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Restoring sensorimotor function through intracortical interfaces: progress and looming challenges

Key Points

  • Brain–machine interface (BMI) control of the kinematics of reaching has progressed dramatically, whereas BMI control of the hand and of the dynamics of movement is still quite limited.

  • Conveying somatosensory feedback is critical for BMIs to be clinically viable, but afferent interfaces are still rather primitive.

  • Biomimicry — that is, attempting to exploit or reproduce natural patterns of neuronal activity — may be an important design criterion.

  • Adaptation, the ability of the nervous system to adapt to novel motor and sensory mappings, is also likely to be crucial.

  • The lifespan of cortical interfaces is currently inadequate.

Abstract

The loss of a limb or paralysis resulting from spinal cord injury has devastating consequences on quality of life. One approach to restoring lost sensory and motor abilities in amputees and patients with tetraplegia is to supply them with implants that provide a direct interface with the CNS. Such brain–machine interfaces might enable a patient to exert voluntary control over a prosthetic or robotic limb or over the electrically induced contractions of paralysed muscles. A parallel interface could convey sensory information about the consequences of these movements back to the patient. Recent developments in the algorithms that decode motor intention from neuronal activity and in approaches to convey sensory feedback by electrically stimulating neurons, using biomimetic and adaptation-based approaches, have shown the promise of invasive interfaces with sensorimotor cortices, although substantial challenges remain.

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Figure 1: Idealized bidirectional brain–machine interface for prosthetic control.
Figure 2: Seven-DOF control of a prosthetic limb for reaching and grasping.
Figure 3: Offline electromyography-based predictions during flexion and extension isometric wrist torque.
Figure 4: Conveying information about contact location and pressure using a biomimetic approach.
Figure 5: Bidirectional interface based on learned associations.

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Acknowledgements

The authors gratefully thank J. Yau, H. Saal, A. Suminski and K. Otto for their comments on a previous version of the manuscript. The authors also thank G. Tabot for designing figure 1. S.J.B.is supported by US Defense Advanced Research Projects Agency (DARPA) contract #N66001-10-C-4056, US National Science Foundation (NSF) grant IOS-1150209 and US National Institutes of Health (NIH) grant 082865. L.E.M. is supported by grants from the US NIH (NS053603, NS048845) and the US NSF (0932263), with additional funding from the Chicago Community Trust through the Searle Program for Neurological Restoration.

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Glossary

Degree of freedom

(DOF). The number of signals required to control a device. The DOF is determined approximately by the number of parameters that defines its configuration.

Decoders

A set of (often linear) coefficients used to transform a large number of signals recorded from the brain into a small number of control signals. A decoder might also be used simply to classify the brain signals into two or more clusters that could be used to control the state of a limb.

Offline analysis

A test of decoder performance, typically using signals previously recorded from an able-bodied subject, enabling comparison of the decoder's 'predictions' with the actual movement-related signals.

Impedance

In electricity, the opposition to alternating current by an electric circuit. In limb movement, a measure of how much the limb resists motion when subjected to a force.

Redundant

A limb having more degrees of freedom (for example, muscles or joint rotations) than are minimally necessary to position and orient its end point. Redundancy conveys flexibility but also requires more complex control algorithms.

Actor–critic

A reinforcement learning approach that consists of having an 'actor' perform an action based on the state of the system and a 'critic' evaluate the consequences of that action. The probability of performing that action given the state is then modified based on the consequences.

Online control

Actual predictions made with a decoder in real-time, allowing the user to control a robotic limb or the motion of a cursor.

Ballistic

A preprogrammed movement that is sufficiently rapid that it cannot be modified by online sensory feedback.

Proprioception

The sense of the relative position and motion of parts of the body (particularly limbs) and of the effort deployed in movement.

Flutter

Low-frequency (5–50 Hz) oscillations.

Verisimilitude

In the context of sensory brain–machine interfaces, the similarity to naturally occurring percepts.

Percutaneous

Literally, 'by way of the skin'. In this context, an interface that penetrates the skin in order to convey signals to and from the nervous system.

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Bensmaia, S., Miller, L. Restoring sensorimotor function through intracortical interfaces: progress and looming challenges. Nat Rev Neurosci 15, 313–325 (2014). https://doi.org/10.1038/nrn3724

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