Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces

Intracortical brain-machine interfaces (BMIs) aim to restore lost motor function to people with neurological deficits by decoding neural activity into control signals for guiding prostheses. An important challenge facing BMIs is that, over time, the number of neural signals recorded from implanted m...

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Published inScientific reports Vol. 7; no. 1; pp. 7395 - 16
Main Authors Kao, Jonathan C., Ryu, Stephen I., Shenoy, Krishna V.
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 07.08.2017
Nature Publishing Group
Nature Portfolio
Subjects
631/114/116/2393
631/114/116/2394
631/378/2632/1663
631/378/2632/2634
631/378/2632/2635
Algorithms
Arrays
Behavior
Brain research
Clinical trials
Cortex (motor)
Electrical engineering
Electrodes
Humanities and Social Sciences
Interfaces
Kinematics
multidisciplinary
Neurological diseases
Neurons
Neurosciences
Parameter estimation
Population
Population dynamics
Principles
Prosthetics
Recovery of function
Science
Science (multidisciplinary)
Software
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ISSN2045-2322
2045-2322
DOI10.1038/s41598-017-06029-x

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Abstract Intracortical brain-machine interfaces (BMIs) aim to restore lost motor function to people with neurological deficits by decoding neural activity into control signals for guiding prostheses. An important challenge facing BMIs is that, over time, the number of neural signals recorded from implanted multielectrode arrays will decline and result in a concomitant decrease of BMI performance. We sought to extend BMI lifetime by developing an algorithmic technique, implemented entirely in software, to improve performance over state-of-the-art algorithms as the number of recorded neural signals decline. Our approach augments the decoder by incorporating neural population dynamics remembered from an earlier point in the array lifetime. We demonstrate, in closed-loop experiments with two rhesus macaques, that after the loss of approximately 60% of recording electrodes, our approach outperforms state-of-the-art decoders by a factor of 3.2× and 1.7× (corresponding to a 46% and 22% recovery of maximal performance). Further, our results suggest that neural population dynamics in motor cortex are invariant to the number of recorded neurons. By extending functional BMI lifetime, this approach increases the clinical viability of BMIs.
AbstractList Abstract Intracortical brain-machine interfaces (BMIs) aim to restore lost motor function to people with neurological deficits by decoding neural activity into control signals for guiding prostheses. An important challenge facing BMIs is that, over time, the number of neural signals recorded from implanted multielectrode arrays will decline and result in a concomitant decrease of BMI performance. We sought to extend BMI lifetime by developing an algorithmic technique, implemented entirely in software, to improve performance over state-of-the-art algorithms as the number of recorded neural signals decline. Our approach augments the decoder by incorporating neural population dynamics remembered from an earlier point in the array lifetime. We demonstrate, in closed-loop experiments with two rhesus macaques, that after the loss of approximately 60% of recording electrodes, our approach outperforms state-of-the-art decoders by a factor of 3.2× and 1.7× (corresponding to a 46% and 22% recovery of maximal performance). Further, our results suggest that neural population dynamics in motor cortex are invariant to the number of recorded neurons. By extending functional BMI lifetime, this approach increases the clinical viability of BMIs.
Intracortical brain-machine interfaces (BMIs) aim to restore lost motor function to people with neurological deficits by decoding neural activity into control signals for guiding prostheses. An important challenge facing BMIs is that, over time, the number of neural signals recorded from implanted multielectrode arrays will decline and result in a concomitant decrease of BMI performance. We sought to extend BMI lifetime by developing an algorithmic technique, implemented entirely in software, to improve performance over state-of-the-art algorithms as the number of recorded neural signals decline. Our approach augments the decoder by incorporating neural population dynamics remembered from an earlier point in the array lifetime. We demonstrate, in closed-loop experiments with two rhesus macaques, that after the loss of approximately 60% of recording electrodes, our approach outperforms state-of-the-art decoders by a factor of 3.2× and 1.7× (corresponding to a 46% and 22% recovery of maximal performance). Further, our results suggest that neural population dynamics in motor cortex are invariant to the number of recorded neurons. By extending functional BMI lifetime, this approach increases the clinical viability of BMIs.Intracortical brain-machine interfaces (BMIs) aim to restore lost motor function to people with neurological deficits by decoding neural activity into control signals for guiding prostheses. An important challenge facing BMIs is that, over time, the number of neural signals recorded from implanted multielectrode arrays will decline and result in a concomitant decrease of BMI performance. We sought to extend BMI lifetime by developing an algorithmic technique, implemented entirely in software, to improve performance over state-of-the-art algorithms as the number of recorded neural signals decline. Our approach augments the decoder by incorporating neural population dynamics remembered from an earlier point in the array lifetime. We demonstrate, in closed-loop experiments with two rhesus macaques, that after the loss of approximately 60% of recording electrodes, our approach outperforms state-of-the-art decoders by a factor of 3.2× and 1.7× (corresponding to a 46% and 22% recovery of maximal performance). Further, our results suggest that neural population dynamics in motor cortex are invariant to the number of recorded neurons. By extending functional BMI lifetime, this approach increases the clinical viability of BMIs.
Intracortical brain-machine interfaces (BMIs) aim to restore lost motor function to people with neurological deficits by decoding neural activity into control signals for guiding prostheses. An important challenge facing BMIs is that, over time, the number of neural signals recorded from implanted multielectrode arrays will decline and result in a concomitant decrease of BMI performance. We sought to extend BMI lifetime by developing an algorithmic technique, implemented entirely in software, to improve performance over state-of-the-art algorithms as the number of recorded neural signals decline. Our approach augments the decoder by incorporating neural population dynamics remembered from an earlier point in the array lifetime. We demonstrate, in closed-loop experiments with two rhesus macaques, that after the loss of approximately 60% of recording electrodes, our approach outperforms state-of-the-art decoders by a factor of 3.2× and 1.7× (corresponding to a 46% and 22% recovery of maximal performance). Further, our results suggest that neural population dynamics in motor cortex are invariant to the number of recorded neurons. By extending functional BMI lifetime, this approach increases the clinical viability of BMIs.
ArticleNumber 7395
Author Kao, Jonathan C.
Shenoy, Krishna V.
Ryu, Stephen I.
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  givenname: Jonathan C.
  surname: Kao
  fullname: Kao, Jonathan C.
  organization: Department of Electrical Engineering, University of California Los Angeles, Department of Electrical Engineering, Stanford University
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  givenname: Stephen I.
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  fullname: Ryu, Stephen I.
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  givenname: Krishna V.
  surname: Shenoy
  fullname: Shenoy, Krishna V.
  email: shenoy@stanford.edu
  organization: Department of Electrical Engineering, Stanford University, Department of Neurobiology, Stanford University, Department of Bioengineering, Stanford University, Neurosciences Program, Stanford University, Howard Hughes Medical Institute, Stanford University
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Snippet Intracortical brain-machine interfaces (BMIs) aim to restore lost motor function to people with neurological deficits by decoding neural activity into control...
Abstract Intracortical brain-machine interfaces (BMIs) aim to restore lost motor function to people with neurological deficits by decoding neural activity into...
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Title Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces
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