The Experts below are selected from a list of 3021 Experts worldwide ranked by ideXlab platform
Krishna V Shenoy - One of the best experts on this subject based on the ideXlab platform.
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a high performance Neural Prosthesis incorporating discrete state selection with hidden markov models
2017Co-Authors: Jonathan C Kao, Paul Nuyujukian, Stephen I Ryu, Krishna V ShenoyAbstract:Communication Neural prostheses aim to restore efficient communication to people with motor neurological injury or disease by decoding Neural activity into control signals. These control signals are both analog (e.g., the velocity of a computer mouse) and discrete (e.g., clicking an icon with a computer mouse) in nature. Effective, high-performing, and intuitive-to-use communication prostheses should be capable of decoding both analog and discrete state variables seamlessly. However, to date, the highest-performing autonomous communication prostheses rely on precise analog decoding and typically do not incorporate high-performance discrete decoding. In this report, we incorporated a hidden Markov model (HMM) into an intracortical communication Prosthesis to enable accurate and fast discrete state decoding in parallel with analog decoding. In closed-loop experiments with nonhuman primates implanted with multielectrode arrays, we demonstrate that incorporating an HMM into a Neural Prosthesis can increase state-of-the-art achieved bitrate by $13.9\%$ and $4.2\%$ in two monkeys ( $p ). We found that the transition model of the HMM is critical to achieving this performance increase. Further, we found that using an HMM resulted in the highest achieved peak performance we have ever observed for these monkeys, achieving peak bitrates of $6.5$ , $5.7$ , and $4.7$ bps in Monkeys J, R, and L, respectively. Finally, we found that this Neural Prosthesis was robustly controllable for the duration of entire experimental sessions. These results demonstrate that high-performance discrete decoding can be beneficially combined with analog decoding to achieve new state-of-the-art levels of performance.
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a high performance keyboard Neural Prosthesis enabled by task optimization
2015Co-Authors: Paul Nuyujukian, Joline M Fan, Jonathan C Kao, Stephen I Ryu, Krishna V ShenoyAbstract:Communication Neural prostheses are an emerging class of medical devices that aim to restore efficient communication to people suffering from paralysis. These systems rely on an interface with the user, either via the use of a continuously moving cursor (e.g., mouse) or the discrete selection of symbols (e.g., keyboard). In developing these interfaces, many design choices have a significant impact on the performance of the system. The objective of this study was to explore the design choices of a continuously moving cursor Neural Prosthesis and optimize the interface to maximize information theoretic performance. We swept interface parameters of two keyboard-like tasks to find task and subject-specific optimal parameters as measured by achieved bitrate using two rhesus macaques implanted with multielectrode arrays. In this paper, we present the highest performing free-paced Neural Prosthesis under any recording modality with sustainable communication rates of up to 3.5 bits/s. These findings demonstrate that meaningful high performance can be achieved using an intracortical Neural Prosthesis, and that, when optimized, these systems may be appropriate for use as communication devices for those with physical disabilities.
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performance sustaining intracortical Neural prostheses
2014Co-Authors: Paul Nuyujukian, Joline M Fan, Jonathan C Kao, Stephen I Ryu, Sergey D Stavisky, Krishna V ShenoyAbstract:Objective. Neural prostheses, or brain–machine interfaces, aim to restore efficient communication and movement ability to those suffering from paralysis. A major challenge these systems face is robust performance, particularly with aging signal sources. The aim in this study was to develop a Neural Prosthesis that could sustain high performance in spite of signal instability while still minimizing retraining time. Approach. We trained two rhesus macaques implanted with intracortical microelectrode arrays 1–4 years prior to this study to acquire targets with a Neurally-controlled cursor. We measured their performance via achieved bitrate (bits per second, bps). This task was repeated over contiguous days to evaluate the sustained performance across time. Main results. We found that in the monkey with a younger (i.e., two year old) implant and better signal quality, a fixed decoder could sustain performance for a month at a rate of 4 bps, the highest achieved communication rate reported to date. This fixed decoder was evaluated across 22 months and experienced a performance decline at a rate of 0.24 bps yr-1. In the monkey with the older (i.e., 3.5 year old) implant and poorer signal quality, a fixed decoder could not sustain performance for more than a few days. Nevertheless, performance in this monkey was maintained for two weeks without requiring additional online retraining time by utilizing prior days' experimental data. Upon analysis of the changes in channel tuning, we found that this stability appeared partially attributable to the cancelling-out of Neural tuning fluctuations when projected to two-dimensional cursor movements. Significance. The findings in this study (1) document the highest-performing communication Neural Prosthesis in monkeys, (2) confirm and extend prior reports of the stability of fixed decoders, and (3) demonstrate a protocol for system stability under conditions where fixed decoders would otherwise fail. These improvements to decoder stability are important for minimizing training time and should make Neural prostheses more practical to use.
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a high performance Neural Prosthesis enabled by control algorithm design
2012Co-Authors: Vikash Gilja, Paul Nuyujukian, Cynthia A Chestek, John P Cunningham, Byron M Yu, Mark M Churchland, Matthew T Kaufman, Krishna V ShenoyAbstract:Current Neural prostheses can translate Neural activity into control signals for guiding prosthetic devices, but poor performance limits practical application. Here the authors present a new cursor-control algorithm that approaches native arm control speed and accuracy, permits sustained uninterrupted use for hours, generalizes to more challenging tasks and provides repeatable high performance for years after implantation, thereby increasing the clinical viability of Neural prostheses.
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cortical Neural Prosthesis performance improves when eye position is monitored
2008Co-Authors: Aaron P Batista, Stephen I Ryu, G Santhanam, Afsheen Afshar, Krishna V ShenoyAbstract:Neural prostheses that extract signals directly from cortical neurons have recently become feasible as assistive technologies for tetraplegic individuals. Significant effort toward improving the performance of these systems is now warranted. A simple technique that can improve Prosthesis performance is to account for the direction of gaze in the operation of the Prosthesis. This proposal stems from recent discoveries that the direction of gaze influences Neural activity in several areas that are commonly targeted for electrode implantation in Neural prosthetics. Here, we first demonstrate that Neural Prosthesis performance does improve when eye position is taken into account. We then show that eye position can be estimated directly from Neural activity, and thus performance gains can be realized even without a device that tracks eye position.
Paul Nuyujukian - One of the best experts on this subject based on the ideXlab platform.
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a high performance Neural Prosthesis incorporating discrete state selection with hidden markov models
2017Co-Authors: Jonathan C Kao, Paul Nuyujukian, Stephen I Ryu, Krishna V ShenoyAbstract:Communication Neural prostheses aim to restore efficient communication to people with motor neurological injury or disease by decoding Neural activity into control signals. These control signals are both analog (e.g., the velocity of a computer mouse) and discrete (e.g., clicking an icon with a computer mouse) in nature. Effective, high-performing, and intuitive-to-use communication prostheses should be capable of decoding both analog and discrete state variables seamlessly. However, to date, the highest-performing autonomous communication prostheses rely on precise analog decoding and typically do not incorporate high-performance discrete decoding. In this report, we incorporated a hidden Markov model (HMM) into an intracortical communication Prosthesis to enable accurate and fast discrete state decoding in parallel with analog decoding. In closed-loop experiments with nonhuman primates implanted with multielectrode arrays, we demonstrate that incorporating an HMM into a Neural Prosthesis can increase state-of-the-art achieved bitrate by $13.9\%$ and $4.2\%$ in two monkeys ( $p ). We found that the transition model of the HMM is critical to achieving this performance increase. Further, we found that using an HMM resulted in the highest achieved peak performance we have ever observed for these monkeys, achieving peak bitrates of $6.5$ , $5.7$ , and $4.7$ bps in Monkeys J, R, and L, respectively. Finally, we found that this Neural Prosthesis was robustly controllable for the duration of entire experimental sessions. These results demonstrate that high-performance discrete decoding can be beneficially combined with analog decoding to achieve new state-of-the-art levels of performance.
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Clinical translation of a high-performance Neural Prosthesis
2015Co-Authors: Vikash Gilja, Anish A. Sarma, Brittany L. Sorice, Chethan Pandarinath, Beata Jarosiewicz, Christine H Blabe, John D Simeral, János A. Perge, Paul Nuyujukian, Leigh R. HochbergAbstract:Neural prostheses have the potential to improve the quality of life of individuals with paralysis by directly mapping Neural activity to limb- and computer-control signals. We translated a Neural prosthetic system previously developed in animal model studies for use by two individuals with amyotrophic lateral sclerosis who had intracortical microelectrode arrays placed in motor cortex. Measured more than 1 year after implant, the Neural cursor-control system showed the highest published performance achieved by a person to date, more than double that of previous pilot clinical trial participants.
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a high performance keyboard Neural Prosthesis enabled by task optimization
2015Co-Authors: Paul Nuyujukian, Joline M Fan, Jonathan C Kao, Stephen I Ryu, Krishna V ShenoyAbstract:Communication Neural prostheses are an emerging class of medical devices that aim to restore efficient communication to people suffering from paralysis. These systems rely on an interface with the user, either via the use of a continuously moving cursor (e.g., mouse) or the discrete selection of symbols (e.g., keyboard). In developing these interfaces, many design choices have a significant impact on the performance of the system. The objective of this study was to explore the design choices of a continuously moving cursor Neural Prosthesis and optimize the interface to maximize information theoretic performance. We swept interface parameters of two keyboard-like tasks to find task and subject-specific optimal parameters as measured by achieved bitrate using two rhesus macaques implanted with multielectrode arrays. In this paper, we present the highest performing free-paced Neural Prosthesis under any recording modality with sustainable communication rates of up to 3.5 bits/s. These findings demonstrate that meaningful high performance can be achieved using an intracortical Neural Prosthesis, and that, when optimized, these systems may be appropriate for use as communication devices for those with physical disabilities.
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performance sustaining intracortical Neural prostheses
2014Co-Authors: Paul Nuyujukian, Joline M Fan, Jonathan C Kao, Stephen I Ryu, Sergey D Stavisky, Krishna V ShenoyAbstract:Objective. Neural prostheses, or brain–machine interfaces, aim to restore efficient communication and movement ability to those suffering from paralysis. A major challenge these systems face is robust performance, particularly with aging signal sources. The aim in this study was to develop a Neural Prosthesis that could sustain high performance in spite of signal instability while still minimizing retraining time. Approach. We trained two rhesus macaques implanted with intracortical microelectrode arrays 1–4 years prior to this study to acquire targets with a Neurally-controlled cursor. We measured their performance via achieved bitrate (bits per second, bps). This task was repeated over contiguous days to evaluate the sustained performance across time. Main results. We found that in the monkey with a younger (i.e., two year old) implant and better signal quality, a fixed decoder could sustain performance for a month at a rate of 4 bps, the highest achieved communication rate reported to date. This fixed decoder was evaluated across 22 months and experienced a performance decline at a rate of 0.24 bps yr-1. In the monkey with the older (i.e., 3.5 year old) implant and poorer signal quality, a fixed decoder could not sustain performance for more than a few days. Nevertheless, performance in this monkey was maintained for two weeks without requiring additional online retraining time by utilizing prior days' experimental data. Upon analysis of the changes in channel tuning, we found that this stability appeared partially attributable to the cancelling-out of Neural tuning fluctuations when projected to two-dimensional cursor movements. Significance. The findings in this study (1) document the highest-performing communication Neural Prosthesis in monkeys, (2) confirm and extend prior reports of the stability of fixed decoders, and (3) demonstrate a protocol for system stability under conditions where fixed decoders would otherwise fail. These improvements to decoder stability are important for minimizing training time and should make Neural prostheses more practical to use.
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a high performance Neural Prosthesis enabled by control algorithm design
2012Co-Authors: Vikash Gilja, Paul Nuyujukian, Cynthia A Chestek, John P Cunningham, Byron M Yu, Mark M Churchland, Matthew T Kaufman, Krishna V ShenoyAbstract:Current Neural prostheses can translate Neural activity into control signals for guiding prosthetic devices, but poor performance limits practical application. Here the authors present a new cursor-control algorithm that approaches native arm control speed and accuracy, permits sustained uninterrupted use for hours, generalizes to more challenging tasks and provides repeatable high performance for years after implantation, thereby increasing the clinical viability of Neural prostheses.
Silvestro Micera - One of the best experts on this subject based on the ideXlab platform.
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Introduction to Neuroprosthetics
2011Co-Authors: Klaus-peter Hoffmann, Silvestro MiceraAbstract:Neuroprosthetics is a comparatively young, dynamically developing subject with double-digit sales growth rates. Due to the preconditions on implantability, biocompatibility, and miniaturization, it is strongly linked to the development of microsystems technology, nanotechnology, information technology, biotechnology, and the application of new materials. The fields of application of neuroprostheses are diseases associated with impairments of myogenic or neurogenic functions. These can lead to the loss of the whole function. Neuroprostheses use electric stimuli to stimulate Neural structures, muscles or receptors, in order to support, augment or partly restore the respective disordered or lost function. Functional disorders include paralysis after stroke, reduced hearing, tremor as an example of movement disorders, or the loss of an extremity. Often, the use of a Neural Prosthesis can improve the quality of life of the person concerned. The objective is to help the patient to participate in everyday life. Thus, cosmetic, ethical, and social aspects always have to be considered.
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3D hybrid electrode structure as implantable interface for a vestibular Neural Prosthesis in humans
2011Co-Authors: Klaus-p. Hoffmann, Jack Digiovanna, Wigand Poppendieck, Simon Tätzner, Maria Izabel Kos, Nils Guinand, Jean-p. Guyot, Silvestro MiceraAbstract:Implantable interfaces are essential components of vestibular Neural prostheses. They interface the biological system with electrical stimulation that is used to restore transfer of vestibular information. Regarding the anatomical situation special 3D structures are required. In this paper, the design and the manufacturing process of a novel 3D hybrid microelectrode structure as interface to the human vestibular system are described. Photolithography techniques, assembling technology and rapid prototyping are used for manufacturing.
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a closed loop Neural Prosthesis for vestibular disorders
2010Co-Authors: Silvestro Micera, Klaus-peter Hoffmann, Andreas Demosthenous, Jack Digiovanna, Alain Berthoz, Jeanphilippe Guyot, Daniel M Merfeld, Manfred MorariAbstract:Vestibular disorders can cause severe problems including nausea, inability to concentrate, and visual deficits. The CLONS project is developing a closed-loop sensory Neural Prosthesis to alleviate these symptoms. Conceptually, the Prosthesis restores vestibular information by stimulating the semicircular canals according to measurements from inertial sensors rigidly affixed to the user. Here we present a project overview and brief update of our progress in animal models and selected human volunteers.
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assessment technologies for the analysis of the efficacy of a vestibular Neural Prosthesis
2010Co-Authors: Silvestro Micera, J Carpaneto, V Genovese, A M Sabatini, Bassi L Luciani, Andrea Mannini, Vito Monaco, L Odetti, Wigand Poppendieck, Klaus-peter HoffmannAbstract:The development of systems of functional assessment following the implant of vestibular Neural Prosthesis is present: (1) a wearable sensor system for the recording of head kinematics; (2) a moving platform for the assessment of postural stability. The wearable system can be worn on the head and can be applied for the detection of rotational and linear accelerations. For this purpose, two methods are investigated. The first uses commercially available MEMS sensors, the second follows a biologically inspired approach to mimic the function of the natural vestibular system. Signals provided by the sensors are processed by a digital processor using a stimulus coding system including an extended Kalman filter (EKF). Outcomes of the EKF algorithm will be conditioned in order to control the stimulating unit leading the electrodes implanted in the vestibular system. The moving platform can record information about the ground reaction forces produced during walking in different experimental conditions (different walking speeds, perturbation of locomotion in the antero-posterior and medio-lateral directions, etc.).
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preliminary analysis of multi channel recordings for the development of a high level cortical Neural Prosthesis
2005Co-Authors: Silvestro Micera, J Carpaneto, Maria Alessandra Umilta, Magali J Rochat, L Escola, Vittorio Gallese, M C Carrozza, J Krueger, Giacomo Rizzolatti, P DarioAbstract:The implementation of an effective approach to restore the link between the nervous system and artificial devices in disabled subjects is crucial to increase the acceptability and usability of these systems. Among the different possible solutions, the development of invasive cortical Neural prostheses (ICNPs) is very appealing because of the possibility of extracting information on the user's intention from cortical activity and of delivering a sensory feedback by stimulating the somato-sensory cortex. In the recent past, the efforts of several research groups have been focused on the extraction of low-level commands to directly control the trajectories of the robotic devices by processing cortical signals. However, even if very interesting results have been achieved using this approach, the possibility of extracting more high-level information is becoming to be addressed for its potential advantages. In this paper, the preliminary results of the experiments on the development of a "high-level" ICNP are presented. In particular, a statistical approach is used to characterize the response of the neurons to different experimental conditions and to try to identify the most interesting channels for the development of the ICNP. Moreover, preliminary experiments on pattern recognition using a fuzzy-evolutionary classifier are also presented. Future works will go in the direction of testing extensively the soft-computing classifier in order to discriminate among different robot movements by processing the cortical signals
Andrew Jackson - One of the best experts on this subject based on the ideXlab platform.
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closed loop control of spinal cord stimulation to restore hand function after paralysis
2014Co-Authors: Jonas B Zimmermann, Andrew JacksonAbstract:As yet, no cure exists for upper-limb paralysis resulting from the damage to motor pathways after spinal cord injury or stroke. Recently, Neural activity from the motor cortex of paralyzed individuals has been used to control the movements of a robot arm but restoring function to patients' actual limbs remains a considerable challenge. Previously we have shown that electrical stimulation of the cervical spinal cord in anesthetized monkeys can elicit functional upper-limb movements like reaching and grasping. Here we show that stimulation can be controlled using cortical activity in awake animals to bypass disruption of the corticospinal system, restoring their ability to perform a simple upper-limb task. Monkeys were trained to grasp and pull a spring-loaded handle. After temporary paralysis of the hand was induced by reversible inactivation of primary motor cortex using muscimol, grasp-related single-unit activity from the ventral premotor cortex was converted into stimulation patterns delivered in real-time to the cervical spinal gray matter. During periods of closed-loop stimulation, task-modulated electromyogram, movement amplitude, and task success rate were improved relative to interleaved control periods without stimulation. In some sessions, single motor unit activity from weakly active muscles was also used successfully to control stimulation. These results are the first use of a Neural Prosthesis to improve the hand function of primates after motor cortex disruption, and demonstrate the potential for closed-loop cortical control of spinal cord stimulation to reanimate paralyzed limbs.
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the neurochip bci towards a Neural Prosthesis for upper limb function
2006Co-Authors: Andrew Jackson, Chet T Moritz, Jaideep Mavoori, Timothy H Lucas, Eberhard E FetzAbstract:The Neurochip BCI is an autonomously operating interface between an implanted computer chip and recording and stimulating electrodes in the nervous system. By converting Neural activity recorded in one brain area into electrical stimuli delivered to another site, the Neurochip BCI could form the basis for a simple, direct Neural prosthetic. In tests with normal, unrestrained monkeys, the Neurochip continuously recorded activity of single neurons in primary motor cortex for several weeks at a time. Cortical activity was correlated with simultaneously-recorded electromyogram (EMG) activity from arm muscles during free behavior. In separate experiments with anesthetized monkeys, we found that microstimulation of the cervical spinal cord evoked movements of the arm and hand, often involving multiple muscles synergies. These observations suggest that spinal microstimulation controlled by cortical neurons could help compensate for damaged corticospinal projections.
Stephen I Ryu - One of the best experts on this subject based on the ideXlab platform.
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a high performance Neural Prosthesis incorporating discrete state selection with hidden markov models
2017Co-Authors: Jonathan C Kao, Paul Nuyujukian, Stephen I Ryu, Krishna V ShenoyAbstract:Communication Neural prostheses aim to restore efficient communication to people with motor neurological injury or disease by decoding Neural activity into control signals. These control signals are both analog (e.g., the velocity of a computer mouse) and discrete (e.g., clicking an icon with a computer mouse) in nature. Effective, high-performing, and intuitive-to-use communication prostheses should be capable of decoding both analog and discrete state variables seamlessly. However, to date, the highest-performing autonomous communication prostheses rely on precise analog decoding and typically do not incorporate high-performance discrete decoding. In this report, we incorporated a hidden Markov model (HMM) into an intracortical communication Prosthesis to enable accurate and fast discrete state decoding in parallel with analog decoding. In closed-loop experiments with nonhuman primates implanted with multielectrode arrays, we demonstrate that incorporating an HMM into a Neural Prosthesis can increase state-of-the-art achieved bitrate by $13.9\%$ and $4.2\%$ in two monkeys ( $p ). We found that the transition model of the HMM is critical to achieving this performance increase. Further, we found that using an HMM resulted in the highest achieved peak performance we have ever observed for these monkeys, achieving peak bitrates of $6.5$ , $5.7$ , and $4.7$ bps in Monkeys J, R, and L, respectively. Finally, we found that this Neural Prosthesis was robustly controllable for the duration of entire experimental sessions. These results demonstrate that high-performance discrete decoding can be beneficially combined with analog decoding to achieve new state-of-the-art levels of performance.
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a high performance keyboard Neural Prosthesis enabled by task optimization
2015Co-Authors: Paul Nuyujukian, Joline M Fan, Jonathan C Kao, Stephen I Ryu, Krishna V ShenoyAbstract:Communication Neural prostheses are an emerging class of medical devices that aim to restore efficient communication to people suffering from paralysis. These systems rely on an interface with the user, either via the use of a continuously moving cursor (e.g., mouse) or the discrete selection of symbols (e.g., keyboard). In developing these interfaces, many design choices have a significant impact on the performance of the system. The objective of this study was to explore the design choices of a continuously moving cursor Neural Prosthesis and optimize the interface to maximize information theoretic performance. We swept interface parameters of two keyboard-like tasks to find task and subject-specific optimal parameters as measured by achieved bitrate using two rhesus macaques implanted with multielectrode arrays. In this paper, we present the highest performing free-paced Neural Prosthesis under any recording modality with sustainable communication rates of up to 3.5 bits/s. These findings demonstrate that meaningful high performance can be achieved using an intracortical Neural Prosthesis, and that, when optimized, these systems may be appropriate for use as communication devices for those with physical disabilities.
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performance sustaining intracortical Neural prostheses
2014Co-Authors: Paul Nuyujukian, Joline M Fan, Jonathan C Kao, Stephen I Ryu, Sergey D Stavisky, Krishna V ShenoyAbstract:Objective. Neural prostheses, or brain–machine interfaces, aim to restore efficient communication and movement ability to those suffering from paralysis. A major challenge these systems face is robust performance, particularly with aging signal sources. The aim in this study was to develop a Neural Prosthesis that could sustain high performance in spite of signal instability while still minimizing retraining time. Approach. We trained two rhesus macaques implanted with intracortical microelectrode arrays 1–4 years prior to this study to acquire targets with a Neurally-controlled cursor. We measured their performance via achieved bitrate (bits per second, bps). This task was repeated over contiguous days to evaluate the sustained performance across time. Main results. We found that in the monkey with a younger (i.e., two year old) implant and better signal quality, a fixed decoder could sustain performance for a month at a rate of 4 bps, the highest achieved communication rate reported to date. This fixed decoder was evaluated across 22 months and experienced a performance decline at a rate of 0.24 bps yr-1. In the monkey with the older (i.e., 3.5 year old) implant and poorer signal quality, a fixed decoder could not sustain performance for more than a few days. Nevertheless, performance in this monkey was maintained for two weeks without requiring additional online retraining time by utilizing prior days' experimental data. Upon analysis of the changes in channel tuning, we found that this stability appeared partially attributable to the cancelling-out of Neural tuning fluctuations when projected to two-dimensional cursor movements. Significance. The findings in this study (1) document the highest-performing communication Neural Prosthesis in monkeys, (2) confirm and extend prior reports of the stability of fixed decoders, and (3) demonstrate a protocol for system stability under conditions where fixed decoders would otherwise fail. These improvements to decoder stability are important for minimizing training time and should make Neural prostheses more practical to use.
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cortical Neural Prosthesis performance improves when eye position is monitored
2008Co-Authors: Aaron P Batista, Stephen I Ryu, G Santhanam, Afsheen Afshar, Krishna V ShenoyAbstract:Neural prostheses that extract signals directly from cortical neurons have recently become feasible as assistive technologies for tetraplegic individuals. Significant effort toward improving the performance of these systems is now warranted. A simple technique that can improve Prosthesis performance is to account for the direction of gaze in the operation of the Prosthesis. This proposal stems from recent discoveries that the direction of gaze influences Neural activity in several areas that are commonly targeted for electrode implantation in Neural prosthetics. Here, we first demonstrate that Neural Prosthesis performance does improve when eye position is taken into account. We then show that eye position can be estimated directly from Neural activity, and thus performance gains can be realized even without a device that tracks eye position.