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Principles of neural ensemble physiology underlying the operation of brain–machine interfaces

Nature Reviews Neuroscience volume 10, pages 530–540 (2009)Cite this article

Abstract

Research on brain–machine interfaces has been ongoing for at least a decade. During this period, simultaneous recordings of the extracellular electrical activity of hundreds of individual neurons have been used for direct, real-time control of various artificial devices. Brain–machine interfaces have also added greatly to our knowledge of the fundamental physiological principles governing the operation of large neural ensembles. Further understanding of these principles is likely to have a key role in the future development of neuroprosthetics for restoring mobility in severely paralysed patients.

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Figure 1: Principles of a brain–machine interface.
Figure 2: Neuronal activity during a reaching task.
Figure 3: Discrimination of spatiotemporal microstimulation patterns by owl monkeys.
Figure 4: The concept of a brain–machine–brain (BMBI) interface with artificial sensory feedback.
Figure 5: Neuronal responses in rat somatosensory cortex to passively applied stimuli versus active discrimination of the same stimuli.

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Acknowledgements

We thank N. Fitzsimmons for his assistance with designing figures for this manuscript.

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Authors and Affiliations

  1. Miguel A. L. Nicolelis and Mikhail A. Lebedev are at the Duke University Center for Neuroengineering and the Department of Neurobiology, Duke University, Durham, North Carolina 27710, USA.,

    Miguel A. L. Nicolelis & Mikhail A. Lebedev

  2. Miguel A. L. Nicolelis is also at the Edmond and Lily Safra International Institute of Neuroscience of Natal, Rua Professor Francisco Luciano de Oliveira, 2460, Candelária, Natal, Rio Grande do Norte 59066-060, Brazil and is a Fellow at the Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne, 1015 Lausanne, Switzerland.,

    Miguel A. L. Nicolelis

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Nicolelis, M., Lebedev, M. Principles of neural ensemble physiology underlying the operation of brain–machine interfaces. Nat Rev Neurosci 10, 530–540 (2009). https://doi.org/10.1038/nrn2653

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