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A Parallel Robot with Torque Monitoring for Brachial Monoparesis Rehabilitation Tasks. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11219932] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Robots for rehabilitation tasks require a high degree of safety for the interaction with both the patients and for the operators. In particular, high safety is a stable and intuitive control of the moving elements of the system combined with an external system of sensors able to monitor the position of every aspect of the rehabilitation system (operator, robot, and patient) and overcome in a certain measure all the events that may occur during the robotic rehabilitation procedure. This paper presents the development of an internal torque monitoring system for ASPIRE. This is a parallel robot designed for shoulder rehabilitation, which enables the use of strategies towards developing a HRI (human–robot interaction) system for the therapy. A complete analysis regarding the components of the robotic system is carried out with the purpose of determining the dynamic behavior of the system. Next, the proposed torque monitoring system is developed with respect to the previously obtained data. Several experimental tests are performed using healthy subjects being equipped with a series of biomedical sensors with the purpose of validating the proposed torque monitoring strategy and, at the same time, to satisfy the degree of safety that is requested by the medical procedure.
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Esposito D, Centracchio J, Andreozzi E, Gargiulo GD, Naik GR, Bifulco P. Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey. SENSORS 2021; 21:s21206863. [PMID: 34696076 PMCID: PMC8540117 DOI: 10.3390/s21206863] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/30/2021] [Accepted: 10/12/2021] [Indexed: 12/03/2022]
Abstract
As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complexity, so their usefulness should be carefully evaluated for the specific application.
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Affiliation(s)
- Daniele Esposito
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples “Federico II”, 80125 Naples, Italy; (D.E.); (J.C.); (E.A.); (P.B.)
| | - Jessica Centracchio
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples “Federico II”, 80125 Naples, Italy; (D.E.); (J.C.); (E.A.); (P.B.)
| | - Emilio Andreozzi
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples “Federico II”, 80125 Naples, Italy; (D.E.); (J.C.); (E.A.); (P.B.)
| | - Gaetano D. Gargiulo
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2747, Australia;
- The MARCS Institute, Western Sydney University, Penrith, NSW 2751, Australia
| | - Ganesh R. Naik
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2747, Australia;
- The Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA 5042, Australia
- Correspondence:
| | - Paolo Bifulco
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples “Federico II”, 80125 Naples, Italy; (D.E.); (J.C.); (E.A.); (P.B.)
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Palumbo A, Gramigna V, Calabrese B, Ielpo N. Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:6285. [PMID: 34577493 PMCID: PMC8473300 DOI: 10.3390/s21186285] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 09/09/2021] [Accepted: 09/14/2021] [Indexed: 02/07/2023]
Abstract
The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices, and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges to improve their independence, facilitate participation in activities, thus enhancing overall well-being and preventing impairments. This systematic review provides state-of-the-art applications of EEG-based BCIs, particularly those using motor-imagery (MI) data, to wheelchair control and movement. It presents a thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection, and classification techniques used as well as on wheelchair components and performance evaluation. The results provided in this paper could highlight the limitations of current biomedical instrumentations applied to people with severe disabilities and bring focus to innovative research topics.
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Affiliation(s)
- Arrigo Palumbo
- Department of Medical and Surgical Sciences, “Magna Græcia” University, 88100 Catanzaro, Italy; (A.P.); (B.C.); (N.I.)
| | - Vera Gramigna
- Neuroscience Research Center, Magna Græcia University, 88100 Catanzaro, Italy
| | - Barbara Calabrese
- Department of Medical and Surgical Sciences, “Magna Græcia” University, 88100 Catanzaro, Italy; (A.P.); (B.C.); (N.I.)
| | - Nicola Ielpo
- Department of Medical and Surgical Sciences, “Magna Græcia” University, 88100 Catanzaro, Italy; (A.P.); (B.C.); (N.I.)
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Simon C, Bolton DAE, Kennedy NC, Soekadar SR, Ruddy KL. Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation. Front Neurosci 2021; 15:699428. [PMID: 34276299 PMCID: PMC8282929 DOI: 10.3389/fnins.2021.699428] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/08/2021] [Indexed: 12/18/2022] Open
Abstract
Brain-computer interfaces (BCIs) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, the BCI can be used to translate neural signals associated with movement intentions into tangible feedback for the patient, when they are unable to generate functional movement themselves. Clinical interest in BCI is growing rapidly, as it would facilitate rehabilitation to commence earlier following brain damage and provides options for patients who are unable to partake in traditional physical therapy. However, substantial challenges with existing BCI implementations have prevented its widespread adoption. Recent advances in knowledge and technology provide opportunities to facilitate a change, provided that researchers and clinicians using BCI agree on standardisation of guidelines for protocols and shared efforts to uncover mechanisms. We propose that addressing the speed and effectiveness of learning BCI control are priorities for the field, which may be improved by multimodal or multi-stage approaches harnessing more sensitive neuroimaging technologies in the early learning stages, before transitioning to more practical, mobile implementations. Clarification of the neural mechanisms that give rise to improvement in motor function is an essential next step towards justifying clinical use of BCI. In particular, quantifying the unknown contribution of non-motor mechanisms to motor recovery calls for more stringent control conditions in experimental work. Here we provide a contemporary viewpoint on the factors impeding the scalability of BCI. Further, we provide a future outlook for optimal design of the technology to best exploit its unique potential, and best practices for research and reporting of findings.
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Affiliation(s)
- Colin Simon
- Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David A. E. Bolton
- Department of Kinesiology and Health Science, Utah State University, Logan, UT, United States
| | - Niamh C. Kennedy
- School of Psychology, Ulster University, Coleraine, United Kingdom
| | - Surjo R. Soekadar
- Clinical Neurotechnology Laboratory, Neurowissenschaftliches Forschungszentrum, Department of Psychiatry and Neurosciences, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Kathy L. Ruddy
- Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Ireland
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Bobrova EV, Reshetnikova VV, Vershinina EA, Grishin AA, Bobrov PD, Frolov AA, Gerasimenko YP. Success of Hand Movement Imagination Depends on Personality Traits, Brain Asymmetry, and Degree of Handedness. Brain Sci 2021; 11:853. [PMID: 34202413 PMCID: PMC8301954 DOI: 10.3390/brainsci11070853] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/23/2021] [Accepted: 06/23/2021] [Indexed: 12/05/2022] Open
Abstract
Brain-computer interfaces (BCIs), based on motor imagery, are increasingly used in neurorehabilitation. However, some people cannot control BCI, predictors of this are the features of brain activity and personality traits. It is not known whether the success of BCI control is related to interhemispheric asymmetry. The study was conducted on 44 BCI-naive subjects and included one BCI session, EEG-analysis, 16PF Cattell Questionnaire, estimation of latent left-handedness, and of subjective complexity of real and imagery movements. The success of brain states recognition during imagination of left hand (LH) movement compared to the rest is higher in reserved, practical, skeptical, and not very sociable individuals. Extraversion, liveliness, and dominance are significant for the imagination of right hand (RH) movements in "pure" right-handers, and sensitivity in latent left-handers. Subjective complexity of real LH and of imagery RH movements correlates with the success of brain states recognition in the imagination of movement of LH compared to RH and depends on the level of handedness. Thus, the level of handedness is the factor influencing the success of BCI control. The data are supposed to be connected with hemispheric differences in motor control, lateralization of dopamine, and may be important for rehabilitation of patients after a stroke.
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Affiliation(s)
- Elena V. Bobrova
- Pavlov Institute of Physiology of the Russian Academy of Sciences, 199034 Saint-Petersburg, Russia; (V.V.R.); (E.A.V.); (A.A.G.); (Y.P.G.)
| | - Varvara V. Reshetnikova
- Pavlov Institute of Physiology of the Russian Academy of Sciences, 199034 Saint-Petersburg, Russia; (V.V.R.); (E.A.V.); (A.A.G.); (Y.P.G.)
| | - Elena A. Vershinina
- Pavlov Institute of Physiology of the Russian Academy of Sciences, 199034 Saint-Petersburg, Russia; (V.V.R.); (E.A.V.); (A.A.G.); (Y.P.G.)
| | - Alexander A. Grishin
- Pavlov Institute of Physiology of the Russian Academy of Sciences, 199034 Saint-Petersburg, Russia; (V.V.R.); (E.A.V.); (A.A.G.); (Y.P.G.)
| | - Pavel D. Bobrov
- Institute of Translational Medicine of Pirogov of Russian National Research Medical University, 117997 Moscow, Russia; (P.D.B.); (A.A.F.)
- Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, 117485 Moscow, Russia
| | - Alexander A. Frolov
- Institute of Translational Medicine of Pirogov of Russian National Research Medical University, 117997 Moscow, Russia; (P.D.B.); (A.A.F.)
- Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, 117485 Moscow, Russia
| | - Yury P. Gerasimenko
- Pavlov Institute of Physiology of the Russian Academy of Sciences, 199034 Saint-Petersburg, Russia; (V.V.R.); (E.A.V.); (A.A.G.); (Y.P.G.)
- Department of Physiology and Biophysics, University of Louisville, Louisville, KY 40292, USA
- Kentucky Spinal Cord Injury Research Center, Frazier Rehab Institute, University of Louisville, UofL Health, Louisville, KY 40202, USA
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Silva EMGS, Holanda LJ, Coutinho GKB, Andrade FS, Nascimento GIS, Nagem DAP, Valentim RADM, Lindquist AR. Effects of Active Upper Limb Orthoses Using Brain-Machine Interfaces for Rehabilitation of Patients With Neurological Disorders: Protocol for a Systematic Review and Meta-Analysis. Front Neurosci 2021; 15:661494. [PMID: 34248477 PMCID: PMC8264786 DOI: 10.3389/fnins.2021.661494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 05/04/2021] [Indexed: 12/01/2022] Open
Abstract
Introduction: The field of brain–machine interfaces (BMI) for upper limb (UL) orthoses is growing exponentially due to improvements in motor performance, quality of life, and functionality of people with neurological diseases. Considering this, we planned a systematic review to investigate the effects of BMI-controlled UL orthoses for rehabilitation of patients with neurological disorders. Methods: This systematic review and meta-analysis protocol was elaborated according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P 2015) and Cochrane Handbook for Systematic Reviews of Interventions. A search will be conducted on Pubmed, IEEE Xplore Digital Library, Medline, and Web of Science databases without language and year restrictions, and Patents Scope, Patentlens, and Google Patents websites in English, Spanish, French, German, and Portuguese between 2011 and 2021. Two independent reviewers will include randomized controlled trials and quasi-experimental studies using BMI-controlled active UL orthoses to improve human movement. Studies must contain participants aged >18 years, diagnosed with neurological disorders, and with impaired UL movement. Three independent reviewers will conduct the same procedure for patents. Evidence quality and risk of bias will be evaluated following the Cochrane collaboration by two review authors. Meta-analysis will be conducted in case of homogeneity between groups. Otherwise, a narrative synthesis will be performed. Data will be inserted into a table containing physical description, UL orthoses control system, and effect of BMI-controlled orthoses. Discussion: BMI-controlled orthoses can assist individuals in several routine activities and provide functional independence and sense of overcoming limitations imposed by the underlying disease. These benefits will also be associated with orthoses descriptions, safety, portability, adverse events, and tools used to assess UL motor performance in patients with neurological disorders. PROSPERO Registration Number: CRD42020182195.
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Affiliation(s)
- Emília M G S Silva
- Laboratory of Intervention and Analysis of Movement, Department of Physical Therapy, Federal University of Rio Grande do Norte, Natal, Brazil.,Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Ledycnarf J Holanda
- Laboratory of Intervention and Analysis of Movement, Department of Physical Therapy, Federal University of Rio Grande do Norte, Natal, Brazil.,Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Gustavo K B Coutinho
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Fernanda S Andrade
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil.,Department of Biomedical Engineering, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Gabriel I S Nascimento
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Danilo A P Nagem
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil.,Department of Biomedical Engineering, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Ricardo A de M Valentim
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil.,Department of Biomedical Engineering, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Ana Raquel Lindquist
- Laboratory of Intervention and Analysis of Movement, Department of Physical Therapy, Federal University of Rio Grande do Norte, Natal, Brazil.,Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
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Xue X, Tu H, Deng Z, Zhou L, Li N, Wang X. Effects of brain-computer interface training on upper limb function recovery in stroke patients: A protocol for systematic review and meta-analysis. Medicine (Baltimore) 2021; 100:e26254. [PMID: 34115016 PMCID: PMC8202595 DOI: 10.1097/md.0000000000026254] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND In recent years, with the development of medical technology and the increase of inter-disciplinary cooperation technology, new methods in the field of artificial intelligence medicine emerge in an endless stream. Brain-computer interface (BCI), as a frontier technology of multidisciplinary integration, has been widely used in various fields. Studies have shown that BCI-assisted training can improve upper limb function in stroke patients, but its effect is still controversial and lacks evidence-based evidence, which requires further exploration and confirmation. Therefore, the main purpose of this paper is to systematically evaluate the efficacy of different BCI-assisted training on upper limb function recovery in stroke patients, to provide a reference for the application of BCI-assisted technology in stroke rehabilitation. METHODS We will search PubMed, Web of Science, The Cochrane Library, Chinese National Knowledge Infrastructure Database, Wanfang Data, Weipu Electronics, and other databases (from the establishment to February 2021) for full text in Chinese and English. Randomized controlled trials were collected to examine the effect of BCI-assisted training on upper limb functional recovery in stroke patients. We will consider inclusion, select high-quality articles for data extraction and analysis, and summarize the intervention effect of BCI-assisted training on the upper limb function of stroke patients. Two reviewers will screen titles, abstracts, and full texts independently according to inclusion criteria; Data extraction and risk of bias assessment were performed in the included studies. We will use a hierarchy of recommended assessment, development, and assessment methods to assess the overall certainty of the evidence and report findings accordingly. Endnote X8 will be applied in selecting the study, Review Manager 5.3 will be applied in analyzing and synthesizing. RESULTS The results will provide evidence for judging whether BCI is effective and safe in improving upper limb function in patients with stroke. CONCLUSION Our study will provide reliable evidence for the effect of BCI technology on the improvement of upper limb function in stroke patients. PROSPERO REGISTRATION NUMBER CRD42021250378.
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Affiliation(s)
- Xiali Xue
- Institute of Sports Medicine and Health, Chengdu Sport University
| | - Huan Tu
- Institute of Sports Medicine and Health, Chengdu Sport University
| | - Zhongyi Deng
- Institute of Sports Medicine and Health, Chengdu Sport University
| | - Ling Zhou
- School of Sports Medicine and Health, Chengdu Sport University, Chengdu, Sichuan Province
| | - Ning Li
- Institute of Sports Medicine and Health, Chengdu Sport University
| | - Xiaokun Wang
- The People's Hospital of Mancheng District, Baoding, Hebei Province, China
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A System for Neuromotor Based Rehabilitation on a Passive Robotic Aid. SENSORS 2021; 21:s21093130. [PMID: 33946361 PMCID: PMC8124495 DOI: 10.3390/s21093130] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/24/2021] [Accepted: 04/26/2021] [Indexed: 12/23/2022]
Abstract
In the aging world population, the occurrence of neuromotor deficits arising from stroke and other medical conditions is expected to grow, demanding the design of new and more effective approaches to rehabilitation. In this paper, we show how the combination of robotic technologies with progress in exergaming methodologies may lead to the creation of new rehabilitation protocols favoring motor re-learning. To this end, we introduce the Track-Hold system for neuromotor rehabilitation based on a passive robotic arm and integrated software. A special configuration of weights on the robotic arm fully balances the weight of the patients’ arm, allowing them to perform a purely neurological task, overcoming the muscular effort of similar free-hand exercises. A set of adaptive and configurable exercises are proposed to patients through a large display and a graphical user interface. Common everyday tasks are also proposed for patients to learn again the associated actions in a persistent way, thus improving life independence. A data analysis module was also designed to monitor progress and compute indices of post-stroke neurological damage and Parkinsonian-type disorders. The system was tested in the lab and in a pilot project involving five patients in the post-stroke chronic stage with partial paralysis of the right upper limb, showing encouraging preliminary results.
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Abstract
Recent advances in brain-computer interface technology to restore and rehabilitate neurologic function aim to enable persons with disabling neurologic conditions to communicate, interact with the environment, and achieve other key activities of daily living and personal goals. Here we evaluate the principles, benefits, challenges, and future directions of brain-computer interfaces in the context of neurorehabilitation. We then explore the clinical translation of these technologies and propose an approach to facilitate implementation of brain-computer interfaces for persons with neurologic disease.
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Affiliation(s)
- Michael J Young
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - David J Lin
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- School of Engineering and Carney Institute for Brain Science, Brown University, Providence, Rhode Island
- Department of Veterans Affairs Medical Center, VA RR&D Center for Neurorestoration and Neurotechnology, Providence, Rhode Island
| | - Leigh R Hochberg
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- School of Engineering and Carney Institute for Brain Science, Brown University, Providence, Rhode Island
- Department of Veterans Affairs Medical Center, VA RR&D Center for Neurorestoration and Neurotechnology, Providence, Rhode Island
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