2,907 research outputs found

    Brain-machine interfaces for rehabilitation in stroke: A review

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    BACKGROUND: Motor paralysis after stroke has devastating consequences for the patients, families and caregivers. Although therapies have improved in the recent years, traditional rehabilitation still fails in patients with severe paralysis. Brain-machine interfaces (BMI) have emerged as a promising tool to guide motor rehabilitation interventions as they can be applied to patients with no residual movement. OBJECTIVE: This paper reviews the efficiency of BMI technologies to facilitate neuroplasticity and motor recovery after stroke. METHODS: We provide an overview of the existing rehabilitation therapies for stroke, the rationale behind the use of BMIs for motor rehabilitation, the current state of the art and the results achieved so far with BMI-based interventions, as well as the future perspectives of neural-machine interfaces. RESULTS: Since the first pilot study by Buch and colleagues in 2008, several controlled clinical studies have been conducted, demonstrating the efficacy of BMIs to facilitate functional recovery in completely paralyzed stroke patients with noninvasive technologies such as the electroencephalogram (EEG). CONCLUSIONS: Despite encouraging results, motor rehabilitation based on BMIs is still in a preliminary stage, and further improvements are required to boost its efficacy. Invasive and hybrid approaches are promising and might set the stage for the next generation of stroke rehabilitation therapies.This study was funded by the Bundesministerium für Bildung und Forschung BMBF MOTORBIC (FKZ13GW0053)andAMORSA(FKZ16SV7754), the Deutsche Forschungsgemeinschaft (DFG), the fortüne-Program of the University of Tübingen (2422-0-0 and 2452-0-0), and the Basque GovernmentScienceProgram(EXOTEK:KK2016/00083). NIL was supported by the Basque Government’s scholarship for predoctoral students

    PRESENCE: A human-inspired architecture for speech-based human-machine interaction

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    Recent years have seen steady improvements in the quality and performance of speech-based human-machine interaction driven by a significant convergence in the methods and techniques employed. However, the quantity of training data required to improve state-of-the-art systems seems to be growing exponentially and performance appears to be asymptotic to a level that may be inadequate for many real-world applications. This suggests that there may be a fundamental flaw in the underlying architecture of contemporary systems, as well as a failure to capitalize on the combinatorial properties of human spoken language. This paper addresses these issues and presents a novel architecture for speech-based human-machine interaction inspired by recent findings in the neurobiology of living systems. Called PRESENCE-"PREdictive SENsorimotor Control and Emulation" - this new architecture blurs the distinction between the core components of a traditional spoken language dialogue system and instead focuses on a recursive hierarchical feedback control structure. Cooperative and communicative behavior emerges as a by-product of an architecture that is founded on a model of interaction in which the system has in mind the needs and intentions of a user and a user has in mind the needs and intentions of the system

    Predicting voluntary movements from motor cortical activity with neuromorphic hardware

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    This document is the Accepted Manuscript version of the following article: A. Lungu, A. Riehle, M. P. Nawrot and M. Schmuker, "Predicting voluntary movements from motor cortical activity with neuromorphic hardware," in IBM Journal of Research and Development, Vol. 61, no. 2/3, pp. 5:1-5:12, March-May 1 2017. The version of record is available online at doi: 10.1147/JRD.2017.2656063. © 2017 by International Business Machines Corporation. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Neurons in the mammalian motor cortices 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 individual neuronal activity 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.Peer reviewe

    Toward More Versatile and Intuitive Cortical Brain–Machine Interfaces

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    Brain–machine interfaces have great potential for the development of neuroprosthetic applications to assist patients suffering from brain injury or neurodegenerative disease. One type of brain–machine interface is a cortical motor prosthetic, which is used to assist paralyzed subjects. Motor prosthetics to date have typically used the motor cortex as a source of neural signals for controlling external devices. The review will focus on several new topics in the arena of cortical prosthetics. These include using: recordings from cortical areas outside motor cortex; local field potentials as a source of recorded signals; somatosensory feedback for more dexterous control of robotics; and new decoding methods that work in concert to form an ecology of decode algorithms. These new advances promise to greatly accelerate the applicability and ease of operation of motor prosthetics

    Investigating motor skill in closed-loop myoelectric hand prostheses:Through speed-accuracy trade-offs

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    Human Ipsilateral Motor Physiology and Neuroprosthetic Applications in Chronic Stroke

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    Improving the recovery of lost motor function in hemiplegic chronic stroke survivors is a critical need to improve the lives of these patients. Over the last several decades, neuroprosthetic systems have emerged as novel tools with the potential to restore function in a variety of patient populations. While traditional neuroprosthetics have focused on using neural activity contralateral to a moving limb for device control, an alternative control signal may be necessary to develop brain-computer interface (BCI) systems in stroke survivors that suffer damage to the cortical hemisphere contralateral to the affected limb. While movement-related neural activity also occurs in the hemisphere ipsilateral to a moving limb, it is uncertain if these signals can be used within BCI systems. This dissertation examines the motor activity ipsilateral to a moving limb and the potential use of these signals for neuroprosthetic applications in chronic stroke survivors. Patients performed three-dimensional (3D) reaching movements with the arm ipsilateral to an electrocorticography (ECoG) array in order to assess the extent of kinematic information that can be decoded from the cortex ipsilateral to a moving limb. Additionally, patients performed the same task with the arm contralateral to the same ECoG arrays, allowing us to compare the neural representations of contralateral and ipsilateral limb movements. While spectral power changes related to ipsilateral arm movements begin later and are lower in amplitude than power changes related to contralateral arm movements, 3D kinematics from both contralateral and ipsilateral arm trajectories can be decoded with similar accuracies. The ability to decode movement kinematics from the ipsilateral cortical hemisphere demonstrates the potential to use these signals within BCI applications for controlling multiple degrees of freedom. Next we examined the relationship between electrode invasiveness and signal quality. The ability to decode movement kinematics from neural activity was significantly decreased in simulated electroencephalography (EEG) signals relative to ECoG signals, indicating that invasive signals would be necessary to implement BCI systems with multiple degrees of freedom. For ECoG signals, the human dura also causes a significant decrease in signal quality when electrodes with small spatial sizes are used. This tradeoff between signal quality and electrode invasiveness should therefore be taken into account when designing ECoG BCI systems. Finally, chronic stroke survivors used activity associated with affected hand motor intentions, recorded from their unaffected hemisphere using EEG, to control simple BCI systems. This demonstrates that motor signals from the ipsilateral hemisphere are viable for BCI applications, not only in motor-intact patients, but also in chronic stroke survivors. Taken together, these experiments provide initial demonstrations that it is possible to develop BCI systems using the unaffected hemisphere in stroke survivors with multiple degrees of freedom. Further development of these BCI systems may eventually lead to improving function for a significant population of patients

    Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography

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    One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive PNS-Machine Interfaces was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PNS-Machine Interface (PMI) has been selected to denote human-machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the peripheral nervous system (PNS) in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it

    Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates

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    Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations

    A review of techniques for detection of movement intention using movement-related cortical potentials

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    The movement-related cortical potential (MRCP) is a low-frequency negative shift in the electroencephalography (EEG) recording that takes place about 2 seconds prior to voluntary movement production. MRCP replicates the cortical processes employed in planning and preparation of movement. In this study, we recapitulate the features such as signal’s acquisition, processing, and enhancement and different electrode montages used for EEG data recoding from different studies that used MRCPs to predict the upcoming real or imaginary movement. An authentic identification of human movement intention, accompanying the knowledge of the limb engaged in the performance and its direction of movement, has a potential implication in the control of external devices. This information could be helpful in development of a proficient patient-driven rehabilitation tool based on brain-computer interfaces (BCIs). Such a BCI paradigm with shorter response time appears more natural to the amputees and can also induce plasticity in brain. Along with different training schedules, this can lead to restoration of motor control in stroke patients
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