target population to be rehabilitated with brain-controlled robotic systems, as they may improve their gait function after the reinforcement of their spared intact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatory exoskeleton—without any weight or balance support—for gait rehabilitation of incomplete spinal cord injury (SCI) patients. The integrated system was validated with three healthy subjects, and its viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm, the electroencephalographic signals of the subjects were used to decode their gait intention and to trigger the movements of the exoskeleton. We designed a protocol with a special emphasis on safety, as patients with poor balance were required to stand and walk. We continuously monitored their fatigue and exertion level, and conducted usability and user-satisfaction tests after the experiments. The results show that, for the three healthy subjects, 84.44 ± 14.56% of the trials were correctly decoded. Three out of four patients performed at least one successful BMI session, with an average performance of 77.6 1 ± 14.72%. The shared control strategy implemented (i.e., the exoskeleton could only move during specific periods of time) was effective in preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22 ± 16.69% and 40.45 ± 16.98% of the trials (for healthy subjects and patients, respectively) would have suffered from unexpected activations (i.e., false positives) without the proposed control strategy. All the patients showed low exertion and fatigue levels during the performance of the experiments. This paper constitutes a proof-of-concept study to validate the feasibility of a BMI to control an ambulatory exoskeleton by patients with incomplete paraplegia (i.e., patients with good prognosis for gait rehabilitation).

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          Brain-machine interfaces: past, present and future.

          Since the original demonstration that electrical activity generated by ensembles of cortical neurons can be employed directly to control a robotic manipulator, research on brain-machine interfaces (BMIs) has experienced an impressive growth. Today BMIs designed for both experimental and clinical studies can translate raw neuronal signals into motor commands that reproduce arm reaching and hand grasping movements in artificial actuators. Clearly, these developments hold promise for the restoration of limb mobility in paralyzed subjects. However, as we review here, before this goal can be reached several bottlenecks have to be passed. These include designing a fully implantable biocompatible recording device, further developing real-time computational algorithms, introducing a method for providing the brain with sensory feedback from the actuators, and designing and building artificial prostheses that can be controlled directly by brain-derived signals. By reaching these milestones, future BMIs will be able to drive and control revolutionary prostheses that feel and act like the human arm.
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            Brain-machine interface in chronic stroke rehabilitation: a controlled study.

            Chronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain-machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double-blind sham-controlled design proof of concept study. Thirty-two chronic stroke patients with severe hand weakness were randomly assigned to 2 matched groups and participated in 17.8 ± 1.4 days of training rewarding desynchronization of ipsilesional oscillatory sensorimotor rhythms with contingent online movements of hand and arm orthoses (experimental group, n = 16). In the control group (sham group, n = 16), movements of the orthoses occurred randomly. Both groups received identical behavioral physiotherapy immediately following BMI training or the control intervention. Upper limb motor function scores, electromyography from arm and hand muscles, placebo-expectancy effects, and functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent activity were assessed before and after intervention. A significant group × time interaction in upper limb (combined hand and modified arm) Fugl-Meyer assessment (cFMA) motor scores was found. cFMA scores improved more in the experimental than in the control group, presenting a significant improvement of cFMA scores (3.41 ± 0.563-point difference, p = 0.018) reflecting a clinically meaningful change from no activity to some in paretic muscles. cFMA improvements in the experimental group correlated with changes in fMRI laterality index and with paretic hand electromyography activity. Placebo-expectancy scores were comparable for both groups. The addition of BMI training to behaviorally oriented physiotherapy can be used to induce functional improvements in motor function in chronic stroke patients without residual finger movements and may open a new door in stroke neurorehabilitation. Copyright © 2013 American Neurological Association.
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              Incidence, prevalence and epidemiology of spinal cord injury: what learns a worldwide literature survey?

              Literature survey. To provide an overview of the literature data on incidence, prevalence and epidemiology of spinal cord injury (SCI) worldwide and to study their evolution since 1977. University Antwerp. The literature from 1995 onwards was searched on Pubmed. To include evolutionary data, we incorporated the results of three older studies. Two studies gave prevalence of SCI, and 17 incidence of SCI. The published data on prevalence of SCI was insufficient to consider the range of 223-755 per million inhabitants to be representative for a worldwide estimate. Reported incidence of SCI lies between 10.4 and 83 per million inhabitants per year. One-third of patients with SCI are reported to be tetraplegic and 50% of patients with SCI to have a complete lesion. The mean age of patients sustaining their injury at is reported as 33 years old, and the sex distribution (men/women) as 3.8/1. There is a need for improved registration of SCI, and publication of the findings in many parts of the world. This survey pleads for uniformity in methodology. The data show that the reported incidence and prevalence have not changed substantially over the past 30 years. Data from Northern America and Europe show higher figures for incidence, but prevalence figures have remained the same. Epidemiology of SCI seems to have changed during the last decades with a higher percentage of tetraplegia and of complete lesions. If such evolution is present worldwide, how it could eventually be prevented needs to be studied. Not applicable.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                03 August 2016
                2016
                : 10
                : 359
                Affiliations
                [1] 1Departamento de Informática e Ingeniería de Sistemas, University of Zaragoza Zaragoza, Spain
                [2] 2Instituto de Investigación en Ingeniería de Aragón (I3A) Zaragoza, Spain
                [3] 3Biomechanics and Technical Aids Unit, National Hospital for Spinal Cord Injury Toledo, Spain
                [4] 4Institute for Bioengineering of Catalunya, Universitat Politécnica de Catalunya Barcelona, Spain
                [5] 5Bit & Brain Technologies Zaragoza, Spain
                Author notes

                Edited by: Timothée Levi, University of Bordeaux 1, France

                Reviewed by: Jose Luis Contreras-Vidal, University of Houston, USA; Ren Xu, University of Göttingen, Germany

                *Correspondence: Eduardo López-Larraz edulop@ 123456unizar.es

                This article was submitted to Neuroprosthetics, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2016.00359
                4971110
                27536214
                c0d91b66-1d2c-4467-a1d8-b84e3c88557b
                Copyright © 2016 López-Larraz, Trincado-Alonso, Rajasekaran, Pérez-Nombela, del-Ama, Aranda, Minguez, Gil-Agudo and Montesano.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 29 January 2016
                : 19 July 2016
                Page count
                Figures: 4, Tables: 6, Equations: 0, References: 61, Pages: 15, Words: 12643
                Funding
                Funded by: Ministerio de Ciencia e Innovación 10.13039/501100004837
                Award ID: HYPER-CSD2009-00067
                Categories
                Neuroscience
                Original Research

                Neurosciences
                spinal cord injury (sci),brain machine interfaces (bmi),ambulatory exoskeletons,gait rehabilitation,movement intention decoding,electroencephalography (eeg),event related desynchronization (erd),movement related cortical potentials (mrcp)

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