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. Access through your institution Buy or subscribe This is a preview of subscription content, access via your

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* Learn about institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS BIOMIMETIC COMPUTER-TO-BRAIN COMMUNICATION ENHANCING NATURALISTIC

TOUCH SENSATIONS VIA PERIPHERAL NERVE STIMULATION Article Open access 20 February 2024 CONTINUOUS NEURAL CONTROL OF A BIONIC LIMB RESTORES BIOMIMETIC GAIT AFTER AMPUTATION Article Open

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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. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Organismal Biology and Anatomy, and Committee on

Computational Neuroscience, University of Chicago, Chicago, 60637, Illinois, USA Sliman J. Bensmaia * Department of Physical Medicine and Rehabilitation, and Department of Physiology,

Feinberg School of Medicine, Northwestern University, Chicago, 60611, Illinois, USA Lee E. Miller * Department of Biomedical Engineering, Northwestern University, Evanston, 60208, Illinois,

USA Lee E. Miller Authors * Sliman J. Bensmaia View author publications You can also search for this author inPubMed Google Scholar * Lee E. Miller View author publications You can also

search for this author inPubMed Google Scholar CORRESPONDING AUTHOR Correspondence to Lee E. Miller. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing financial

interests. POWERPOINT SLIDES POWERPOINT SLIDE FOR FIG. 1 POWERPOINT SLIDE FOR FIG. 2 POWERPOINT SLIDE FOR FIG. 3 POWERPOINT SLIDE FOR FIG. 4 POWERPOINT SLIDE FOR FIG. 5 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. RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS

ARTICLE Bensmaia, S., Miller, L. Restoring sensorimotor function through intracortical interfaces: progress and looming challenges. _Nat Rev Neurosci_ 15, 313–325 (2014).

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