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      Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

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      a , b , c , d , e , f , g , h , i , j , g , k , l , m , n , o , p , q , r , s , t ,   u , v , w , x , t , y , z , aa , bb , cc
      Brain computer interfaces (Abingdon, England)
      Brain–computer interface, Brain–machine interface, neuroprosthetics, conference

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          Abstract

          The Sixth International Brain–Computer Interface (BCI) Meeting was held 30 May–3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain–machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.

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          Most cited references260

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          High-performance neuroprosthetic control by an individual with tetraplegia.

          Paralysis or amputation of an arm results in the loss of the ability to orient the hand and grasp, manipulate, and carry objects, functions that are essential for activities of daily living. Brain-machine interfaces could provide a solution to restoring many of these lost functions. We therefore tested whether an individual with tetraplegia could rapidly achieve neurological control of a high-performance prosthetic limb using this type of an interface. We implanted two 96-channel intracortical microelectrodes in the motor cortex of a 52-year-old individual with tetraplegia. Brain-machine-interface training was done for 13 weeks with the goal of controlling an anthropomorphic prosthetic limb with seven degrees of freedom (three-dimensional translation, three-dimensional orientation, one-dimensional grasping). The participant's ability to control the prosthetic limb was assessed with clinical measures of upper limb function. This study is registered with ClinicalTrials.gov, NCT01364480. The participant was able to move the prosthetic limb freely in the three-dimensional workspace on the second day of training. After 13 weeks, robust seven-dimensional movements were performed routinely. Mean success rate on target-based reaching tasks was 91·6% (SD 4·4) versus median chance level 6·2% (95% CI 2·0-15·3). Improvements were seen in completion time (decreased from a mean of 148 s [SD 60] to 112 s [6]) and path efficiency (increased from 0·30 [0·04] to 0·38 [0·02]). The participant was also able to use the prosthetic limb to do skilful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper limb function. No adverse events were reported. With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living. Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute. Copyright © 2013 Elsevier Ltd. All rights reserved.
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            Autism spectrum disorders: developmental disconnection syndromes.

            Autism is a common and heterogeneous childhood neurodevelopmental disorder. Analogous to broad syndromes such as mental retardation, autism has many etiologies and should be considered not as a single disorder but, rather, as 'the autisms'. However, recent genetic findings, coupled with emerging anatomical and functional imaging studies, suggest a potential unifying model in which higher-order association areas of the brain that normally connect to the frontal lobe are partially disconnected during development. This concept of developmental disconnection can accommodate the specific neurobehavioral features that are observed in autism, their emergence during development, and the heterogeneity of autism etiology, behaviors and cognition.
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              The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility.

              To determine the measurement properties and diagnostic utility of the JFK Coma Recovery Scale-Revised (CRS-R). Analysis of interrater and test-retest reliability, internal consistency, concurrent validity, and diagnostic accuracy. Acute inpatient brain injury rehabilitation hospital. Convenience sample of 80 patients with severe acquired brain injury admitted to an inpatient Coma Intervention Program with a diagnosis of either vegetative state (VS) or minimally conscious state (MCS). Not applicable. The CRS-R, the JFK Coma Recovery Scale (CRS), and the Disability Rating Scale (DRS). Interrater and test-retest reliability were high for CRS-R total scores. Subscale analysis showed moderate to high interrater and test-retest agreement although systematic differences in scoring were noted on the visual and oromotor/verbal subscales. CRS-R total scores correlated significantly with total scores on the CRS and DRS indicating acceptable concurrent validity. The CRS-R was able to distinguish 10 patients in an MCS who were otherwise misclassified as in a VS by the DRS. The CRS-R can be administered reliably by trained examiners and repeated measurements yield stable estimates of patient status. CRS-R subscale scores demonstrated good agreement across raters and ratings but should be used cautiously because some scores were underrepresented in the current study. The CRS-R appears capable of differentiating patients in an MCS from those in a VS.
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                Author and article information

                Journal
                101625088
                42691
                Brain Comput Interfaces (Abingdon)
                Brain Comput Interfaces (Abingdon)
                Brain computer interfaces (Abingdon, England)
                2326-263X
                2326-2621
                17 June 2017
                30 January 2017
                2017
                17 November 2017
                : 4
                : 1-2
                : 3-36
                Affiliations
                [a ]Department of Physical Medicine and Rehabilitation, Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
                [b ]G.Tec Medical Engineering GmbH, Guger Technologies OG, Schiedlberg, Austria
                [c ]Psychology Department, Northern Michigan University, Marquette, MI, USA
                [d ]Team PhyPA, Biological Psychology and Neuroergonomics, Technical University of Berlin, Berlin, Germany
                [e ]Auckland University of Technology, New Zealand
                [f ]Cluster of Excellence BrainLinks-BrainTools, University of Freiburg, Germany
                [g ]Neuroscience Business Unit, Starlab Barcelona SLU, Barcelona, Spain
                [h ]Ctr. For Neurorestoration and Neurotechnology, Rehab. R&D Service, Dept. of VA Medical Center, School of Engineering, Brown University, Providence, RI, USA
                [i ]Institute of Neural Engineering, BCI- Lab, Graz University of Technology, Graz, Austria
                [j ]Section Experimental Neurorehabilitation, Spinal Cord Injury Center, University Hospital in Heidelberg, Heidelberg, Germany
                [k ]Neuroelectrics Inc., Boston, USA
                [l ]Institute: Laboratoire Interdisciplinaire Sciences Innovations Sociétés (LISIS), Université Paris-Est Marne-la-Vallée, MARNE-LA-VALLÉE, France
                [m ]Dept Neurology & Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
                [n ]Faculty EEMCS, Enschede, University of Twente, The Netherlands & Imagineering Institute, Iskandar, Malaysia
                [o ]Institute of Neural Engineering, BCI- Lab, Graz University of Technology, Graz, Austria
                [p ]New York State Department of Health, National Center for Adaptive Neurotechnologies, Wadsworth Center, Albany, New York USA
                [q ]Clinical Neurophysiology, Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, IRCCS, Rome, Italy
                [r ]Human Research and Engineering Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD USA
                [s ]Machine Learning Group, Technical University of Berlin, Berlin, Germany
                [t ]Defitech Chair in Brain–machine Interface (CNBI), Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, EPFL-STI-CNBI, Campus Biotech H4, Geneva, Switzerland
                [u ]Cognitive Systems Lab, University of Bremen, Bremen, Germany
                [v ]Brain Mind Research Inst, Weill Cornell Medical College, Early Brain Injury and Recovery Lab, Burke Medical Research Inst, White Plains, New York, USA
                [w ]Department of Neurology, UC Irvine Brain Computer Interface Lab, University of California, Irvine, CA, USA
                [x ]Department of Physical Medicine and Rehabilitation, Department of Veterans Affairs, VA Pittsburgh Healthcare System, University of Pittsburgh, Pittsburgh, PA, USA
                [y ]Center for the Neural Basis of Cognition and Department Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
                [z ]Neuropsychology Lab, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany
                [aa ]Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA USA
                [bb ]Department of Computer Science, Colorado State University, Fort Collins, CO USA
                [cc ]Brain Center Rudolf Magnus, Dept Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
                Author notes
                Article
                NIHMS885257
                10.1080/2326263X.2016.1275488
                5693371
                29152523
                31747ef9-8f3d-4c82-9b56-ea61d439bcc6

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

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                brain–computer interface,brain–machine interface,neuroprosthetics,conference

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