Two people with long-standing tetraplegia use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. John Donoghue and colleagues have previously demonstrated that people with tetraplegia can learn to use neural signals from the motor cortex to control a computer cursor. Work from another lab has also shown that monkeys can learn to use such signals to feed themselves with a robotic arm. Now, Donoghue and colleagues have advanced the technology to a level at which two people with long-standing paralysis — a 58-year-old woman and a 66-year-old man — are able to use a neural interface to direct a robotic arm to reach for and grasp objects. One subject was able to learn to pick up and drink from a bottle using a device implanted 5 years earlier, demonstrating not only that subjects can use the brain–machine interface, but also that it has potential longevity. Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system1,2,3,4,5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices6,7,8. Able-bodied monkeys have used a neural interface system to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.

/pdf/reach-and-grasp-by-people-with-tetraplegia-using-a-neurally-u1xyn3on0d.pdf

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

In this review, we consider the potential functional role of beta-band oscillations, which at present is not yet well understood. We discuss evidence from recent studies on top-down mechanisms involved in cognitive processing, on the motor system and on the pathophysiology of movement disorders that suggest a unifying hypothesis: beta-band activity seems related to the maintenance of the current sensorimotor or cognitive state. We hypothesize that beta oscillations and/or coupling in the beta-band are expressed more strongly if the maintenance of the status quo is intended or predicted, than if a change is expected. Moreover, we suggest that pathological enhancement of beta-band activity is likely to result in an abnormal persistence of the status quo and a deterioration of flexible behavioural and cognitive control.

Beta-band oscillations--signalling the status quo?

The journal of neuroscience : the official journal of the Society for Neuroscience.

https://www.lehigh.edu/~inbios21/PDF/Fall2007/Brain-Machine_Interface.pdf

Brain–machine interfaces: past, present and future

저작근 근전도에 관한 정상교합자와 ii급 부정교합자의 비교 연구

In an embodiment, the invention relates to neural prosthetic devices in which control signals are based on the cognitive activity of the prosthetic user. The control signals may be used to control an array of external devices, such as prosthetics, computer systems, and speech synthesizers. Data obtained from monkeys' movement intentions were recorded, decoded with a computer algorithm, and used to position cursors on a computer screen. Not only the intended goals, but also the value of the reward the animals expected to receive at the end of each trial, were decoded from the recordings. The results indicate that brain activity related to cognitive variables can be a viable source of signals for the control of a cognitive-based neural prosthetic.

https://science.sciencemag.org/content/sci/305/5681/258.full.pdf

Cognitive control signals for neural prosthetics

Cortical Local Field Potential Encodes Movement Intentions in the Posterior Parietal Cortex

To perform grasping movements, the hand is shaped according to the form of the target object and the intended manipulation, which in turn depends on the context of the action. The anterior intraparietal cortex (AIP) is strongly involved in the sensorimotor transformation of grasping movements, but the extent to which it encodes context-specific information for hand grasping is unclear. To explore this issue, we recorded 571 single-units in AIP of two macaques during a delayed grasping task, in which animals were instructed by an external context cue (LED) to perform power or precision grips on a handle that was presented in various orientations. While 55% of the recorded neurons encoded the object orientation from the cue epoch on, the number of cells encoding the grip type increased from 25% during the cue epoch to 58% during movement execution. Furthermore, a classification of cells according to the time of their tuning onset revealed differences in the function and anatomical location of early- versus late-tuned cells. In a cue separation task, when the object was presented first, neurons representing power or precision grips were activated simultaneously until the actual grip type was instructed. In contrast, when the grasp type instruction was presented before the object, type information was only weakly represented in AIP, but was strongly encoded after the grasp target was revealed. We conclude that AIP encodes context specific hand grasping movements to perceived objects, but in the absence of a grasp target, the encoding of context information is weak.

https://www.jneurosci.org/content/jneuro/29/20/6436.full.pdf

Context-Specific Grasp Movement Representation in the Macaque Anterior Intraparietal Area

The selection of visual stimuli as a target for a motor action may depend on external as well as internal variables. The parietal reach region (PRR) in the posterior parietal cortex plays an important role in the transformation of visual information into reach movement plans. We asked how neurons in PRR of macaque monkeys reflect the decision process of selecting one of two visual stimuli as a target for a reach movement. Spiking activity was recorded while the animal performed a free-choice task with one target presented in the preferred direction and the other in the off direction of the cell. Stimulus-onset asynchrony (SOA) was adjusted to ensure that both targets were selected equally often and the amount of reward was fixed. Neural activity in PRR was action specific for arm reaching and reflected the timing of the SOA as well as the selection of reach targets. In individual trials, activity was strongly linked to the choice of the animal, and, for the majority of cells, target selections could be predicted from activity in the stimulation or planning period, i.e., before the movement started. Many neurons were gain modulated by the fixation position, but gain modulation did not influence the target selection process directly. Finally, it was found that target selection for saccade movements was only weakly represented in PRR. These findings suggest that PRR is involved in decision making for reach movements and that separate cortical networks exist for target selection of different types of action.

https://www.jneurosci.org/content/jneuro/27/8/2001.full.pdf

Target Selection Signals for Arm Reaching in the Posterior Parietal Cortex

Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity.

/pdf/neural-population-dynamics-during-reaching-are-better-2koswdoed0.pdf

Hansjörg Scherberger

Papers

Cognitive control signals for neural prosthetics

Cortical Local Field Potential Encodes Movement Intentions in the Posterior Parietal Cortex

Context-Specific Grasp Movement Representation in the Macaque Anterior Intraparietal Area

Target Selection Signals for Arm Reaching in the Posterior Parietal Cortex

Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning