Neural and Cognitive Engineering Group

Torrejón del Rey, Spain

Neural and Cognitive Engineering Group

Torrejón del Rey, Spain
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Gallego J.A.,Neural and Cognitive Engineering Group | Gallego J.A.,Northwestern University | Dideriksen J.L.,University of Gottingen | Dideriksen J.L.,University of Aalborg | And 11 more authors.
Journal of Neuroscience | Year: 2015

The pathophysiology of essential tremor (ET), the most common movement disorder, is not fully understood. We investigated which factors determine the variability in the phase difference between neural drives to antagonist muscles, a long-standing observation yet unexplained. We used a computational model to simulate the effects of different levels of voluntary and tremulous synaptic input to antagonistic motoneuron pools on the tremor. We compared these simulations to data from 11 human ET patients. In both analyses, the neural drive to muscle was represented as the pooled spike trains of several motor units, which provides an accurate representation of the common synaptic input to motoneurons. The simulations showed that, for each voluntary input level, the phase difference between neural drives to antagonist muscles is determined by the relative strength of the supraspinal tremor input to the motoneuron pools. In addition, when the supraspinal tremor input to one muscle was weak or absent, Ia afferents provided significant common tremor input due to passive stretch. The simulations predicted that without a voluntary drive (rest tremor) the neural drives would be more likely in phase, while a concurrent voluntary input (postural tremor) would lead more frequently to an out-of-phase pattern. The experimental results matched these predictions, showing a significant change in phase difference between postural and rest tremor. They also indicated that the common tremor input is always shared by the antagonistic motoneuron pools, in agreement with the simulations. Our results highlight that the interplay between supraspinal input and spinal afferents is relevant for tremor generation. © 2015 the authors.


Velasco M.A.,Neural and Cognitive Engineering Group | Clemotte A.,Neural and Cognitive Engineering Group | Raya R.,University of San Pablo - CEU | Ceres R.,Cajal Institute Csic Av | Rocon E.,Neural and Cognitive Engineering Group
International Journal of Human Computer Studies | Year: 2017

This paper presents an experiment to validate a head-mounted inertial interface for human-computer interaction (HCI) developed for people with cerebral palsy (CP). The method is based on Fitts's law, an empirical model of human motor performance for aimed movements. Head motion is recorded in a series of goal-crossing tasks and a regression model of the movement time (MT) is estimated for each user. Values of R2 above 0.9 are indicators of a strong correlation of those motion patterns with the linear model proposed by Fitts. The analysis of MT confirmed that head movements of users without disability follow Fitts's law and showed that 3 users with CP (MACS IV and V) had the same behavior. There was a weaker correlation (R2=0.839) for one individual with cervical dystonia and ballistic movements and no correlation for two users with cervical hypotonia and dyskinetic CP. Results show the impact of ballistic movements and poor postural control in computer interaction. They also provide the foundation for new interaction techniques to develop a universal computer interface for motor impaired users. © 2017 Elsevier Ltd


Ethier C.,Northwestern University | Gallego J.A.,Northwestern University | Gallego J.A.,Neural and Cognitive Engineering Group | Miller L.E.,Northwestern University
Current Opinion in Neurobiology | Year: 2015

There is mounting evidence that appropriately timed neuromuscular stimulation can induce neural plasticity and generate functional recovery from motor disorders. This review addresses the idea that coordinating stimulation with a patient's voluntary effort might further enhance neurorehabilitation. Studies in cell cultures and behaving animals have delineated the rules underlying neural plasticity when single neurons are used as triggers. However, the rules governing more complex stimuli and larger networks are less well understood. We argue that functional recovery might be optimized if stimulation were modulated by a brain machine interface, to match the details of the patient's voluntary intent. The potential of this novel approach highlights the need for a better understanding of the complex rules underlying this form of plasticity. © 2015 Elsevier Ltd.


Monge-Pereira E.,Rey Juan Carlos University | Ibanez-Pereda J.,University College London | Alguacil-Diego I.M.,Rey Juan Carlos University | Serrano J.I.,Neural and Cognitive Engineering Group | And 2 more authors.
PM and R | Year: 2016

Background: Brain-computer interface (BCI) systems have been suggested as a promising tool for neurorehabilitation. However, to date, there is a lack of homogeneous findings. Furthermore, no systematic reviews have analyzed the degree of validation of these interventions for upper limb (UL) motor rehabilitation poststroke. Objectives: The study aims were to compile all available studies that assess an UL intervention based on an electroencephalography (EEG) BCI system in stroke; to analyze the methodological quality of the studies retrieved; and to determine the effects of these interventions on the improvement of motor abilities. Type: This was a systematic review. Literature Survey: Searches were conducted in PubMed, PEDro, Embase, Cumulative Index to Nursing and Allied Health, Web of Science, and Cochrane Central Register of Controlled Trial from inception to September 30, 2015. Methodology: This systematic review compiles all available studies that assess a UL intervention based on an EEG-BCI system in patients with stroke, analyzing their methodological quality using the Critical Review Form for Quantitative Studies, and determining the grade of recommendation of these interventions for improving motor abilities as established by the Oxford Centre for Evidence-based Medicine. The articles were selected according to the following criteria: studies evaluating an EEG-based BCI intervention; studies including patients with a stroke and hemiplegia, regardless of lesion origin or temporal evolution; interventions using an EEG-based brain-computer interface to restore functional abilities of the affected UL, regardless of the interface used or its combination with other therapies; and studies using validated tools to evaluate motor function. Synthesis: After the literature search, 13 articles were included in this review: 4 studies were randomized controlled trials; 1 study was a controlled study; 4 studies were case series studies; and 4 studies were case reports. The methodological quality of the included papers ranged from 6 to 15, and the level of evidence varied from 1b to 5. The articles included in this review involved a total of 141 stroke patients. Conclusions: This systematic review suggests that BCI interventions may be a promising rehabilitation approach in subjects with stroke. Level of Evidence: To be determined. © 2017 American Academy of Physical Medicine and Rehabilitation.


Bayon C.,Neural and Cognitive Engineering Group | Lerma S.,Nino Jesus Hospital | Frizera A.,Federal University of Espirito Santo | Rocon E.,Neural and Cognitive Engineering Group
Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics | Year: 2016

Cerebral Palsy (CP) is the most common cause of permanent serious physical disability in childhood. New strategies are needed to help promote, maintain, and rehabilitate the functional capacity of children with severe level of impairment. Overground walking rehabilitation devices appear as an alternative treatment for improving gait performance as well as training natural gait patterns among this population. The main objective of this work is to present a Human-Robot interaction strategy for overground rehabilitation to support novel robotic-based therapies for CP rehabilitation. This strategy is implemented in a new robotic platform named CPWalker. In our approach, legs' kinematics information obtained from a Laser Range Finder (LRF) sensor is used to detect the user's locomotion intentions and drive the robotic platform. The controller continuously adjust robot's velocity to human velocity achieving an adequate robot motion that assists the locomotion at each step. During a preliminary validation we observed that our strategy is able to fast adapt to patients and provide them a stable gait pattern at different speeds. As a result, the proposed controller is able to provide a natural interface between the robotic-platform and the patient. © 2016 IEEE.


Koutsou A.D.,Cajal Institute | Moreno J.C.,Cajal Institute | Del Ama A.J.,National Hospital for Spinal Cord Injury | Rocon E.,Neural and Cognitive Engineering Group | Pons J.L.,Cajal Institute
Journal of NeuroEngineering and Rehabilitation | Year: 2016

Non-invasive neuroprosthetic (NP) technologies for movement compensation and rehabilitation remain with challenges for their clinical application. Two of those major challenges are selective activation of muscles and fatigue management. This review discusses how electrode arrays improve the efficiency and selectivity of functional electrical stimulation (FES) applied via transcutaneous electrodes. In this paper we review the principles and achievements during the last decade on techniques for artificial motor unit recruitment to improve the selective activation of muscles. We review the key factors affecting the outcome of muscle force production via multi-pad transcutaneous electrical stimulation and discuss how stimulation parameters can be set to optimize external activation of body segments. A detailed review of existing electrode array systems proposed by different research teams is also provided. Furthermore, a review of the targeted applications of existing electrode arrays for control of upper and lower limb NPs is provided. Eventually, last section demonstrates the potential of electrode arrays to overcome the major challenges of NPs for compensation and rehabilitation of patient-specific impairments. © 2016 The Author(s).


Ibanez J.,Cajal Institute | Serrano J.I.,Neural and Cognitive Engineering Group | del Castillo M.D.,Neural and Cognitive Engineering Group | Minguez J.,Aragon Institute of Engineering Research | Pons J.L.,Cajal Institute
Medical and Biological Engineering and Computing | Year: 2015

The extent to which the electroencephalographic activity allows the characterization of movements with the upper limb is an open question. This paper describes the design and validation of a classifier of upper-limb analytical movements based on electroencephalographic activity extracted from intervals preceding self-initiated movement tasks. Features selected for the classification are subject specific and associated with the movement tasks. Further tests are performed to reject the hypothesis that other information different from the task-related cortical activity is being used by the classifiers. Six healthy subjects were measured performing self-initiated upper-limb analytical movements. A Bayesian classifier was used to classify among seven different kinds of movements. Features considered covered the alpha and beta bands. A genetic algorithm was used to optimally select a subset of features for the classification. An average accuracy of 62.9 ± 7.5 % was reached, which was above the baseline level observed with the proposed methodology (30.2 ± 4.3 %). The study shows how the electroencephalography carries information about the type of analytical movement performed with the upper limb and how it can be decoded before the movement begins. In neurorehabilitation environments, this information could be used for monitoring and assisting purposes. © 2015, International Federation for Medical and Biological Engineering.


PubMed | Cajal Institute, Neural and Cognitive Engineering group and National Hospital for Spinal Cord Injury
Type: Journal Article | Journal: Journal of neuroengineering and rehabilitation | Year: 2016

Non-invasive neuroprosthetic (NP) technologies for movement compensation and rehabilitation remain with challenges for their clinical application. Two of those major challenges are selective activation of muscles and fatigue management. This review discusses how electrode arrays improve the efficiency and selectivity of functional electrical stimulation (FES) applied via transcutaneous electrodes. In this paper we review the principles and achievements during the last decade on techniques for artificial motor unit recruitment to improve the selective activation of muscles. We review the key factors affecting the outcome of muscle force production via multi-pad transcutaneous electrical stimulation and discuss how stimulation parameters can be set to optimize external activation of body segments. A detailed review of existing electrode array systems proposed by different research teams is also provided. Furthermore, a review of the targeted applications of existing electrode arrays for control of upper and lower limb NPs is provided. Eventually, last section demonstrates the potential of electrode arrays to overcome the major challenges of NPs for compensation and rehabilitation of patient-specific impairments.


PubMed | Institute Biomecanica Of Valencia, Neural and Cognitive Engineering group and Hospital Infantil Universitario Nino Jesus
Type: Journal Article | Journal: Journal of neuroengineering and rehabilitation | Year: 2016

Cerebral Palsy (CP) is a disorder of posture and movement due to a defect in the immature brain. The use of robotic devices as alternative treatment to improve the gait function in patients with CP has increased. Nevertheless, current gait trainers are focused on controlling complete joint trajectories, avoiding postural control and the adaptation of the therapy to a specific patient. This paper presents the applicability of a new robotic platform called CPWalker in children with spastic diplegia.CPWalker consists of a smart walker with body weight and autonomous locomotion support and an exoskeleton for joint motion support. Likewise, CPWalker enables strategies to improve postural control during walking. The integrated robotic platform provides means for testing novel gait rehabilitation therapies in subjects with CP and similar motor disorders. Patient-tailored therapies were programmed in the device for its evaluation in three children with spastic diplegia for 5weeks. After ten sessions of personalized training with CPWalker, the children improved the mean velocity (51.9441.97%), cadence (29.1933.36%) and step length (26.4919.58%) in each leg. Post-3D gait assessments provided kinematic outcomes closer to normal values than Pre-3D assessments.The results show the potential of the novel robotic platform to serve as a rehabilitation tool. The autonomous locomotion and impedance control enhanced the childrens participation during therapies. Moreover, participants postural control was substantially improved, which indicates the usefulness of the approach based on promoting the patients trunk control while the locomotion therapy is executed. Although results are promising, further studies with bigger sample size are required.


Bayon C.,Neural and Cognitive Engineering Group | Ramirez O.,Neural and Cognitive Engineering Group | Velasco M.,Neural and Cognitive Engineering Group | Serrano J.I.,Neural and Cognitive Engineering Group | And 3 more authors.
Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics | Year: 2016

Several robotic platforms were recently developed aimed at improving the locomotion capacity of people with gait impairment. Most of these gait trainers are limited to treadmill training, which is not a motivating condition for children with cerebral palsy (CP). This paper presents a pilot study done with two children with spastic CP, who trained with a new robotic platform called CPWalker during five weeks. This experimental device is a novel over ground prototype for gait rehabilitation with body weight support for children with CP. After rehabilitation training, both patients improved the mean velocity, cadence and step length. Moreover, the comparison between pre and post-kinematics analysis without the robot shows specific developments for each subject depending on the focus of the therapy (mainly trunk or hip flexion-extension). © 2016 IEEE.

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