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Vasko JL, Aume L, Tamrakar S, Colachis SCI, Dunlap CF, Rich A, Meyers EC, Gabrieli D, Friedenberg DA. Increasing Robustness of Brain-Computer Interfaces Through Automatic Detection and Removal of Corrupted Input Signals. Front Neurosci 2022; 16:858377. [PMID: 35573306 PMCID: PMC9096265 DOI: 10.3389/fnins.2022.858377] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/15/2022] [Indexed: 11/29/2022] Open
Abstract
For brain–computer interfaces (BCIs) to be viable for long-term daily usage, they must be able to quickly identify and adapt to signal disruptions. Furthermore, the detection and mitigation steps need to occur automatically and without the need for user intervention while also being computationally tractable for the low-power hardware that will be used in a deployed BCI system. Here, we focus on disruptions that are likely to occur during chronic use that cause some recording channels to fail but leave the remaining channels unaffected. In these cases, the algorithm that translates recorded neural activity into actions, the neural decoder, should seamlessly identify and adjust to the altered neural signals with minimal inconvenience to the user. First, we introduce an adapted statistical process control (SPC) method that automatically identifies disrupted channels so that both decoding algorithms can be adjusted, and technicians can be alerted. Next, after identifying corrupted channels, we demonstrate the automated and rapid removal of channels from a neural network decoder using a masking approach that does not change the decoding architecture, making it amenable for transfer learning. Finally, using transfer and unsupervised learning techniques, we update the model weights to adjust for the corrupted channels without requiring the user to collect additional calibration data. We demonstrate with both real and simulated neural data that our approach can maintain high-performance while simultaneously minimizing computation time and data storage requirements. This framework is invisible to the user but can dramatically increase BCI robustness and usability.
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Affiliation(s)
- Jordan L Vasko
- Battelle Memorial Institute, Columbus, OH, United States
| | - Laura Aume
- Battelle Memorial Institute, Columbus, OH, United States
| | | | | | - Collin F Dunlap
- Battelle Memorial Institute, Columbus, OH, United States.,Department of Biomedical Engineering, The Ohio State University, Columbus, OH, United States
| | - Adam Rich
- Battelle Memorial Institute, Columbus, OH, United States
| | - Eric C Meyers
- Battelle Memorial Institute, Columbus, OH, United States
| | - David Gabrieli
- Battelle Memorial Institute, Columbus, OH, United States
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2
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Chandrasekaran S, Fifer M, Bickel S, Osborn L, Herrero J, Christie B, Xu J, Murphy RKJ, Singh S, Glasser MF, Collinger JL, Gaunt R, Mehta AD, Schwartz A, Bouton CE. Historical perspectives, challenges, and future directions of implantable brain-computer interfaces for sensorimotor applications. Bioelectron Med 2021; 7:14. [PMID: 34548098 PMCID: PMC8456563 DOI: 10.1186/s42234-021-00076-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 08/29/2021] [Indexed: 11/10/2022] Open
Abstract
Almost 100 years ago experiments involving electrically stimulating and recording from the brain and the body launched new discoveries and debates on how electricity, movement, and thoughts are related. Decades later the development of brain-computer interface technology began, which now targets a wide range of applications. Potential uses include augmentative communication for locked-in patients and restoring sensorimotor function in those who are battling disease or have suffered traumatic injury. Technical and surgical challenges still surround the development of brain-computer technology, however, before it can be widely deployed. In this review we explore these challenges, historical perspectives, and the remarkable achievements of clinical study participants who have bravely forged new paths for future beneficiaries.
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Affiliation(s)
- Santosh Chandrasekaran
- Neural Bypass and Brain Computer Interface Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Matthew Fifer
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Stephan Bickel
- The Human Brain Mapping Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Department of Neurosurgery, Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
| | - Luke Osborn
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Jose Herrero
- The Human Brain Mapping Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Breanne Christie
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Junqian Xu
- Departments of Radiology and Psychiatry, Baylor College of Medicine, Houston, TX, USA
| | - Rory K J Murphy
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA
| | - Sandeep Singh
- Good Shepherd Rehabilitation Hospital, Allentown, PA, USA
| | - Matthew F Glasser
- Departments of Radiology and Neuroscience, Washington University in St Louis, Saint Louis, MO, USA
| | - Jennifer L Collinger
- Rehabilitation Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Robert Gaunt
- Rehabilitation Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashesh D Mehta
- The Human Brain Mapping Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Department of Neurosurgery, Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
| | - Andrew Schwartz
- McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chad E Bouton
- Neural Bypass and Brain Computer Interface Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA.
- Department of Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA.
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3
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Bouton CE. Merging brain-computer interface and functional electrical stimulation technologies for movement restoration. HANDBOOK OF CLINICAL NEUROLOGY 2020; 168:303-309. [PMID: 32164861 DOI: 10.1016/b978-0-444-63934-9.00022-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BCI (brain-computer interface) and functional electrical stimulation (FES) technologies have advanced significantly over the last several decades. Recent efforts have involved the integration of these technologies with the goal of restoring functional movement in paralyzed patients. Implantable BCIs have provided neural recordings with increased spatial resolution and have been combined with sophisticated neural decoding algorithms and increasingly capable FES systems to advance efforts toward this goal. This chapter reviews historical developments that have occurred as the exciting fields of BCI and FES have evolved and now overlapped to allow new breakthroughs in medicine, targeting restoration of movement and lost function in users with disabilities.
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Affiliation(s)
- Chad E Bouton
- Center for Bioelectronic Medicine, Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, United States.
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4
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Lee MH, Kwon OY, Kim YJ, Kim HK, Lee YE, Williamson J, Fazli S, Lee SW. EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy. Gigascience 2019; 8:5304369. [PMID: 30698704 PMCID: PMC6501944 DOI: 10.1093/gigascience/giz002] [Citation(s) in RCA: 145] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Revised: 10/31/2018] [Accepted: 01/09/2019] [Indexed: 11/24/2022] Open
Abstract
Background Electroencephalography (EEG)-based brain-computer interface (BCI) systems are mainly divided into three major paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). Here, we present a BCI dataset that includes the three major BCI paradigms with a large number of subjects over multiple sessions. In addition, information about the psychological and physiological conditions of BCI users was obtained using a questionnaire, and task-unrelated parameters such as resting state, artifacts, and electromyography of both arms were also recorded. We evaluated the decoding accuracies for the individual paradigms and determined performance variations across both subjects and sessions. Furthermore, we looked for more general, severe cases of BCI illiteracy than have been previously reported in the literature. Results Average decoding accuracies across all subjects and sessions were 71.1% (± 0.15), 96.7% (± 0.05), and 95.1% (± 0.09), and rates of BCI illiteracy were 53.7%, 11.1%, and 10.2% for MI, ERP, and SSVEP, respectively. Compared to the ERP and SSVEP paradigms, the MI paradigm exhibited large performance variations between both subjects and sessions. Furthermore, we found that 27.8% (15 out of 54) of users were universally BCI literate, i.e., they were able to proficiently perform all three paradigms. Interestingly, we found no universally illiterate BCI user, i.e., all participants were able to control at least one type of BCI system. Conclusions Our EEG dataset can be utilized for a wide range of BCI-related research questions. All methods for the data analysis in this study are supported with fully open-source scripts that can aid in every step of BCI technology. Furthermore, our results support previous but disjointed findings on the phenomenon of BCI illiteracy.
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Affiliation(s)
- Min-Ho Lee
- Department of Brain and Cognitive Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea
| | - O-Yeon Kwon
- Department of Brain and Cognitive Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea
| | - Yong-Jeong Kim
- Department of Brain and Cognitive Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea
| | - Hong-Kyung Kim
- Department of Brain and Cognitive Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea
| | - Young-Eun Lee
- Department of Brain and Cognitive Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea
| | - John Williamson
- Department of Brain and Cognitive Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea
| | - Siamac Fazli
- Department of Computer Science, Nazarbayev University, Qabanbay Batyr Ave 53, Astana 010000, Kazakhstan
| | - Seong-Whan Lee
- Department of Brain and Cognitive Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea
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5
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Bockbrader MA, Francisco G, Lee R, Olson J, Solinsky R, Boninger ML. Brain Computer Interfaces in Rehabilitation Medicine. PM R 2019; 10:S233-S243. [PMID: 30269808 DOI: 10.1016/j.pmrj.2018.05.028] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 05/22/2018] [Accepted: 05/31/2018] [Indexed: 12/24/2022]
Abstract
One innovation currently influencing physical medicine and rehabilitation is brain-computer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user's intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user's interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. This article discusses the basic components of BCI for rehabilitation, including recording systems and locations, signal processing and translation algorithms, and external devices controlled through BCI commands. An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.
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Affiliation(s)
- Marcia A Bockbrader
- Department of Physical Medicine & Rehabilitation, The Ohio State University, 480 Medical Center Dr, Columbus, OH 43210; and Neurological Institute, Ohio State University Wexner Medical Center, Columbus, OH(∗).
| | - Gerard Francisco
- Department of Physical Medicine & Rehabilitation, The University of Texas, Houston, TX(†)
| | - Ray Lee
- Department of Orthopaedic and Rehabilitation, Schwab Rehabilitation Hospital, University of Chicago, Chicago, IL(‡)
| | - Jared Olson
- Department of Physical Medicine and Rehabilitation, University of Colorado, Aurora, CO(§)
| | - Ryan Solinsky
- Spaulding Rehabilitation Hospital, Boston; and Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA(¶)
| | - Michael L Boninger
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh; and VA Pittsburgh Health Care System, Pittsburgh, PA(#)
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6
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Skomrock ND, Schwemmer MA, Ting JE, Trivedi HR, Sharma G, Bockbrader MA, Friedenberg DA. A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent. Front Neurosci 2018; 12:763. [PMID: 30459542 PMCID: PMC6232881 DOI: 10.3389/fnins.2018.00763] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 10/03/2018] [Indexed: 12/18/2022] Open
Abstract
Laboratory demonstrations of brain-computer interface (BCI) systems show promise for reducing disability associated with paralysis by directly linking neural activity to the control of assistive devices. Surveys of potential users have revealed several key BCI performance criteria for clinical translation of such a system. Of these criteria, high accuracy, short response latencies, and multi-functionality are three key characteristics directly impacted by the neural decoding component of the BCI system, the algorithm that translates neural activity into control signals. Building a decoder that simultaneously addresses these three criteria is complicated because optimizing for one criterion may lead to undesirable changes in the other criteria. Unfortunately, there has been little work to date to quantify how decoder design simultaneously affects these performance characteristics. Here, we systematically explore the trade-off between accuracy, response latency, and multi-functionality for discrete movement classification using two different decoding strategies-a support vector machine (SVM) classifier which represents the current state-of-the-art for discrete movement classification in laboratory demonstrations and a proposed deep neural network (DNN) framework. We utilized historical intracortical recordings from a human tetraplegic study participant, who imagined performing several different hand and finger movements. For both decoders, we found that response time increases (i.e., slower reaction) and accuracy decreases as the number of functions increases. However, we also found that both the increase of response times and the decline in accuracy with additional functions is less for the DNN than the SVM. We also show that data preprocessing steps can affect the performance characteristics of the two decoders in drastically different ways. Finally, we evaluated the performance of our tetraplegic participant using the DNN decoder in real-time to control functional electrical stimulation (FES) of his paralyzed forearm. We compared his performance to that of able-bodied participants performing the same task, establishing a quantitative target for ideal BCI-FES performance on this task. Cumulatively, these results help quantify BCI decoder performance characteristics relevant to potential users and the complex interactions between them.
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Affiliation(s)
- Nicholas D. Skomrock
- Advanced Analytics and Health Research, Battelle Memorial Institute, Columbus, OH, United States
| | - Michael A. Schwemmer
- Advanced Analytics and Health Research, Battelle Memorial Institute, Columbus, OH, United States
| | - Jordyn E. Ting
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States
| | - Hemang R. Trivedi
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States
| | - Gaurav Sharma
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States
| | - Marcia A. Bockbrader
- Neurological Institute, The Ohio State University, Columbus, OH, United States
- Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, OH, United States
| | - David A. Friedenberg
- Advanced Analytics and Health Research, Battelle Memorial Institute, Columbus, OH, United States
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7
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Schwemmer MA, Skomrock ND, Sederberg PB, Ting JE, Sharma G, Bockbrader MA, Friedenberg DA. Meeting brain-computer interface user performance expectations using a deep neural network decoding framework. Nat Med 2018; 24:1669-1676. [PMID: 30250141 DOI: 10.1038/s41591-018-0171-y] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Accepted: 07/31/2018] [Indexed: 12/12/2022]
Abstract
Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices1-9. Surveys of potential end-users have identified key BCI system features10-14, including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI's neural decoding algorithm1,15, which is trained to associate neural activation patterns with intended user actions. Here, we introduce a new deep neural network16 decoding framework for BCI systems enabling discrete movements that addresses these four key performance characteristics. Using intracortical data from a participant with tetraplegia, we provide offline results demonstrating that our decoder is highly accurate, sustains this performance beyond a year without explicit daily retraining by combining it with an unsupervised updating procedure3,17-20, responds faster than competing methods8, and can increase functionality with minimal retraining by using a technique known as transfer learning21. We then show that our participant can use the decoder in real-time to reanimate his paralyzed forearm with functional electrical stimulation (FES), enabling accurate manipulation of three objects from the grasp and release test (GRT)22. These results demonstrate that deep neural network decoders can advance the clinical translation of BCI technology.
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Affiliation(s)
| | | | - Per B Sederberg
- Department of Psychology, University of Virginia, Charlottesville, VA, USA
| | - Jordyn E Ting
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, USA
| | - Gaurav Sharma
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, USA
| | - Marcia A Bockbrader
- Neurological Institute, Ohio State University, Columbus, OH, USA.,Department of Physical Medicine and Rehabilitation, Ohio State University, Columbus, OH, USA
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8
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Zhang M, Schwemmer MA, Ting JE, Majstorovic CE, Friedenberg DA, Bockbrader MA, Jerry Mysiw W, Rezai AR, Annetta NV, Bouton CE, Bresler HS, Sharma G. Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications. Bioelectron Med 2018; 4:11. [PMID: 32232087 PMCID: PMC7098253 DOI: 10.1186/s42234-018-0011-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 07/17/2018] [Indexed: 12/15/2022] Open
Abstract
Background Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain. Methods In this study, we show the applicability of wavelet decomposition method to extract and demonstrate the utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor cortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean wavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP (lf-MWP, 0–234 Hz), mid-frequency MWP (mf-MWP, 234 Hz–3.75 kHz) and high-frequency MWP (hf-MWP, >3.75 kHz). We analyzed these features using data collected from two experiments that were repeated over the course of about 3 years and compared their signal stability and decoding performance with the more standard threshold crossings, local field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings. Results All neural features could stably track neural information for over 3 years post-implantation and were less prone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector machine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively, in classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings. Conclusions Our results suggest that using MWP features in the appropriate frequency bands can provide an effective neural feature for brain computer interface intended for chronic applications. Trial registration This study was approved by the U.S. Food and Drug Administration (Investigational Device Exemption) and the Ohio State University Medical Center Institutional Review Board (Columbus, Ohio). The study conformed to institutional requirements for the conduct of human subjects and was filed on ClinicalTrials.gov (Identifier NCT01997125). Electronic supplementary material The online version of this article (10.1186/s42234-018-0011-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mingming Zhang
- 1Battelle Memorial Institute, 505 King Ave, Columbus, OH 43021 USA
| | | | - Jordyn E Ting
- 1Battelle Memorial Institute, 505 King Ave, Columbus, OH 43021 USA
| | | | | | - Marcia A Bockbrader
- 2Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, OH 43210 USA
| | - W Jerry Mysiw
- 2Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, OH 43210 USA
| | - Ali R Rezai
- 3West Virginia University School of Medicine, 1 Medical Center Dr, Morgantown, WV 26506 USA
| | | | - Chad E Bouton
- 4Feinstein Institute for Medical Research, Manhasset, NY 11030 USA
| | | | - Gaurav Sharma
- 1Battelle Memorial Institute, 505 King Ave, Columbus, OH 43021 USA
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9
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Friedenberg DA, Schwemmer MA, Landgraf AJ, Annetta NV, Bockbrader MA, Bouton CE, Zhang M, Rezai AR, Mysiw WJ, Bresler HS, Sharma G. Neuroprosthetic-enabled control of graded arm muscle contraction in a paralyzed human. Sci Rep 2017; 7:8386. [PMID: 28827605 PMCID: PMC5567199 DOI: 10.1038/s41598-017-08120-9] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 07/07/2017] [Indexed: 11/12/2022] Open
Abstract
Neuroprosthetics that combine a brain computer interface (BCI) with functional electrical stimulation (FES) can restore voluntary control of a patients’ own paralyzed limbs. To date, human studies have demonstrated an “all-or-none” type of control for a fixed number of pre-determined states, like hand-open and hand-closed. To be practical for everyday use, a BCI-FES system should enable smooth control of limb movements through a continuum of states and generate situationally appropriate, graded muscle contractions. Crucially, this functionality will allow users of BCI-FES neuroprosthetics to manipulate objects of different sizes and weights without dropping or crushing them. In this study, we present the first evidence that using a BCI-FES system, a human with tetraplegia can regain volitional, graded control of muscle contraction in his paralyzed limb. In addition, we show the critical ability of the system to generalize beyond training states and accurately generate wrist flexion states that are intermediate to training levels. These innovations provide the groundwork for enabling enhanced and more natural fine motor control of paralyzed limbs by BCI-FES neuroprosthetics.
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Affiliation(s)
- David A Friedenberg
- Advanced Analytics and Health Research, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA.
| | - Michael A Schwemmer
- Advanced Analytics and Health Research, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA
| | - Andrew J Landgraf
- Advanced Analytics and Health Research, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA
| | - Nicholas V Annetta
- Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA
| | - Marcia A Bockbrader
- Center for Neuromodulation, The Ohio State University, Columbus, Ohio, 43210, USA.,Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, Ohio, 43210, USA
| | - Chad E Bouton
- Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA.,Feinstein Institute for Medical Research, 350 Community Drive, Manhasset, New York, 11030, USA
| | - Mingming Zhang
- Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA
| | - Ali R Rezai
- Center for Neuromodulation, The Ohio State University, Columbus, Ohio, 43210, USA.,Department of Neurological Surgery, The Ohio State University, Columbus, Ohio, 43210, USA
| | - W Jerry Mysiw
- Center for Neuromodulation, The Ohio State University, Columbus, Ohio, 43210, USA.,Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, Ohio, 43210, USA
| | - Herbert S Bresler
- Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA
| | - Gaurav Sharma
- Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA
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10
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Marjanovic N, Kerr K, Aranda R, Hickey R, Esmailbeigi H. Wearable wireless User Interface Cursor-Controller (UIC-C). ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:3852-3855. [PMID: 29060738 DOI: 10.1109/embc.2017.8037697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Controlling a computer or a smartphone's cursor allows the user to access a world full of information. For millions of people with limited upper extremities motor function, controlling the cursor becomes profoundly difficult. Our team has developed the User Interface Cursor-Controller (UIC-C) to assist the impaired individuals in regaining control over the cursor. The UIC-C is a hands-free device that utilizes the tongue muscle to control the cursor movements. The entire device is housed inside a subject specific retainer. The user maneuvers the cursor by manipulating a joystick imbedded inside the retainer via their tongue. The joystick movement commands are sent to an electronic device via a Bluetooth connection. The device is readily recognizable as a cursor controller by any Bluetooth enabled electronic device. The device testing results have shown that the time it takes the user to control the cursor accurately via the UIC-C is about three times longer than a standard computer mouse controlled via the hand. The device does not require any permanent modifications to the body; therefore, it could be used during the period of acute rehabilitation of the hands. With the development of modern smart homes, and enhancement electronics controlled by the computer, UIC-C could be integrated into a system that enables individuals with permanent impairment, the ability to control the cursor. In conclusion, the UIC-C device is designed with the goal of allowing the user to accurately control a cursor during the periods of either acute or permanent upper extremities impairment.
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