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Herring EZ, Graczyk EL, Memberg WD, Adams R, Fernandez Baca-Vaca G, Hutchison BC, Krall JT, Alexander BJ, Conlan EC, Alfaro KE, Bhat P, Ketting-Olivier AB, Haddix CA, Taylor DM, Tyler DJ, Sweet JA, Kirsch RF, Ajiboye AB, Miller JP. Reconnecting the Hand and Arm to the Brain: Efficacy of Neural Interfaces for Sensorimotor Restoration After Tetraplegia. Neurosurgery 2024; 94:864-874. [PMID: 37982637 DOI: 10.1227/neu.0000000000002769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 09/01/2023] [Indexed: 11/21/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Paralysis after spinal cord injury involves damage to pathways that connect neurons in the brain to peripheral nerves in the limbs. Re-establishing this communication using neural interfaces has the potential to bridge the gap and restore upper extremity function to people with high tetraplegia. We report a novel approach for restoring upper extremity function using selective peripheral nerve stimulation controlled by intracortical microelectrode recordings from sensorimotor networks, along with restoration of tactile sensation of the hand using intracortical microstimulation. METHODS A 27-year-old right-handed man with AIS-B (motor-complete, sensory-incomplete) C3-C4 tetraplegia was enrolled into the clinical trial. Six 64-channel intracortical microelectrode arrays were implanted into left hemisphere regions involved in upper extremity function, including primary motor and sensory cortices, inferior frontal gyrus, and anterior intraparietal area. Nine 16-channel extraneural peripheral nerve electrodes were implanted to allow targeted stimulation of right median, ulnar (2), radial, axillary, musculocutaneous, suprascapular, lateral pectoral, and long thoracic nerves, to produce selective muscle contractions on demand. Proof-of-concept studies were performed to demonstrate feasibility of using a brain-machine interface to read from and write to the brain for restoring motor and sensory functions of the participant's own arm and hand. RESULTS Multiunit neural activity that correlated with intended motor action was successfully recorded from intracortical arrays. Microstimulation of electrodes in somatosensory cortex produced repeatable sensory percepts of individual fingers for restoration of touch sensation. Selective electrical activation of peripheral nerves produced antigravity muscle contractions, resulting in functional movements that the participant was able to command under brain control to perform virtual and actual arm and hand movements. The system was well tolerated with no operative complications. CONCLUSION The combination of implanted cortical electrodes and nerve cuff electrodes has the potential to create bidirectional restoration of motor and sensory functions of the arm and hand after neurological injury.
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Affiliation(s)
- Eric Z Herring
- School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Neurosurgery, The Neurological Institute, University Hospital Cleveland Medical Center, Cleveland , Ohio , USA
| | - Emily L Graczyk
- School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland , Ohio , USA
| | - William D Memberg
- School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland , Ohio , USA
| | - Robert Adams
- School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Neurosurgery, The Neurological Institute, University Hospital Cleveland Medical Center, Cleveland , Ohio , USA
| | - Gaudalupe Fernandez Baca-Vaca
- School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Neurosurgery, The Neurological Institute, University Hospital Cleveland Medical Center, Cleveland , Ohio , USA
| | - Brianna C Hutchison
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
| | - John T Krall
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
| | - Benjamin J Alexander
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
| | - Emily C Conlan
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
| | - Kenya E Alfaro
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
| | - Preethisiri Bhat
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
| | - Aaron B Ketting-Olivier
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
| | - Chase A Haddix
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Neuroscience, The Cleveland Clinic, Cleveland , Ohio , USA
| | - Dawn M Taylor
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland , Ohio , USA
- Department of Neuroscience, The Cleveland Clinic, Cleveland , Ohio , USA
| | - Dustin J Tyler
- School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland , Ohio , USA
| | - Jennifer A Sweet
- School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Neurosurgery, The Neurological Institute, University Hospital Cleveland Medical Center, Cleveland , Ohio , USA
| | - Robert F Kirsch
- School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland , Ohio , USA
| | - A Bolu Ajiboye
- School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland , Ohio , USA
| | - Jonathan P Miller
- School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Neurosurgery, The Neurological Institute, University Hospital Cleveland Medical Center, Cleveland , Ohio , USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland , Ohio , USA
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Herring EZ, Graczyk EL, Memberg WD, Adams RD, Baca-Vaca GF, Hutchison BC, Krall JT, Alexander BJ, Conlan EC, Alfaro KE, Bhat PR, Ketting-Olivier AB, Haddix CA, Taylor DM, Tyler DJ, Kirsch RF, Ajiboye AB, Miller JP. Reconnecting the Hand and Arm to the Brain: Efficacy of Neural Interfaces for Sensorimotor Restoration after Tetraplegia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.24.23288977. [PMID: 37162904 PMCID: PMC10168522 DOI: 10.1101/2023.04.24.23288977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Background Paralysis after spinal cord injury involves damage to pathways that connect neurons in the brain to peripheral nerves in the limbs. Re-establishing this communication using neural interfaces has the potential to bridge the gap and restore upper extremity function to people with high tetraplegia. Objective We report a novel approach for restoring upper extremity function using selective peripheral nerve stimulation controlled by intracortical microelectrode recordings from sensorimotor networks, along with restoration of tactile sensation of the hand using intracortical microstimulation. Methods A right-handed man with motor-complete C3-C4 tetraplegia was enrolled into the clinical trial. Six 64-channel intracortical microelectrode arrays were implanted into left hemisphere regions involved in upper extremity function, including primary motor and sensory cortices, inferior frontal gyrus, and anterior intraparietal area. Nine 16-channel extraneural peripheral nerve electrodes were implanted to allow targeted stimulation of right median, ulnar (2), radial, axillary, musculocutaneous, suprascapular, lateral pectoral, and long thoracic nerves, to produce selective muscle contractions on demand. Proof-of-concept studies were performed to demonstrate feasibility of a bidirectional brain-machine interface to restore function of the participant's own arm and hand. Results Multi-unit neural activity that correlated with intended motor action was successfully recorded from intracortical arrays. Microstimulation of electrodes in somatosensory cortex produced repeatable sensory percepts of individual fingers for restoration of touch sensation. Selective electrical activation of peripheral nerves produced antigravity muscle contractions. The system was well tolerated with no operative complications. Conclusion The combination of implanted cortical electrodes and nerve cuff electrodes has the potential to allow restoration of motor and sensory functions of the arm and hand after neurological injury.
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Affiliation(s)
- Eric Z Herring
- School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
- The Neurological Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Emily L Graczyk
- School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Cleveland, Ohio, USA
| | - William D Memberg
- School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Cleveland, Ohio, USA
| | - Robert D Adams
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Cleveland, Ohio, USA
| | | | - Brianna C Hutchison
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - John T Krall
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Benjamin J Alexander
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Emily C Conlan
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Kenya E Alfaro
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Preethi R Bhat
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | | | - Chase A Haddix
- Department of Neuroscience, Lerner Research Institute, The Cleveland Clinic, Cleveland, Ohio, USA
| | - Dawn M Taylor
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Cleveland, Ohio, USA
- Department of Neuroscience, Lerner Research Institute, The Cleveland Clinic, Cleveland, Ohio, USA
| | - Dustin J Tyler
- School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Robert F Kirsch
- School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Cleveland, Ohio, USA
| | - A Bolu Ajiboye
- School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Cleveland, Ohio, USA
| | - Jonathan P Miller
- School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
- The Neurological Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Cleveland, Ohio, USA
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Pandarinath C, Bensmaia SJ. The science and engineering behind sensitized brain-controlled bionic hands. Physiol Rev 2022; 102:551-604. [PMID: 34541898 PMCID: PMC8742729 DOI: 10.1152/physrev.00034.2020] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/07/2021] [Accepted: 09/13/2021] [Indexed: 12/13/2022] Open
Abstract
Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.
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Affiliation(s)
- Chethan Pandarinath
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia
- Department of Neurosurgery, Emory University, Atlanta, Georgia
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois
- Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, Illinois
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Feng Z, Sun Y, Qian L, Qi Y, Wang Y, Guan C, Sun Y. Design a novel BCI for neurorehabilitation using concurrent LFP and EEG features: a case study. IEEE Trans Biomed Eng 2021; 69:1554-1563. [PMID: 34582344 DOI: 10.1109/tbme.2021.3115799] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Brain-computer interfaces (BCI) that enables people with severe motor disabilities to use their brain signals for direct control of objects have attracted increased interest in rehabilitation. To date, no study has investigated feasibility of the BCI framework incorporating both intracortical and scalp signals. Methods: Concurrent local field potential (LFP) from the hand-knob area and scalp EEG were recorded in a paraplegic patient undergoing a spike-based close-loop neurorehabilitation training. Based upon multimodal spatio-spectral feature extraction and Naive Bayes classification, we developed, for the first time, a novel LFP-EEG-BCI for motor intention decoding. A transfer learning (TL) approach was employed to further improve the feasibility. The performance of the proposed LFP-EEG-BCI for four-class upper-limb motor intention decoding was assessed. Results: Using a decision fusion strategy, we showed that the LFP-EEG-BCI significantly (p <0.05) outperformed single modal BCI (LFP-BCI and EEG-BCI) in terms of decoding accuracy with the best performance achieved using regularized common spatial pattern features. Interrogation of feature characteristics revealed discriminative spatial and spectral patterns, which may lead to new insights for better understanding of brain dynamics during different motor imagery tasks and promote development of efficient decoding algorithms. Moreover, we showed that similar classification performance could be obtained with few training trials, therefore highlighting the efficacy of TL. Conclusion: The present findings demonstrated the superiority of the novel LFP-EEG-BCI in motor intention decoding. Significance: This work introduced a novel LFP-EEG-BCI that may lead to new directions for developing practical neurorehabilitation systems with high detection accuracy and multi-paradigm feasibility in clinical applications.
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Ahmadi N, Constandinou T, Bouganis CS. Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning. J Neural Eng 2021; 18. [PMID: 33477128 DOI: 10.1088/1741-2552/abde8a] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 01/21/2021] [Indexed: 12/18/2022]
Abstract
OBJECTIVE Brain-machine interfaces (BMIs) seek to restore lost motor functions in individuals with neurological disorders by enabling them to control external devices directly with their thoughts. This work aims to improve robustness and decoding accuracy that currently become major challenges in the clinical translation of intracortical BMIs. APPROACH We propose entire spiking activity (ESA) -an envelope of spiking activity that can be extracted by a simple, threshold-less, and automated technique- as the input signal. We couple ESA with deep learning-based decoding algorithm that uses quasi-recurrent neural network (QRNN) architecture. We evaluate comprehensively the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of three non-human primates performing different tasks. MAIN RESULTS Our proposed method yields consistently higher decoding performance than any other combinations of the input signal and decoding algorithm previously reported across long term recording sessions. It can sustain high decoding performance even when removing spikes from the raw signals, when using the different number of channels, and when using a smaller amount of training data. SIGNIFICANCE Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.
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Affiliation(s)
- Nur Ahmadi
- Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, London, SW7 2BT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Timothy Constandinou
- Electrical & Electronic Engineering, Imperial College London, South Kensington Campus, London, London, SW7 2BT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Christos-Savvas Bouganis
- Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, London, SW7 2BT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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Bullard AJ, Hutchison BC, Lee J, Chestek CA, Patil PG. Estimating Risk for Future Intracranial, Fully Implanted, Modular Neuroprosthetic Systems: A Systematic Review of Hardware Complications in Clinical Deep Brain Stimulation and Experimental Human Intracortical Arrays. Neuromodulation 2019; 23:411-426. [DOI: 10.1111/ner.13069] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 08/05/2019] [Accepted: 09/10/2019] [Indexed: 01/08/2023]
Affiliation(s)
- Autumn J. Bullard
- Department of Biomedical Engineering University of Michigan Ann Arbor MI USA
| | | | - Jiseon Lee
- Department of Biomedical Engineering University of Michigan Ann Arbor MI USA
| | - Cynthia A. Chestek
- Department of Biomedical Engineering University of Michigan Ann Arbor MI USA
- Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA
| | - Parag G. Patil
- Department of Biomedical Engineering University of Michigan Ann Arbor MI USA
- Department of Neurosurgery University of Michigan Medical School Ann Arbor MI USA
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Branco MP, de Boer LM, Ramsey NF, Vansteensel MJ. Encoding of kinetic and kinematic movement parameters in the sensorimotor cortex: A Brain-Computer Interface perspective. Eur J Neurosci 2019; 50:2755-2772. [PMID: 30633413 PMCID: PMC6625947 DOI: 10.1111/ejn.14342] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 11/30/2018] [Accepted: 01/07/2019] [Indexed: 01/23/2023]
Abstract
For severely paralyzed people, Brain-Computer Interfaces (BCIs) can potentially replace lost motor output and provide a brain-based control signal for augmentative and alternative communication devices or neuroprosthetics. Many BCIs focus on neuronal signals acquired from the hand area of the sensorimotor cortex, employing changes in the patterns of neuronal firing or spectral power associated with one or more types of hand movement. Hand and finger movement can be described by two groups of movement features, namely kinematics (spatial and motion aspects) and kinetics (muscles and forces). Despite extensive primate and human research, it is not fully understood how these features are represented in the SMC and how they lead to the appropriate movement. Yet, the available information may provide insight into which features are most suitable for BCI control. To that purpose, the current paper provides an in-depth review on the movement features encoded in the SMC. Even though there is no consensus on how exactly the SMC generates movement, we conclude that some parameters are well represented in the SMC and can be accurately used for BCI control with discrete as well as continuous feedback. However, the vast evidence also suggests that movement should be interpreted as a combination of multiple parameters rather than isolated ones, pleading for further exploration of sensorimotor control models for accurate BCI control.
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Affiliation(s)
- Mariana P. Branco
- Brain Center Rudolf MagnusDepartment of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
| | | | - Nick F. Ramsey
- Brain Center Rudolf MagnusDepartment of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Mariska J. Vansteensel
- Brain Center Rudolf MagnusDepartment of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
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Vaidya M, Flint RD, Wang PT, Barry A, Li Y, Ghassemi M, Tomic G, Yao J, Carmona C, Mugler EM, Gallick S, Driver S, Brkic N, Ripley D, Liu C, Kamper D, Do AH, Slutzky MW. Hemicraniectomy in Traumatic Brain Injury: A Noninvasive Platform to Investigate High Gamma Activity for Brain Machine Interfaces. IEEE Trans Neural Syst Rehabil Eng 2019; 27:1467-1472. [PMID: 31021800 DOI: 10.1109/tnsre.2019.2912298] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Brain-machine interfaces (BMIs) translate brain signals into control signals for an external device, such as a computer cursor or robotic limb. These signals can be obtained either noninvasively or invasively. Invasive recordings, using electrocorticography (ECoG) or intracortical microelectrodes, provide higher bandwidth and more informative signals. Rehabilitative BMIs, which aim to drive plasticity in the brain to enhance recovery after brain injury, have almost exclusively used non-invasive recordings, such electroencephalography (EEG) or magnetoencephalography (MEG), which have limited bandwidth and information content. Invasive recordings provide more information and spatiotemporal resolution, but do incur risk, and thus are not usually investigated in people with stroke or traumatic brain injury (TBI). Here, in this paper, we describe a new BMI paradigm to investigate the use of higher frequency signals in brain-injured subjects without incurring significant risk. We recorded EEG in TBI subjects who required hemicraniectomies (removal of a part of the skull). EEG over the hemicraniectomy (hEEG) contained substantial information in the high gamma frequency range (65-115 Hz). Using this information, we decoded continuous finger flexion force with moderate to high accuracy (variance accounted for 0.06 to 0.52), which at best approaches that using epidural signals. These results indicate that people with hemicraniectomies can provide a useful resource for developing BMI therapies for the treatment of brain injury.
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Milekovic T, Bacher D, Sarma AA, Simeral JD, Saab J, Pandarinath C, Yvert B, Sorice BL, Blabe C, Oakley EM, Tringale KR, Eskandar E, Cash SS, Shenoy KV, Henderson JM, Hochberg LR, Donoghue JP. Volitional control of single-electrode high gamma local field potentials by people with paralysis. J Neurophysiol 2019; 121:1428-1450. [PMID: 30785814 DOI: 10.1152/jn.00131.2018] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Intracortical brain-computer interfaces (BCIs) can enable individuals to control effectors, such as a computer cursor, by directly decoding the user's movement intentions from action potentials and local field potentials (LFPs) recorded within the motor cortex. However, the accuracy and complexity of effector control achieved with such "biomimetic" BCIs will depend on the degree to which the intended movements used to elicit control modulate the neural activity. In particular, channels that do not record distinguishable action potentials and only record LFP modulations may be of limited use for BCI control. In contrast, a biofeedback approach may surpass these limitations by letting the participants generate new control signals and learn strategies that improve the volitional control of signals used for effector control. Here, we show that, by using a biofeedback paradigm, three individuals with tetraplegia achieved volitional control of gamma LFPs (40-400 Hz) recorded by a single microelectrode implanted in the precentral gyrus. Control was improved over a pair of consecutive sessions up to 3 days apart. In all but one session, the channel used to achieve control lacked distinguishable action potentials. Our results indicate that biofeedback LFP-based BCIs may potentially contribute to the neural modulation necessary to obtain reliable and useful control of effectors. NEW & NOTEWORTHY Our study demonstrates that people with tetraplegia can volitionally control individual high-gamma local-field potential (LFP) channels recorded from the motor cortex, and that this control can be improved using biofeedback. Motor cortical LFP signals are thought to be both informative and stable intracortical signals and, thus, of importance for future brain-computer interfaces.
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Affiliation(s)
- Tomislav Milekovic
- Department of Neuroscience, Brown University , Providence, Rhode Island.,Carney Institute for Brain Science, Brown University , Providence, Rhode Island.,Department of Fundamental Neuroscience, Faculty of Medicine, University of Geneva , Geneva , Switzerland
| | - Daniel Bacher
- Carney Institute for Brain Science, Brown University , Providence, Rhode Island.,School of Engineering, Brown University , Providence, Rhode Island
| | - Anish A Sarma
- Carney Institute for Brain Science, Brown University , Providence, Rhode Island.,School of Engineering, Brown University , Providence, Rhode Island.,Center for Neurorestoration and Neurotechnology, Rehabilitation Research & Development Service, Department of Veterans Affairs , Providence, Rhode Island
| | - John D Simeral
- Carney Institute for Brain Science, Brown University , Providence, Rhode Island.,School of Engineering, Brown University , Providence, Rhode Island.,Center for Neurorestoration and Neurotechnology, Rehabilitation Research & Development Service, Department of Veterans Affairs , Providence, Rhode Island
| | - Jad Saab
- Carney Institute for Brain Science, Brown University , Providence, Rhode Island.,School of Engineering, Brown University , Providence, Rhode Island
| | - Chethan Pandarinath
- Department of Neurosurgery, Stanford University , Stanford, California.,Department of Electrical Engineering, Stanford University , Stanford, California.,Stanford Neurosciences Institute, Stanford University , Stanford, California
| | - Blaise Yvert
- Department of Neuroscience, Brown University , Providence, Rhode Island.,Carney Institute for Brain Science, Brown University , Providence, Rhode Island.,Inserm, University of Grenoble, Clinatec-Lab U1205, Grenoble , France
| | - Brittany L Sorice
- Department of Neurology, Massachusetts General Hospital , Boston, Massachusetts
| | - Christine Blabe
- Department of Neurosurgery, Stanford University , Stanford, California
| | - Erin M Oakley
- Department of Neurology, Massachusetts General Hospital , Boston, Massachusetts
| | - Kathryn R Tringale
- Department of Neurology, Massachusetts General Hospital , Boston, Massachusetts
| | - Emad Eskandar
- Department of Neurosurgery, Massachusetts General Hospital , Boston, Massachusetts.,Harvard Medical School , Boston, Massachusetts
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital , Boston, Massachusetts.,Harvard Medical School , Boston, Massachusetts
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University , Stanford, California.,Stanford Neurosciences Institute, Stanford University , Stanford, California.,Neurosciences Program, Stanford University , Stanford, California.,Department of Neurobiology, Stanford University , Stanford, California.,Department of Bioengineering, Stanford University , Stanford, California
| | - Jaimie M Henderson
- Department of Neurosurgery, Stanford University , Stanford, California.,Stanford Neurosciences Institute, Stanford University , Stanford, California.,Department of Neurology and Neurological Sciences, Stanford University , Stanford, California
| | - Leigh R Hochberg
- Carney Institute for Brain Science, Brown University , Providence, Rhode Island.,School of Engineering, Brown University , Providence, Rhode Island.,Center for Neurorestoration and Neurotechnology, Rehabilitation Research & Development Service, Department of Veterans Affairs , Providence, Rhode Island.,Department of Neurology, Massachusetts General Hospital , Boston, Massachusetts.,Harvard Medical School , Boston, Massachusetts
| | - John P Donoghue
- Department of Neuroscience, Brown University , Providence, Rhode Island.,Carney Institute for Brain Science, Brown University , Providence, Rhode Island.,Center for Neurorestoration and Neurotechnology, Rehabilitation Research & Development Service, Department of Veterans Affairs , Providence, Rhode Island
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10
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Bullard AJ, Nason SR, Irwin ZT, Nu CS, Smith B, Campean A, Peckham PH, Kilgore KL, Willsey MS, Patil PG, Chestek CA. Design and testing of a 96-channel neural interface module for the Networked Neuroprosthesis system. Bioelectron Med 2019; 5:3. [PMID: 32232094 PMCID: PMC7098219 DOI: 10.1186/s42234-019-0019-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 01/25/2019] [Indexed: 11/20/2022] Open
Abstract
Background The loss of motor functions resulting from spinal cord injury can have devastating implications on the quality of one’s life. Functional electrical stimulation has been used to help restore mobility, however, current functional electrical stimulation (FES) systems require residual movements to control stimulation patterns, which may be unintuitive and not useful for individuals with higher level cervical injuries. Brain machine interfaces (BMI) offer a promising approach for controlling such systems; however, they currently still require transcutaneous leads connecting indwelling electrodes to external recording devices. While several wireless BMI systems have been designed, high signal bandwidth requirements limit clinical translation. Case Western Reserve University has developed an implantable, modular FES system, the Networked Neuroprosthesis (NNP), to perform combinations of myoelectric recording and neural stimulation for controlling motor functions. However, currently the existing module capabilities are not sufficient for intracortical recordings. Methods Here we designed and tested a 1 × 4 cm, 96-channel neural recording module prototype to fit within the specifications to mate with the NNP. The neural recording module extracts power between 0.3–1 kHz, instead of transmitting the raw, high bandwidth neural data to decrease power requirements. Results The module consumed 33.6 mW while sampling 96 channels at approximately 2 kSps. We also investigated the relationship between average spiking band power and neural spike rate, which produced a maximum correlation of R = 0.8656 (Monkey N) and R = 0.8023 (Monkey W). Conclusion Our experimental results show that we can record and transmit 96 channels at 2ksps within the power restrictions of the NNP system and successfully communicate over the NNP network. We believe this device can be used as an extension to the NNP to produce a clinically viable, fully implantable, intracortically-controlled FES system and advance the field of bioelectronic medicine.
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Affiliation(s)
- Autumn J Bullard
- 1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI USA
| | - Samuel R Nason
- 1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI USA
| | - Zachary T Irwin
- 1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI USA
| | - Chrono S Nu
- 1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI USA
| | - Brian Smith
- 2Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH USA
| | - Alex Campean
- 2Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH USA
| | - P Hunter Peckham
- 2Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH USA.,3Department of Orthopaedics, MetroHealth Medical Center, Cleveland, OH USA
| | - Kevin L Kilgore
- 2Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH USA.,3Department of Orthopaedics, MetroHealth Medical Center, Cleveland, OH USA.,4Research Service, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, OH USA
| | - Matthew S Willsey
- 5Department of Neurosurgery, University of Michigan, Ann Arbor, MI USA
| | - Parag G Patil
- 1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI USA.,5Department of Neurosurgery, University of Michigan, Ann Arbor, MI USA.,6Department of Neurology, University of Michigan, Ann Arbor, MI USA.,7Department of Anesthesiology, University of Michigan, Ann Arbor, MI USA
| | - Cynthia A Chestek
- 1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI USA.,8Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI USA
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11
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Young D, Willett F, Memberg WD, Murphy B, Rezaii P, Walter B, Sweet J, Miller J, Shenoy KV, Hochberg LR, Kirsch RF, Ajiboye AB. Closed-loop cortical control of virtual reach and posture using Cartesian and joint velocity commands. J Neural Eng 2018; 16:026011. [PMID: 30523839 DOI: 10.1088/1741-2552/aaf606] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Brain-computer interfaces (BCIs) are a promising technology for the restoration of function to people with paralysis, especially for controlling coordinated reaching. Typical BCI studies decode Cartesian endpoint velocities as commands, but human arm movements might be better controlled in a joint-based coordinate frame, which may match underlying movement encoding in the motor cortex. A better understanding of BCI controlled reaching by people with paralysis may lead to performance improvements in brain-controlled assistive devices. APPROACH Two intracortical BCI participants in the BrainGate2 pilot clinical trial performed a visual 3D endpoint virtual reality reaching task using two decoders: Cartesian and joint velocity. Task performance metrics (i.e. success rate and path efficiency) and single feature and population tuning were compared across the two decoder conditions. The participants also demonstrated the first BCI control of a fourth dimension of reaching, the arm's swivel angle, in a 4D posture matching task. MAIN RESULTS Both users achieved significantly higher success rates using Cartesian velocity control, and joint controlled trajectories were more variable and significantly more curved. Neural tuning analyses showed that most single feature activity was best described by a Cartesian kinematic encoding model, and population analyses revealed only slight differences in aggregate activity between the decoder conditions. Simulations of a BCI user reproduced trajectory features seen during closed-loop joint control when assuming only Cartesian-tuned features passed through a joint decoder. With minimal training, both participants controlled the virtual arm's swivel angle to complete a 4D posture matching task, and achieved significantly higher success using a Cartesian + swivel velocity decoder compared to a joint velocity decoder. SIGNIFICANCE These results suggest that Cartesian velocity command interfaces may provide better BCI control of arm movements than other kinematic variables, even in 4D posture tasks with swivel angle targets.
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Affiliation(s)
- D Young
- Case Western Reserve University, Cleveland, OH, United States of America. Department of VA Medical Center, FES Center of Excellence, Rehabilitation R&D Service, Louis Stokes Cleveland, Cleveland, OH, United States of America
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12
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Schaeffer MC, Aksenova T. Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review. Front Neurosci 2018; 12:540. [PMID: 30158847 PMCID: PMC6104172 DOI: 10.3389/fnins.2018.00540] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 07/17/2018] [Indexed: 11/13/2022] Open
Abstract
Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses. In particular, motor BCI systems benefit patients if the decoded actions reflect the users' intentions with an accuracy that enables them to efficiently interact with their environment. One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy. This paper will review the literature of data-driven and user-specific transducer design and identification approaches and it focuses on internally-paced motor BCIs. In particular, continuous kinematic biomimetic and mental-task decoders are reviewed. Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered. The current progress and challenges related to the design of clinical-compatible motor BCI transducers are additionally discussed.
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Affiliation(s)
| | - Tetiana Aksenova
- CEA, LETI, CLINATEC, MINATEC Campus, Université Grenoble Alpes, Grenoble, France
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13
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Caldwell R, Mandal H, Sharma R, Solzbacher F, Tathireddy P, Rieth L. Analysis of Al 2O 3-parylene C bilayer coatings and impact of microelectrode topography on long term stability of implantable neural arrays. J Neural Eng 2018; 14:046011. [PMID: 28351998 DOI: 10.1088/1741-2552/aa69d3] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Performance of many dielectric coatings for neural electrodes degrades over time, contributing to loss of neural signals and evoked percepts. Studies using planar test substrates have found that a novel bilayer coating of atomic-layer deposited (ALD) Al2O3 and parylene C is a promising candidate for neural electrode applications, exhibiting superior stability to parylene C alone. However, initial results from bilayer encapsulation testing on non-planar devices have been less positive. Our aim was to evaluate ALD Al2O3-parylene C coatings using novel test paradigms, to rigorously evaluate dielectric coatings for neural electrode applications by incorporating neural electrode topography into test structure design. APPROACH Five test devices incorporated three distinct topographical features common to neural electrodes, derived from the utah electrode array (UEA). Devices with bilayer (52 nm Al2O3 + 6 µm parylene C) were evaluated against parylene C controls (N ⩾ 6 per device type). Devices were aged in phosphate buffered saline at 67 °C for up to 311 d, and monitored through: (1) leakage current to evaluate encapsulation lifetimes (>1 nA during 5VDC bias indicated failure), and (2) wideband (1-105 Hz) impedance. MAIN RESULTS Mean-times-to-failure (MTTFs) ranged from 12 to 506 d for bilayer-coated devices, versus 10 to >2310 d for controls. Statistical testing (log-rank test, α = 0.05) of failure rates gave mixed results but favored the control condition. After failure, impedance loss for bilayer devices continued for months and manifested across the entire spectrum, whereas the effect was self-limiting after several days, and restricted to frequencies <100 Hz for controls. These results correlated well with observations of UEAs encapsulated with bilayer and control films. SIGNIFICANCE We observed encapsulation failure modes and behaviors comparable to neural electrode performance which were undetected in studies with planar test devices. We found the impact of parylene C defects to be exacerbated by ALD Al2O3, and conclude that inferior bilayer performance arises from degradation of ALD Al2O3 when directly exposed to saline. This is an important consideration, given that neural electrodes with bilayer coatings are expected to have ALD Al2O3 exposed at dielectric boundaries that delineate electrode sites. Process improvements and use of different inorganic coatings to decrease dissolution in physiological fluids may improve performance. Testing frameworks which take neural electrode complexities into account will be well suited to reliably evaluate such encapsulation schemes.
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Affiliation(s)
- Ryan Caldwell
- Department of Bioengineering, University of Utah, Salt Lake City, UT, United States of America
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14
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Milekovic T, Sarma AA, Bacher D, Simeral JD, Saab J, Pandarinath C, Sorice BL, Blabe C, Oakley EM, Tringale KR, Eskandar E, Cash SS, Henderson JM, Shenoy KV, Donoghue JP, Hochberg LR. Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals. J Neurophysiol 2018; 120:343-360. [PMID: 29694279 DOI: 10.1152/jn.00493.2017] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Restoring communication for people with locked-in syndrome remains a challenging clinical problem without a reliable solution. Recent studies have shown that people with paralysis can use brain-computer interfaces (BCIs) based on intracortical spiking activity to efficiently type messages. However, due to neuronal signal instability, most intracortical BCIs have required frequent calibration and continuous assistance of skilled engineers to maintain performance. Here, an individual with locked-in syndrome due to brain stem stroke and an individual with tetraplegia secondary to amyotrophic lateral sclerosis (ALS) used a simple communication BCI based on intracortical local field potentials (LFPs) for 76 and 138 days, respectively, without recalibration and without significant loss of performance. BCI spelling rates of 3.07 and 6.88 correct characters/minute allowed the participants to type messages and write emails. Our results indicate that people with locked-in syndrome could soon use a slow but reliable LFP-based BCI for everyday communication without ongoing intervention from a technician or caregiver. NEW & NOTEWORTHY This study demonstrates, for the first time, stable repeated use of an intracortical brain-computer interface by people with tetraplegia over up to four and a half months. The approach uses local field potentials (LFPs), signals that may be more stable than neuronal action potentials, to decode participants' commands. Throughout the several months of evaluation, the decoder remained unchanged; thus no technical interventions were required to maintain consistent brain-computer interface operation.
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Affiliation(s)
- Tomislav Milekovic
- Department of Neuroscience, Brown University , Providence, Rhode Island.,Carney Institute for Brain Science, Brown University , Providence, Rhode Island.,Department of Fundamental Neuroscience, Faculty of Medicine, University of Geneva , Geneva , Switzerland
| | - Anish A Sarma
- Carney Institute for Brain Science, Brown University , Providence, Rhode Island.,School of Engineering, Brown University , Providence, Rhode Island.,Center for Neurorestoration and Neurotechnology, Rehabilitation Research & Development, Department of Veterans Affairs , Providence, Rhode Island
| | - Daniel Bacher
- Carney Institute for Brain Science, Brown University , Providence, Rhode Island.,School of Engineering, Brown University , Providence, Rhode Island
| | - John D Simeral
- Carney Institute for Brain Science, Brown University , Providence, Rhode Island.,School of Engineering, Brown University , Providence, Rhode Island.,Center for Neurorestoration and Neurotechnology, Rehabilitation Research & Development, Department of Veterans Affairs , Providence, Rhode Island
| | - Jad Saab
- Carney Institute for Brain Science, Brown University , Providence, Rhode Island.,School of Engineering, Brown University , Providence, Rhode Island
| | - Chethan Pandarinath
- Department of Neurosurgery, Stanford University , Stanford, California.,Department of Electrical Engineering, Stanford University , Stanford, California.,Stanford Neurosciences Institute, Stanford University , Stanford, California
| | - Brittany L Sorice
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital , Boston, Massachusetts
| | - Christine Blabe
- Department of Neurosurgery, Stanford University , Stanford, California
| | - Erin M Oakley
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital , Boston, Massachusetts
| | - Kathryn R Tringale
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital , Boston, Massachusetts
| | - Emad Eskandar
- Department of Neurosurgery, Massachusetts General Hospital , Boston, Massachusetts.,Harvard Medical School , Boston, Massachusetts
| | - Sydney S Cash
- Harvard Medical School , Boston, Massachusetts.,Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital , Boston, Massachusetts
| | - Jaimie M Henderson
- Department of Neurosurgery, Stanford University , Stanford, California.,Department of Neurology and Neurological Sciences, Stanford University , Stanford, California.,Stanford Neurosciences Institute, Stanford University , Stanford, California
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University , Stanford, California.,Neurosciences Program, Stanford University , Stanford, California.,Department of Neurobiology, Stanford University , Stanford, California.,Department of Bioengineering, Stanford University , Stanford, California.,Stanford Neurosciences Institute, Stanford University , Stanford, California.,Howard Hughes Medical Institute at Stanford University , Stanford, California
| | - John P Donoghue
- Department of Neuroscience, Brown University , Providence, Rhode Island.,Carney Institute for Brain Science, Brown University , Providence, Rhode Island.,Center for Neurorestoration and Neurotechnology, Rehabilitation Research & Development, Department of Veterans Affairs , Providence, Rhode Island
| | - Leigh R Hochberg
- Carney Institute for Brain Science, Brown University , Providence, Rhode Island.,School of Engineering, Brown University , Providence, Rhode Island.,Center for Neurorestoration and Neurotechnology, Rehabilitation Research & Development, Department of Veterans Affairs , Providence, Rhode Island.,Harvard Medical School , Boston, Massachusetts.,Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital , Boston, Massachusetts
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15
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16
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Caldwell R, Sharma R, Takmakov P, Street MG, Solzbacher F, Tathireddy P, Rieth L. Neural electrode resilience against dielectric damage may be improved by use of highly doped silicon as a conductive material. J Neurosci Methods 2018; 293:210-225. [DOI: 10.1016/j.jneumeth.2017.10.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Revised: 10/01/2017] [Accepted: 10/02/2017] [Indexed: 11/28/2022]
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17
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Brandman DM, Cash SS, Hochberg LR. Review: Human Intracortical Recording and Neural Decoding for Brain-Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng 2017; 25:1687-1696. [PMID: 28278476 PMCID: PMC5815832 DOI: 10.1109/tnsre.2017.2677443] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Brain-computer interfaces (BCIs) use neural information recorded from the brain for the voluntary control of external devices. The development of BCI systems has largely focused on improving functional independence for individuals with severe motor impairments, including providing tools for communication and mobility. In this review, we describe recent advances in intracortical BCI technology and provide potential directions for further research.
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18
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Ajiboye AB, Willett FR, Young DR, Memberg WD, Murphy BA, Miller JP, Walter BL, Sweet JA, Hoyen HA, Keith MW, Peckham PH, Simeral JD, Donoghue JP, Hochberg LR, Kirsch RF. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. Lancet 2017; 389:1821-1830. [PMID: 28363483 PMCID: PMC5516547 DOI: 10.1016/s0140-6736(17)30601-3] [Citation(s) in RCA: 419] [Impact Index Per Article: 59.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Revised: 01/02/2017] [Accepted: 01/06/2017] [Indexed: 11/01/2022]
Abstract
BACKGROUND People with chronic tetraplegia, due to high-cervical spinal cord injury, can regain limb movements through coordinated electrical stimulation of peripheral muscles and nerves, known as functional electrical stimulation (FES). Users typically command FES systems through other preserved, but unrelated and limited in number, volitional movements (eg, facial muscle activity, head movements, shoulder shrugs). We report the findings of an individual with traumatic high-cervical spinal cord injury who coordinated reaching and grasping movements using his own paralysed arm and hand, reanimated through implanted FES, and commanded using his own cortical signals through an intracortical brain-computer interface (iBCI). METHODS We recruited a participant into the BrainGate2 clinical trial, an ongoing study that obtains safety information regarding an intracortical neural interface device, and investigates the feasibility of people with tetraplegia controlling assistive devices using their cortical signals. Surgical procedures were performed at University Hospitals Cleveland Medical Center (Cleveland, OH, USA). Study procedures and data analyses were performed at Case Western Reserve University (Cleveland, OH, USA) and the US Department of Veterans Affairs, Louis Stokes Cleveland Veterans Affairs Medical Center (Cleveland, OH, USA). The study participant was a 53-year-old man with a spinal cord injury (cervical level 4, American Spinal Injury Association Impairment Scale category A). He received two intracortical microelectrode arrays in the hand area of his motor cortex, and 4 months and 9 months later received a total of 36 implanted percutaneous electrodes in his right upper and lower arm to electrically stimulate his hand, elbow, and shoulder muscles. The participant used a motorised mobile arm support for gravitational assistance and to provide humeral abduction and adduction under cortical control. We assessed the participant's ability to cortically command his paralysed arm to perform simple single-joint arm and hand movements and functionally meaningful multi-joint movements. We compared iBCI control of his paralysed arm with that of a virtual three-dimensional arm. This study is registered with ClinicalTrials.gov, number NCT00912041. FINDINGS The intracortical implant occurred on Dec 1, 2014, and we are continuing to study the participant. The last session included in this report was Nov 7, 2016. The point-to-point target acquisition sessions began on Oct 8, 2015 (311 days after implant). The participant successfully cortically commanded single-joint and coordinated multi-joint arm movements for point-to-point target acquisitions (80-100% accuracy), using first a virtual arm and second his own arm animated by FES. Using his paralysed arm, the participant volitionally performed self-paced reaches to drink a mug of coffee (successfully completing 11 of 12 attempts within a single session 463 days after implant) and feed himself (717 days after implant). INTERPRETATION To our knowledge, this is the first report of a combined implanted FES+iBCI neuroprosthesis for restoring both reaching and grasping movements to people with chronic tetraplegia due to spinal cord injury, and represents a major advance, with a clear translational path, for clinically viable neuroprostheses for restoration of reaching and grasping after paralysis. FUNDING National Institutes of Health, Department of Veterans Affairs.
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Affiliation(s)
- A Bolu Ajiboye
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; School of Medicine, Case Western Reserve University, Cleveland, OH, USA; US Department of Veterans Affairs, Louis Stokes Cleveland Veterans Affairs Medical Center, Functional Electrical Stimulation Center of Excellence, Rehabilitation R&D Service, Cleveland, OH, USA.
| | - Francis R Willett
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; School of Medicine, Case Western Reserve University, Cleveland, OH, USA; US Department of Veterans Affairs, Louis Stokes Cleveland Veterans Affairs Medical Center, Functional Electrical Stimulation Center of Excellence, Rehabilitation R&D Service, Cleveland, OH, USA
| | - Daniel R Young
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; School of Medicine, Case Western Reserve University, Cleveland, OH, USA; US Department of Veterans Affairs, Louis Stokes Cleveland Veterans Affairs Medical Center, Functional Electrical Stimulation Center of Excellence, Rehabilitation R&D Service, Cleveland, OH, USA
| | - William D Memberg
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; School of Medicine, Case Western Reserve University, Cleveland, OH, USA; US Department of Veterans Affairs, Louis Stokes Cleveland Veterans Affairs Medical Center, Functional Electrical Stimulation Center of Excellence, Rehabilitation R&D Service, Cleveland, OH, USA
| | - Brian A Murphy
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; School of Medicine, Case Western Reserve University, Cleveland, OH, USA; US Department of Veterans Affairs, Louis Stokes Cleveland Veterans Affairs Medical Center, Functional Electrical Stimulation Center of Excellence, Rehabilitation R&D Service, Cleveland, OH, USA
| | - Jonathan P Miller
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA; US Department of Veterans Affairs, Louis Stokes Cleveland Veterans Affairs Medical Center, Functional Electrical Stimulation Center of Excellence, Rehabilitation R&D Service, Cleveland, OH, USA; Department of Neurological Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Benjamin L Walter
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA; US Department of Veterans Affairs, Louis Stokes Cleveland Veterans Affairs Medical Center, Functional Electrical Stimulation Center of Excellence, Rehabilitation R&D Service, Cleveland, OH, USA; Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Jennifer A Sweet
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA; US Department of Veterans Affairs, Louis Stokes Cleveland Veterans Affairs Medical Center, Functional Electrical Stimulation Center of Excellence, Rehabilitation R&D Service, Cleveland, OH, USA; Department of Neurological Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Harry A Hoyen
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA; Department of Orthopaedics, MetroHealth Medical Center, Cleveland, OH, USA
| | - Michael W Keith
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA; Department of Orthopaedics, MetroHealth Medical Center, Cleveland, OH, USA
| | - P Hunter Peckham
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; School of Medicine, Case Western Reserve University, Cleveland, OH, USA; US Department of Veterans Affairs, Louis Stokes Cleveland Veterans Affairs Medical Center, Functional Electrical Stimulation Center of Excellence, Rehabilitation R&D Service, Cleveland, OH, USA
| | - John D Simeral
- School of Engineering, Brown University, Providence, RI, USA; Brown Institute for Brain Science, Brown University, Providence, RI, USA; Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI, USA; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - John P Donoghue
- Brown Institute for Brain Science, Brown University, Providence, RI, USA; Department of Neuroscience, Brown University, Providence, RI, USA; Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI, USA; Wyss Center for Bio and Neuroengineering, Geneva, Switzerland
| | - Leigh R Hochberg
- School of Engineering, Brown University, Providence, RI, USA; Brown Institute for Brain Science, Brown University, Providence, RI, USA; Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI, USA; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Robert F Kirsch
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; School of Medicine, Case Western Reserve University, Cleveland, OH, USA; US Department of Veterans Affairs, Louis Stokes Cleveland Veterans Affairs Medical Center, Functional Electrical Stimulation Center of Excellence, Rehabilitation R&D Service, Cleveland, OH, USA; Department of Neurological Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
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19
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Huggins JE, Guger C, Ziat M, Zander TO, Taylor D, Tangermann M, Soria-Frisch A, Simeral J, Scherer R, Rupp R, Ruffini G, Robinson DKR, Ramsey NF, Nijholt A, Müller-Putz G, McFarland DJ, Mattia D, Lance BJ, Kindermans PJ, Iturrate I, Herff C, Gupta D, Do AH, Collinger JL, Chavarriaga R, Chase SM, Bleichner MG, Batista A, Anderson CW, Aarnoutse EJ. Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future. BRAIN-COMPUTER INTERFACES 2017; 4:3-36. [PMID: 29152523 PMCID: PMC5693371 DOI: 10.1080/2326263x.2016.1275488] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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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Affiliation(s)
- Jane E. Huggins
- Department of Physical Medicine and Rehabilitation, Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Christoph Guger
- G.Tec Medical Engineering GmbH, Guger Technologies OG, Schiedlberg, Austria
| | - Mounia Ziat
- Psychology Department, Northern Michigan University, Marquette, MI, USA
| | - Thorsten O. Zander
- Team PhyPA, Biological Psychology and Neuroergonomics, Technical University of Berlin, Berlin, Germany
| | | | - Michael Tangermann
- Cluster of Excellence BrainLinks-BrainTools, University of Freiburg, Germany
| | | | - John Simeral
- Ctr. For Neurorestoration and Neurotechnology, Rehab. R&D Service, Dept. of VA Medical Center, School of Engineering, Brown University, Providence, RI, USA
| | - Reinhold Scherer
- Institute of Neural Engineering, BCI- Lab, Graz University of Technology, Graz, Austria
| | - Rüdiger Rupp
- Section Experimental Neurorehabilitation, Spinal Cord Injury Center, University Hospital in Heidelberg, Heidelberg, Germany
| | - Giulio Ruffini
- Neuroscience Business Unit, Starlab Barcelona SLU, Barcelona, Spain
- Neuroelectrics Inc., Boston, USA
| | - Douglas K. R. Robinson
- Institute: Laboratoire Interdisciplinaire Sciences Innovations Sociétés (LISIS), Université Paris-Est Marne-la-Vallée, MARNE-LA-VALLÉE, France
| | - Nick F. Ramsey
- Dept Neurology & Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
| | - Anton Nijholt
- Faculty EEMCS, Enschede, University of Twente, The Netherlands & Imagineering Institute, Iskandar, Malaysia
| | - Gernot Müller-Putz
- Institute of Neural Engineering, BCI- Lab, Graz University of Technology, Graz, Austria
| | - Dennis J. McFarland
- New York State Department of Health, National Center for Adaptive Neurotechnologies, Wadsworth Center, Albany, New York USA
| | - Donatella Mattia
- Clinical Neurophysiology, Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, IRCCS, Rome, Italy
| | - Brent J. Lance
- Human Research and Engineering Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD USA
| | | | - Iñaki Iturrate
- Defitech Chair in Brain–machine Interface (CNBI), Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, EPFL-STI-CNBI, Campus Biotech H4, Geneva, Switzerland
| | - Christian Herff
- Cognitive Systems Lab, University of Bremen, Bremen, Germany
| | - Disha Gupta
- Brain Mind Research Inst, Weill Cornell Medical College, Early Brain Injury and Recovery Lab, Burke Medical Research Inst, White Plains, New York, USA
| | - An H. Do
- Department of Neurology, UC Irvine Brain Computer Interface Lab, University of California, Irvine, CA, USA
| | - Jennifer L. Collinger
- Department of Physical Medicine and Rehabilitation, Department of Veterans Affairs, VA Pittsburgh Healthcare System, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ricardo Chavarriaga
- Defitech Chair in Brain–machine Interface (CNBI), Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, EPFL-STI-CNBI, Campus Biotech H4, Geneva, Switzerland
| | - Steven M. Chase
- Center for the Neural Basis of Cognition and Department Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Martin G. Bleichner
- Neuropsychology Lab, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany
| | - Aaron Batista
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA USA
| | - Charles W. Anderson
- Department of Computer Science, Colorado State University, Fort Collins, CO USA
| | - Erik J. Aarnoutse
- Brain Center Rudolf Magnus, Dept Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
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Hammad SH, Kamavuako EN, Farina D, Jensen W. Simulation of a Real-Time Brain Computer Interface for Detecting a Self-Paced Hitting Task. Neuromodulation 2016; 19:804-811. [PMID: 27513737 DOI: 10.1111/ner.12478] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 05/18/2016] [Accepted: 06/01/2016] [Indexed: 11/29/2022]
Abstract
OBJECTIVES An invasive brain-computer interface (BCI) is a promising neurorehabilitation device for severely disabled patients. Although some systems have been shown to work well in restricted laboratory settings, their utility must be tested in less controlled, real-time environments. Our objective was to investigate whether a specific motor task could be reliably detected from multiunit intracortical signals from freely moving animals in a simulated, real-time setting. MATERIALS AND METHODS Intracortical signals were first obtained from electrodes placed in the primary motor cortex of four rats that were trained to hit a retractable paddle (defined as a "Hit"). In the simulated real-time setting, the signal-to-noise-ratio was first increased by wavelet denoising. Action potentials were detected, and features were extracted (spike count, mean absolute values, entropy, and combination of these features) within pre-defined time windows (200 ms, 300 ms, and 400 ms) to classify the occurrence of a "Hit." RESULTS We found higher detection accuracy of a "Hit" (73.1%, 73.4%, and 67.9% for the three window sizes, respectively) when the decision was made based on a combination of features rather than on a single feature. However, the duration of the window length was not statistically significant (p = 0.5). CONCLUSION Our results showed the feasibility of detecting a motor task in real time in a less restricted environment compared to environments commonly applied within invasive BCI research, and they showed the feasibility of using information extracted from multiunit recordings, thereby avoiding the time-consuming and complex task of extracting and sorting single units.
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Affiliation(s)
- Sofyan H Hammad
- Department of Health Science and Technology, Center for Sensory-Motor Interaction, Aalborg University, Aalborg, Denmark
| | - Ernest N Kamavuako
- Department of Health Science and Technology, Center for Sensory-Motor Interaction, Aalborg University, Aalborg, Denmark
| | - Dario Farina
- Department of Health Science and Technology, Center for Sensory-Motor Interaction, Aalborg University, Aalborg, Denmark.,Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Georg-August University, Göttingen, Germany
| | - Winnie Jensen
- Department of Health Science and Technology, Center for Sensory-Motor Interaction, Aalborg University, Aalborg, Denmark
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21
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Murphy MD, Guggenmos DJ, Bundy DT, Nudo RJ. Current Challenges Facing the Translation of Brain Computer Interfaces from Preclinical Trials to Use in Human Patients. Front Cell Neurosci 2016; 9:497. [PMID: 26778962 PMCID: PMC4702293 DOI: 10.3389/fncel.2015.00497] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2015] [Accepted: 12/10/2015] [Indexed: 12/13/2022] Open
Abstract
Current research in brain computer interface (BCI) technology is advancing beyond preclinical studies, with trials beginning in human patients. To date, these trials have been carried out with several different types of recording interfaces. The success of these devices has varied widely, but different factors such as the level of invasiveness, timescale of recorded information, and ability to maintain stable functionality of the device over a long period of time all must be considered in addition to accuracy in decoding intent when assessing the most practical type of device moving forward. Here, we discuss various approaches to BCIs, distinguishing between devices focusing on control of operations extrinsic to the subject (e.g., prosthetic limbs, computer cursors) and those focusing on control of operations intrinsic to the brain (e.g., using stimulation or external feedback), including closed-loop or adaptive devices. In this discussion, we consider the current challenges facing the translation of various types of BCI technology to eventual human application.
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Affiliation(s)
- Maxwell D Murphy
- Bioengineering Graduate Program, University of KansasLawrence, KS, USA; Department of Rehabilitation Medicine, University of Kansas Medical CenterKansas City, KS, USA
| | - David J Guggenmos
- Department of Rehabilitation Medicine, University of Kansas Medical Center Kansas City, KS, USA
| | - David T Bundy
- Department of Rehabilitation Medicine, University of Kansas Medical Center Kansas City, KS, USA
| | - Randolph J Nudo
- Department of Rehabilitation Medicine, University of Kansas Medical CenterKansas City, KS, USA; Landon Center on Aging, University of Kansas Medical CenterKansas City, KS, USA
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22
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Adewole DO, Serruya MD, Harris JP, Burrell JC, Petrov D, Chen HI, Wolf JA, Cullen DK. The Evolution of Neuroprosthetic Interfaces. Crit Rev Biomed Eng 2016; 44:123-52. [PMID: 27652455 PMCID: PMC5541680 DOI: 10.1615/critrevbiomedeng.2016017198] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The ideal neuroprosthetic interface permits high-quality neural recording and stimulation of the nervous system while reliably providing clinical benefits over chronic periods. Although current technologies have made notable strides in this direction, significant improvements must be made to better achieve these design goals and satisfy clinical needs. This article provides an overview of the state of neuroprosthetic interfaces, starting with the design and placement of these interfaces before exploring the stimulation and recording platforms yielded from contemporary research. Finally, we outline emerging research trends in an effort to explore the potential next generation of neuroprosthetic interfaces.
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Affiliation(s)
- Dayo O. Adewole
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Mijail D. Serruya
- Department of Neurology, Jefferson University, Philadelphia, PA, USA
| | - James P. Harris
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - Justin C. Burrell
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - Dmitriy Petrov
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - H. Isaac Chen
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - John A. Wolf
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - D. Kacy Cullen
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
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Mateo S, Di Rienzo F, Bergeron V, Guillot A, Collet C, Rode G. Motor imagery reinforces brain compensation of reach-to-grasp movement after cervical spinal cord injury. Front Behav Neurosci 2015; 9:234. [PMID: 26441568 PMCID: PMC4566051 DOI: 10.3389/fnbeh.2015.00234] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Accepted: 08/19/2015] [Indexed: 01/19/2023] Open
Abstract
Individuals with cervical spinal cord injury (SCI) that causes tetraplegia are challenged with dramatic sensorimotor deficits. However, certain rehabilitation techniques may significantly enhance their autonomy by restoring reach-to-grasp movements. Among others, evidence of motor imagery (MI) benefits for neurological rehabilitation of upper limb movements is growing. This literature review addresses MI effectiveness during reach-to-grasp rehabilitation after tetraplegia. Among articles from MEDLINE published between 1966 and 2015, we selected ten studies including 34 participants with C4 to C7 tetraplegia and 22 healthy controls published during the last 15 years. We found that MI of possible non-paralyzed movements improved reach-to-grasp performance by: (i) increasing both tenodesis grasp capabilities and muscle strength; (ii) decreasing movement time (MT), and trajectory variability; and (iii) reducing the abnormally increased brain activity. MI can also strengthen motor commands by potentiating recruitment and synchronization of motoneurons, which leads to improved recovery. These improvements reflect brain adaptations induced by MI. Furthermore, MI can be used to control brain-computer interfaces (BCI) that successfully restore grasp capabilities. These results highlight the growing interest for MI and its potential to recover functional grasping in individuals with tetraplegia, and motivate the need for further studies to substantiate it.
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Affiliation(s)
- Sébastien Mateo
- ImpAct Team, Lyon Neuroscience Research Center, Université Lyon 1, Université de Lyon, INSERM U1028, CNRS UMR5292 Lyon, France ; Hospices Civils de Lyon, Hôpital Henry Gabrielle, Plateforme Mouvement et Handicap Lyon, France ; Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France ; Ecole Normale Supérieure de Lyon, CNRS UMR5672 Lyon, France
| | - Franck Di Rienzo
- Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France
| | - Vance Bergeron
- Ecole Normale Supérieure de Lyon, CNRS UMR5672 Lyon, France
| | - Aymeric Guillot
- Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France ; Institut Universitaire de France Paris, France
| | - Christian Collet
- Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France
| | - Gilles Rode
- ImpAct Team, Lyon Neuroscience Research Center, Université Lyon 1, Université de Lyon, INSERM U1028, CNRS UMR5292 Lyon, France ; Hospices Civils de Lyon, Hôpital Henry Gabrielle, Plateforme Mouvement et Handicap Lyon, France
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24
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Rouse AG, Schieber MH. Advancing brain-machine interfaces: moving beyond linear state space models. Front Syst Neurosci 2015; 9:108. [PMID: 26283932 PMCID: PMC4516874 DOI: 10.3389/fnsys.2015.00108] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2015] [Accepted: 07/13/2015] [Indexed: 12/20/2022] Open
Abstract
Advances in recent years have dramatically improved output control by Brain-Machine Interfaces (BMIs). Such devices nevertheless remain robotic and limited in their movements compared to normal human motor performance. Most current BMIs rely on transforming recorded neural activity to a linear state space composed of a set number of fixed degrees of freedom. Here we consider a variety of ways in which BMI design might be advanced further by applying non-linear dynamics observed in normal motor behavior. We consider (i) the dynamic range and precision of natural movements, (ii) differences between cortical activity and actual body movement, (iii) kinematic and muscular synergies, and (iv) the implications of large neuronal populations. We advance the hypothesis that a given population of recorded neurons may transmit more useful information than can be captured by a single, linear model across all movement phases and contexts. We argue that incorporating these various non-linear characteristics will be an important next step in advancing BMIs to more closely match natural motor performance.
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Affiliation(s)
- Adam G Rouse
- Department of Neurology, University of Rochester Rochester, NY, USA ; Department of Neurobiology and Anatomy, University of Rochester Rochester, NY, USA ; Department of Biomedical Engineering, University of Rochester Rochester, NY, USA
| | - Marc H Schieber
- Department of Neurology, University of Rochester Rochester, NY, USA ; Department of Neurobiology and Anatomy, University of Rochester Rochester, NY, USA ; Department of Biomedical Engineering, University of Rochester Rochester, NY, USA
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25
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Abstract
Single neuron actions and interactions are the sine qua non of brain function, and nearly all diseases and injuries of the CNS trace their clinical sequelae to neuronal dysfunction or failure. Remarkably, discussion of neuronal activity is largely absent in clinical neuroscience. Advances in neurotechnology and computational capabilities, accompanied by shifts in theoretical frameworks, have led to renewed interest in the information represented by single neurons. Using direct interfaces with the nervous system, millisecond-scale information will soon be extracted from single neurons in clinical environments, supporting personalized treatment of neurologic and psychiatric disease. In this Perspective, we focus on single-neuronal activity in restoring communication and motor control in patients suffering from devastating neurological injuries. We also explore the single neuron's role in epilepsy and movement disorders, surgical anesthesia, and in cognitive processes disrupted in neurodegenerative and neuropsychiatric disease. Finally, we speculate on how technological advances will revolutionize neurotherapeutics.
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Affiliation(s)
- Sydney S Cash
- Neurotechnology Trials Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
| | - Leigh R Hochberg
- Neurotechnology Trials Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; School of Engineering and Institute for Brain Science, Brown University, Providence, RI 02912, USA; Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, RI 02908, USA.
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26
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Malik WQ, Hochberg LR, Donoghue JP, Brown EN. Modulation depth estimation and variable selection in state-space models for neural interfaces. IEEE Trans Biomed Eng 2015; 62:570-81. [PMID: 25265627 PMCID: PMC4356256 DOI: 10.1109/tbme.2014.2360393] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Rapid developments in neural interface technology are making it possible to record increasingly large signal sets of neural activity. Various factors such as asymmetrical information distribution and across-channel redundancy may, however, limit the benefit of high-dimensional signal sets, and the increased computational complexity may not yield corresponding improvement in system performance. High-dimensional system models may also lead to overfitting and lack of generalizability. To address these issues, we present a generalized modulation depth measure using the state-space framework that quantifies the tuning of a neural signal channel to relevant behavioral covariates. For a dynamical system, we develop computationally efficient procedures for estimating modulation depth from multivariate data. We show that this measure can be used to rank neural signals and select an optimal channel subset for inclusion in the neural decoding algorithm. We present a scheme for choosing the optimal subset based on model order selection criteria. We apply this method to neuronal ensemble spike-rate decoding in neural interfaces, using our framework to relate motor cortical activity with intended movement kinematics. With offline analysis of intracortical motor imagery data obtained from individuals with tetraplegia using the BrainGate neural interface, we demonstrate that our variable selection scheme is useful for identifying and ranking the most information-rich neural signals. We demonstrate that our approach offers several orders of magnitude lower complexity but virtually identical decoding performance compared to greedy search and other selection schemes. Our statistical analysis shows that the modulation depth of human motor cortical single-unit signals is well characterized by the generalized Pareto distribution. Our variable selection scheme has wide applicability in problems involving multisensor signal modeling and estimation in biomedical engineering systems.
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Affiliation(s)
- Wasim Q. Malik
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School Boston, MA 02115 USA; with the Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA02139 USA; with the School of Engineering, and the Institute for Brain Science, Brown University, Providence, RI 02912 USA; and with the Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI 02908 USA ()
| | - Leigh R. Hochberg
- Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI 02908 USA, with the Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115 USA; and with the School of Engineering, and the Institute for Brain Science, Brown University, Providence, RI 02912 USA ()
| | - John P. Donoghue
- Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI 02908 USA, and also with the Department of Neuroscience, and the Institute for Brain Science, Brown University, Providence, RI 02912 USA ()
| | - Emery N. Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115 USA, and also with the Institute for Medical Engineering and Science, and the Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA ()
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27
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Yuan H, He B. Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives. IEEE Trans Biomed Eng 2015; 61:1425-35. [PMID: 24759276 DOI: 10.1109/tbme.2014.2312397] [Citation(s) in RCA: 224] [Impact Index Per Article: 24.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems extract specific features of brain activity and translate them into control signals that drive an output. Recently, a category of BCIs that are built on the rhythmic activity recorded over the sensorimotor cortex, i.e., the sensorimotor rhythm (SMR), has attracted considerable attention among the BCIs that use noninvasive neural recordings, e.g., electroencephalography (EEG), and have demonstrated the capability of multidimensional prosthesis control. This paper reviews the current state and future perspectives of SMR-based BCI and its clinical applications, in particular focusing on the EEG SMR. The characteristic features of SMR from the human brain are described and their underlying neural sources are discussed. The functional components of SMR-based BCI, together with its current clinical applications, are reviewed. Finally, limitations of SMR-BCIs and future outlooks are also discussed.
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28
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Novak D, Omlin X, Leins-Hess R, Riener R. Predicting targets of human reaching motions using different sensing technologies. IEEE Trans Biomed Eng 2013; 60:2645-54. [PMID: 23674417 DOI: 10.1109/tbme.2013.2262455] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Rapid recognition of voluntary motions is crucial in human-computer interaction, but few studies compare the predictive abilities of different sensing technologies. This paper thus compares performances of different technologies when predicting targets of human reaching motions: electroencephalography (EEG), electrooculography, camera-based eye tracking, electromyography (EMG), hand position, and the user's preferences. Supervised machine learning is used to make predictions at different points in time (before and during limb motion) with each individual sensing modality. Different modalities are then combined using an algorithm that takes into account the different times at which modalities provide useful information. Results show that EEG can make predictions before limb motion onset, but requires subject-specific training and exhibits decreased performance as the number of possible targets increases. EMG and hand position give high accuracy, but only once the motion has begun. Eye tracking is robust and exhibits high accuracy at the very onset of limb motion. Several advantages of combining different modalities are also shown, including advantages of combining measurements with contextual data. Finally, some recommendations are given for sensing modalities with regard to different criteria and applications. The information could aid human-computer interaction designers in selecting and evaluating appropriate equipment for their applications.
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Affiliation(s)
- Domen Novak
- Sensory-Motor Systems Lab, ETH Zurich, CH-8092 Zurich, Switzerland.
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Lee SM, Kim JH, Byeon HJ, Choi YY, Park KS, Lee SH. A capacitive, biocompatible and adhesive electrode for long-term and cap-free monitoring of EEG signals. J Neural Eng 2013; 10:036006. [PMID: 23574793 DOI: 10.1088/1741-2560/10/3/036006] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Long-term electroencephalogram (EEG) monitoring broadens EEG applications to various areas, but it requires cap-free recording of EEG signals. Our objective here is to develop a capacitive, small-sized, adhesive and biocompatible electrode for the cap-free and long-term EEG monitoring. APPROACH We have developed an electrode made of polydimethylsiloxane (PDMS) and adhesive PDMS for EEG monitoring. This electrode can be attached to a hairy scalp and be completely hidden by the hair. We tested its electrical and mechanical (adhesive) properties by measuring voltage gain to frequency and adhesive force using 30 repeat cycles of the attachment and detachment test. Electrode performance on EEG was evaluated by alpha rhythm detection and measuring steady state visually evoked potential and N100 auditory evoked potential. MAIN RESULTS We observed the successful recording of alpha rhythm and evoked signals to diverse stimuli with high signal quality. The biocompatibility of the electrode was verified and a survey found that the electrode was comfortable and convenient to wear. SIGNIFICANCE These results indicate that the proposed EEG electrode is suitable and convenient for long term EEG monitoring.
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Affiliation(s)
- Seung Min Lee
- Department of Biomedical Engineering, College of Health Science, Korea University, Seoul 136-100, Korea.
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Novak D, Omlin X, Leins-Hess R, Riener R. Effectiveness of different sensing modalities in predicting targets of reaching movements. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:4255-4258. [PMID: 24110672 DOI: 10.1109/embc.2013.6610485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Human motion recognition is essential for many biomedical applications, but few studies compare the abilities of multiple sensing modalities. This paper thus evaluates the effectiveness of different modalities when predicting targets of human reaching movements. Electroencephalography, electrooculography, camera-based eye tracking, electromyography, hand tracking and the user's preferences are used to make predictions at different points in time. Prediction accuracies are calculated based on data from 10 subjects in within-subject crossvalidation. Results show that electroencephalography can make predictions before limb motion onset, but its accuracy decreases as the number of potential targets increases. Electromyography and hand tracking give high accuracy, but only after motion onset. Eye tracking is robust and gives high accuracy at limb motion onset. Combining multiple modalities can increase accuracy, though not always. While many studies have evaluated individual sensing modalities, this study provides quantitative data on many modalities at different points of time in a single setting. The information could help biomedical engineers choose the most appropriate equipment for a particular application.
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