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Brain-Machine Interfaces: From Restoring Sensorimotor Control to Augmenting Cognition

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Handbook of Neuroengineering

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Abstract

Brain-machine interface (BMI) technologies developed at the turn of the last century were expected to not only decode the intention to move, but to use that signal to stimulate back into the nervous system to restore movement to the patient’s own body. As the field moves to achieve these goals, two important events have spawned parallel pathways that have changed our fundamental idea about how to proceed and, at the same time, allowed unprecedented insight into brain function. First, studies in human subjects have made clear that BMI will not simply restore function, but rather, patients will learn to use the technology in a way that assists what the user wants to do rather than replace function. Second, rapid advances in neurotechnology in the past 20 years have shown BMI to be an invaluable tool to study brain function and suggest it will be applicable to restoring and perhaps even augmenting cognition. This chapter will provide some historical context to the development of BMI, and then review recent advances to understand the representation of information in the brain.

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Abbreviations

BMI:

Brain-machine interfaces

DBS:

Deep brain stimulations

FA:

Factor analysis

FES:

Functional electrical stimulations

GLM:

General linear model

GPFA:

Gaussian-process factor analysis

Hz:

Hertz

ICA:

Independent components analysis

MHE:

Moving horizon estimation

MPC:

Mode; predictive control

PCA:

Principal components analysis

PD:

Parkinson’s disease

PWA:

Piecewise affine

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Authors and Affiliations

  1. Department of Biomedical Engineering, University of California Davis, Davis, CA, USA

    Karen Moxon

  2. Department of Mechanical and Aerospace Engineering, University of California Davis, Davis, CA, USA

    Zhaodan Kong

  3. Department of Neurobiology, Physiology and Behavior, University of California Davis, Davis, CA, USA

    Jochen Ditterich

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  1. Karen Moxon
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  2. Zhaodan Kong
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  3. Jochen Ditterich
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Correspondence to Karen Moxon or Jochen Ditterich .

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Editors and Affiliations

  1. Neuroengineering and Biomedical Instrumentation Laboratory Department of Biomedical Engineering and Department of Electrical and Computer Engineering, Neurology, Johns Hopkins University, Baltimore, MD, USA

    Nitish V. Thakor

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Moxon, K., Kong, Z., Ditterich, J. (2023). Brain-Machine Interfaces: From Restoring Sensorimotor Control to Augmenting Cognition. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-5540-1_36

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