Brain-machine interfaces: an overview <s> Neuronal plasticity associated with BMI usage <s> A tool is an extension of the hand in both a physical and a perceptual sense. The presence of body schemata has been postulated as the basis of the perceptual assimilation of tool and hand. We trained macaque monkeys to retrieve distant objects using a rake, and neuronal activity was recorded in the caudal postcentral gyrus where the somatosensory and visual signals converge. There we found a large number of bimodal neurones which appeared to code the schema of the hand. During tool use, their visual receptive fields were altered to include the entire length of the rake or to cover the expanded accessible space. These findings may represent neural correlates of the modified schema of the hand in which the tool was incorporated. <s> BIB001 </s> Brain-machine interfaces: an overview <s> Neuronal plasticity associated with BMI usage <s> Monkeys can learn to directly control the movements of an artificial actuator by using a brain-machine interface (BMI) driven by the activity of a sample of cortical neurons. Eventually, they can do so without moving their limbs. Neuronal adaptations underlying the transition from control of the limb to control of the actuator are poorly understood. Here, we show that rapid modifications in neuronal representation of velocity of the hand and actuator occur in multiple cortical areas during the operation of a BMI. Initially, monkeys controlled the actuator by moving a hand-held pole. During this period, the BMI was trained to predict the actuator velocity. As the monkeys started using their cortical activity to control the actuator, the activity of individual neurons and neuronal populations became less representative of the animal's hand movements while representing the movements of the actuator. As a result of this adaptation, the animals could eventually stop moving their hands yet continue to control the actuator. These results show that, during BMI control, cortical ensembles represent behaviorally significant motor parameters, even if these are not associated with movements of the animal's own limb. <s> BIB002 </s> Brain-machine interfaces: an overview <s> Neuronal plasticity associated with BMI usage <s> Since the original demonstration that electrical activity generated by ensembles of cortical neurons can be employed directly to control a robotic manipulator, research on brain–machine interfaces (BMIs) has experienced an impressive growth. Today BMIs designed for both experimental and clinical studies can translate raw neuronal signals into motor commands that reproduce arm reaching and hand grasping movements in artificial actuators. Clearly, these developments hold promise for the restoration of limb mobility in paralyzed subjects. However, as we review here, before this goal can be reached several bottlenecks have to be passed. These include designing a fully implantable biocompatible recording device, further developing real-time computational algorithms, introducing a method for providing the brain with sensory feedback from the actuators, and designing and building artificial prostheses that can be controlled directly by brain-derived signals. By reaching these milestones, future BMIs will be able to drive and control revolutionary prostheses that feel and act like the human arm. <s> BIB003 </s> Brain-machine interfaces: an overview <s> Neuronal plasticity associated with BMI usage <s> Background ::: During planning and execution of reaching movements, the activity of cortical motor neurons is modulated by a diversity of motor, sensory, and cognitive signals. Brain-machine interfaces (BMIs) extract part of these modulations to directly control artificial actuators. However, cortical modulations that emerge in the novel context of operating the BMI are poorly understood. ::: Methodology/Principal Findings ::: Here we analyzed the changes in neuronal modulations that occurred in different cortical motor areas as monkeys learned to use a BMI to control reaching movements. Using spike-train analysis methods we demonstrate that the modulations of the firing-rates of cortical neurons increased abruptly after the monkeys started operating the BMI. Regression analysis revealed that these enhanced modulations were not correlated with the kinematics of the movement. The initial enhancement in firing rate modulations declined gradually with subsequent training in parallel with the improvement in behavioral performance. ::: Conclusions/Significance ::: We conclude that the enhanced modulations are related to computational tasks that are significant especially in novel motor contexts. Although the function and neuronal mechanism of the enhanced cortical modulations are open for further inquiries, we discuss their potential role in processing execution errors and representing corrective or explorative activity. These representations are expected to contribute to the formation of internal models of the external actuator and their decoding may facilitate BMI improvement. <s> BIB004 </s> Brain-machine interfaces: an overview <s> Neuronal plasticity associated with BMI usage <s> Imagine living in a world where people use their computers, drive their cars, and communicate with one another simply by thinking. In this stunning and inspiring work, Duke University neuroscientist Miguel Nicolelis shares his revolutionary insights into how the brain creates thought and the human sense of selfand how this might be augmented by machines, so that the entire universe will be within our reach.Beyond Boundaries draws on Nicolelis's ground-breaking research with monkeys that he taught to control the movements of a robot located halfway around the globe by using brain signals alone. Nicolelis's work with primates has uncovered a new method for capturing brain functionby recording rich neuronal symphonies rather than the activity of single neurons. His lab is now paving the way for a new treatment for Parkinson's, silk-thin exoskeletons to grant mobility to the paralyzed, and breathtaking leaps in space exploration, global communication, manufacturing, and more. Beyond Boundaries promises to reshape our concept of the technological future, to a world filled with promise and hope. <s> BIB005 </s> Brain-machine interfaces: an overview <s> Neuronal plasticity associated with BMI usage <s> Brain-computer interfaces (BCIs) provide a defined link between neural activity and devices, allowing a detailed study of the neural adaptive responses generating behavioral output. We trained monkeys to perform two-dimensional center-out movements of a computer cursor using a BCI. We then applied a perturbation by randomly selecting a subset of the recorded units and rotating their directional contributions to cursor movement by a consistent angle. Globally, this perturbation mimics a visuomotor transformation, and in the first part of this article we characterize the psychophysical indications of motor adaptation and compare them with known results from adaptation of natural reaching movements. Locally, however, only a subset of the neurons in the population actually contributes to error, allowing us to probe for signatures of neural adaptation that might be specific to the subset of neurons we perturbed. One compensation strategy would be to selectively adapt the subset of cells responsible for the err... <s> BIB006 </s> Brain-machine interfaces: an overview <s> Neuronal plasticity associated with BMI usage <s> Brain-machine interfaces (BMIs) are artificial systems that aim to restore sensation and movement to paralyzed patients. So far, BMIs have enabled only one arm to be moved at a time. Control of bimanual arm movements remains a major challenge. We have developed and tested a bimanual BMI that enables rhesus monkeys to control two avatar arms simultaneously. The bimanual BMI was based on the extracellular activity of 374 to 497 neurons recorded from several frontal and parietal cortical areas of both cerebral hemispheres. Cortical activity was transformed into movements of the two arms with a decoding algorithm called a fifth-order unscented Kalman filter (UKF). The UKF was trained either during a manual task performed with two joysticks or by having the monkeys passively observe the movements of avatar arms. Most cortical neurons changed their modulation patterns when both arms were engaged simultaneously. Representing the two arms jointly in a single UKF decoder resulted in improved decoding performance compared with using separate decoders for each arm. As the animals' performance in bimanual BMI control improved over time, we observed widespread plasticity in frontal and parietal cortical areas. Neuronal representation of the avatar and reach targets was enhanced with learning, whereas pairwise correlations between neurons initially increased and then decreased. These results suggest that cortical networks may assimilate the two avatar arms through BMI control. These findings should help in the design of more sophisticated BMIs capable of enabling bimanual motor control in human patients. <s> BIB007
Many studies have provided evidence of brain plasticity associated with learning to control a BMI. It has been suggested that such plasticity could eventually assimilate prosthetic limbs in the brain representation of the body BIB003 BIB005 . BMI control of external devices has much in common with using a tool. In a famous experiment on cortical plasticity associated with tool usage, Iriki and his colleagues trained monkeys to reach toward distant objects with a rake BIB001 . After the monkeys practiced with this tool, neurons in posterior parietal cortex acquired visual receptive fields extending along the length of the rake. Long-term usage of BMIs could produce similar brain remapping, and several papers have already provided evidence for such plasticity. Neurons engaged in BMI control exhibit stronger modulations BIB004 , changes in correlation with each other [6, BIB007 , and changes in directional tuning BIB002 . Additionally, cortical neurons have been shown to adapt to rotational transformation applied to a subset of neurons engaged in BMI control BIB006 . 