Cortical neural prosthetics

Control of prostheses using cortical signals is based on three elements: chronic microelectrode arrays, extraction algorithms, and prosthetic effectors. Arrays of microelectrodes are permanently implanted in cerebral cortex. These arrays must record populations of single- and multiunit activity inde...

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Bibliographic Details
Published inAnnual review of neuroscience Vol. 27; no. 1; pp. 487 - 507
Main Author SCHWARTZ, Andrew B
Format Journal Article
LanguageEnglish
Published Palo Alto, CA Annual Reviews 01.01.2004
Annual Reviews, Inc
Subjects
Adaptation, Physiological - physiology
Algorithms
Animals
Arms
Biological and medical sciences
Brain
Efferent Pathways - physiology
Electrodes, Implanted - standards
Electrodes, Implanted - trends
Fundamental and applied biological sciences. Psychology
Humans
Learning
Motor control and motor pathways. Reflexes. Control centers of vegetative functions. Vestibular system and equilibration
Motor Cortex - physiology
Motor Cortex - surgery
Movement - physiology
Nerve Net - physiology
Neurons
Paralysis - therapy
Prostheses
Technology
User-Computer Interface
Vertebrates: nervous system and sense organs
Cerebral cortex
Prosthesis
arm control
Central nervous system
Review
chronic microelectrodes
Motor control
Encephalon
prostheses
extraction algorithms
Microelectrode
Arm
neuronal populations
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Summary:Control of prostheses using cortical signals is based on three elements: chronic microelectrode arrays, extraction algorithms, and prosthetic effectors. Arrays of microelectrodes are permanently implanted in cerebral cortex. These arrays must record populations of single- and multiunit activity indefinitely. Information containing position and velocity correlates of animate movement needs to be extracted continuously in real time from the recorded activity. Prosthetic arms, the current effectors used in this work, need to have the agility and configuration of natural arms. Demonstrations using closed-loop control show that subjects change their neural activity to improve performance with these devices. Adaptive-learning algorithms that capitalize on these improvements show that this technology has the capability of restoring much of the arm movement lost with immobilizing deficits.
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ISSN:0147-006X
1545-4126
DOI:10.1146/annurev.neuro.27.070203.144233