Direct control of paralysed muscles by cortical neurons

Moritz, Chet T.; Perlmutter, Steve I.; Fetz, Eberhard E.

doi:10.1038/nature07418

Cited by 545 publications

(436 citation statements)

References 30 publications

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“…Investigators have repeatedly documented that task performance typically increases over the course of practice (11)(12)(13)(14)(15), which is indicative of a learning process taking place in the brain. In many cases, human BCI users have anecdotally reported transitioning from a very deliberate, cognitive approach to nearly automatic execution.…”

mentioning

confidence: 99%

Distributed cortical adaptation during learning of a brain–computer interface task

Wander¹,

Blakely²,

Miller

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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The majority of subjects who attempt to learn control of a braincomputer interface (BCI) can do so with adequate training. Much like when one learns to type or ride a bicycle, BCI users report transitioning from a deliberate, cognitively focused mindset to near automatic control as training progresses. What are the neural correlates of this process of BCI skill acquisition? Seven subjects were implanted with electrocorticography (ECoG) electrodes and had multiple opportunities to practice a 1D BCI task. As subjects became proficient, strong initial task-related activation was followed by lessening of activation in prefrontal cortex, premotor cortex, and posterior parietal cortex, areas that have previously been implicated in the cognitive phase of motor sequence learning and abstract task learning. These results demonstrate that, although the use of a BCI only requires modulation of a local population of neurons, a distributed network of cortical areas is involved in the acquisition of BCI proficiency.brain-machine interface | motor learning | plasticity | electrophysiology | high gamma

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mentioning

confidence: 99%

Distributed cortical adaptation during learning of a brain–computer interface task

Wander¹,

Blakely²,

Miller

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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133

134

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“…BMIs are also under development that could restore voluntary limb movement to a spinal cord-injured patient. Muscle activity (EMG) predictions have been used to control muscles directly via electrical stimulation to produce isometric torque about the wrist (Moritz et al 2008;Pohlmeyer et al 2007aPohlmeyer et al , 2009). More recently, a similar approach was used to restore the ability to grasp objects (Oby et al 2010).…”

mentioning

confidence: 99%

Motor cortical prediction of EMG: evidence that a kinetic brain-machine interface may be robust across altered movement dynamics

Cherian

Krucoff

Miller

2011

Journal of Neurophysiology

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Cherian A, Krucoff MO, Miller LE. Motor cortical prediction of EMG: evidence that a kinetic brain-machine interface may be robust across altered movement dynamics. J Neurophysiol 106: 564 -575, 2011. First published May 11, 2011 doi:10.1152/jn.00553.2010.-During typical movements, signals related to both the kinematics and kinetics of movement are mutually correlated, and each is correlated to some extent with the discharge of neurons in the primary motor cortex (M1). However, it is well known, if not always appreciated, that causality cannot be inferred from correlations. Although these mutual correlations persist, their nature changes with changing postural or dynamical conditions. Under changing conditions, only signals directly controlled by M1 can be expected to maintain a stable relationship with its discharge. If one were to rely on noncausal correlations for a brain-machine interface, its generalization across conditions would likely suffer. We examined this effect, using multielectrode recordings in M1 as input to linear decoders of both end point kinematics (position and velocity) and proximal limb myoelectric signals (EMG) during reaching. We tested these decoders across tasks that altered either the posture of the limb or the end point forces encountered during movement. Within any given task, the accuracy of the kinematic predictions tended to be somewhat better than the EMG predictions. However, when we used the decoders developed under one task condition to predict the signals recorded under different postural or dynamical conditions, only the EMG decoders consistently generalized well. Our results support the view that M1 discharge is more closely related to kinetic variables like EMG than it is to limb kinematics. These results suggest that brain-machine interface applications using M1 to control kinetic variables may prove to be more successful than the more standard kinematic approach.electromyogram; force field; limb movement; posture REACHING IS a deceptively simple action. We are typically aware only of the relatively straight, smoothly accelerating movement of the hand toward a target.

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“…For predicting the single trial movement direction, however, we interpreted the association neurons' output spike count within a fixed-width time window. In future biomedical applications, it might become feasible to directly use the spiking output to activate body muscles [42], potentially obviating the need for counting spikes in a time window to control an actuator. Other potential biomedical applications of spiking neuromorphic computing include restoring spinal cord function [43] and the application in closed-loop deep brain stimulation devices [44], [45].…”

Section: Discussionmentioning

confidence: 99%

Predicting voluntary movements from motor cortical activity with neuromorphic hardware

Lungu¹,

Riehle²,

Nawrot³

et al. 2017

IBM J. Res. & Dev.

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Neurons in mammalian motor cortex encode physical parameters of voluntary movements during planning and execution of a motor task. Brain-machine interfaces can decode limb movements from the activity of these neurons in real time. The future goal is to control prosthetic devices in severely paralyzed patients or to restore communication if the ability to speak or make gestures is lost. Here, we implemented a spiking neural network that decodes movement intentions from the activity of individual neurons recorded in the motor cortex of a monkey. The network runs on neuromorphic hardware and performs its computations in a purely spike-based fashion. It incorporates an insect-brain-inspired, three-layer architecture with 176 neurons. Cortical signals are filtered using lateral inhibition, and the network is trained in a supervised fashion to predict two opposing directions of the monkey's arm reaching movement before the movement is carried out. Our network operates on the actual spikes that have been emitted by motor cortical neurons, without the need to construct intermediate non-spiking representations. Using a pseudo-population of 12 manually-selected neurons, it reliably predicts the movement direction with an accuracy of 89.32 % on unseen data after only 100 training trials. Our results provide a proof of concept for the first-time use of a neuromorphic device for decoding movement intentions.

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Direct control of paralysed muscles by cortical neurons

Cited by 545 publications

References 30 publications

Distributed cortical adaptation during learning of a brain–computer interface task

Distributed cortical adaptation during learning of a brain–computer interface task

Motor cortical prediction of EMG: evidence that a kinetic brain-machine interface may be robust across altered movement dynamics

Predicting voluntary movements from motor cortical activity with neuromorphic hardware

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