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      Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

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          Paralysis following spinal cord injury (SCI), brainstem stroke, amyotrophic lateral sclerosis (ALS) and other disorders can disconnect the brain from the body, eliminating the ability to carry out volitional movements. A neural interface system (NIS) 15 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with longstanding tetraplegia can use an NIS to move and click a computer cursor and to control physical devices 68 . Able-bodied monkeys have used an NIS to control a robotic arm 9 , but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here, we demonstrate the ability of two people with long-standing tetraplegia to use NIS-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor five years earlier, also used a robotic arm to drink coffee from a bottle. While robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after CNS injury, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.

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          Most cited references 31

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          Neuronal ensemble control of prosthetic devices by a human with tetraplegia.

          Neuromotor prostheses (NMPs) aim to replace or restore lost motor functions in paralysed humans by routeing movement-related signals from the brain, around damaged parts of the nervous system, to external effectors. To translate preclinical results from intact animals to a clinically useful NMP, movement signals must persist in cortex after spinal cord injury and be engaged by movement intent when sensory inputs and limb movement are long absent. Furthermore, NMPs would require that intention-driven neuronal activity be converted into a control signal that enables useful tasks. Here we show initial results for a tetraplegic human (MN) using a pilot NMP. Neuronal ensemble activity recorded through a 96-microelectrode array implanted in primary motor cortex demonstrated that intended hand motion modulates cortical spiking patterns three years after spinal cord injury. Decoders were created, providing a 'neural cursor' with which MN opened simulated e-mail and operated devices such as a television, even while conversing. Furthermore, MN used neural control to open and close a prosthetic hand, and perform rudimentary actions with a multi-jointed robotic arm. These early results suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis.
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            Cortical control of a prosthetic arm for self-feeding.

            Arm movement is well represented in populations of neurons recorded from the motor cortex. Cortical activity patterns have been used in the new field of brain-machine interfaces to show how cursors on computer displays can be moved in two- and three-dimensional space. Although the ability to move a cursor can be useful in its own right, this technology could be applied to restore arm and hand function for amputees and paralysed persons. However, the use of cortical signals to control a multi-jointed prosthetic device for direct real-time interaction with the physical environment ('embodiment') has not been demonstrated. Here we describe a system that permits embodied prosthetic control; we show how monkeys (Macaca mulatta) use their motor cortical activity to control a mechanized arm replica in a self-feeding task. In addition to the three dimensions of movement, the subjects' cortical signals also proportionally controlled a gripper on the end of the arm. Owing to the physical interaction between the monkey, the robotic arm and objects in the workspace, this new task presented a higher level of difficulty than previous virtual (cursor-control) experiments. Apart from an example of simple one-dimensional control, previous experiments have lacked physical interaction even in cases where a robotic arm or hand was included in the control loop, because the subjects did not use it to interact with physical objects-an interaction that cannot be fully simulated. This demonstration of multi-degree-of-freedom embodied prosthetic control paves the way towards the development of dexterous prosthetic devices that could ultimately achieve arm and hand function at a near-natural level.
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              Brain-controlled interfaces: movement restoration with neural prosthetics.

              Brain-controlled interfaces are devices that capture brain transmissions involved in a subject's intention to act, with the potential to restore communication and movement to those who are immobilized. Current devices record electrical activity from the scalp, on the surface of the brain, and within the cerebral cortex. These signals are being translated to command signals driving prosthetic limbs and computer displays. Somatosensory feedback is being added to this control as generated behaviors become more complex. New technology to engineer the tissue-electrode interface, electrode design, and extraction algorithms to transform the recorded signal to movement will help translate exciting laboratory demonstrations to patient practice in the near future.

                Author and article information

                10 April 2012
                16 May 2012
                01 May 2013
                : 485
                : 7398
                : 372-375
                [1 ]Rehabilitation Research & Development Service, Department of Veterans Affairs, Providence, RI.
                [2 ]School of Engineering and Institute for Brain Science, Brown University, Providence, RI.
                [3 ]Department of Neuroscience and Institute for Brain Science, Brown University, Providence, RI.
                [4 ]Massachusetts General Hospital, Boston, MA.
                [5 ]Harvard Medical School, Boston, MA.
                [6 ]German Aerospace Center, Institute of Robotics and Mechatronics (DLR, Oberpfaffenhofen), Germany.
                Author notes
                Correspondence and requests for materials should be addressed to J.P.D. ( john_donoghue@ ) or L.R.H. ( leigh@ )

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                Funded by: National Institute of Child Health & Human Development : NICHD
                Award ID: HHSN275201100018C || HD
                Funded by: National Institute of Child Health & Human Development : NICHD
                Award ID: RC1 HD063931-02 || HD
                Funded by: National Institute of Neurological Disorders and Stroke : NINDS
                Award ID: R56 NS025074-23 || NS
                Funded by: National Institute of Biomedical Imaging and Bioengineering : NIBIB
                Award ID: R01 EB007401-05 || EB
                Funded by: National Institute on Deafness and Other Communication Disorders : NIDCD
                Award ID: R01 DC009899-02 || DC
                Funded by: National Institute of Child Health & Human Development : NICHD
                Award ID: N01 HD053403 || HD



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