Restoring sensorimotor function through intracortical interfaces: progress and looming challenges

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.

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.

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.

Department of Organismal Biology and Anatomy, and Committee on Computational Neuroscience, University of Chicago, Chicago, 60637, Illinois, USA

Department of Physical Medicine and Rehabilitation, and Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, 60611, Illinois, USA

Department of Biomedical Engineering, Northwestern University, Evanston, 60208, Illinois, USA

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

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.

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.

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.

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.

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.

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

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

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

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

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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