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Borzelli D, Vieira TMM, Botter A, Gazzoni M, Lacquaniti F, d'Avella A. Synaptic inputs to motor neurons underlying muscle coactivation for functionally different tasks have different spectral characteristics. J Neurophysiol 2024; 131:1126-1142. [PMID: 38629162 DOI: 10.1152/jn.00199.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 04/11/2024] [Accepted: 04/11/2024] [Indexed: 06/01/2024] Open
Abstract
The central nervous system (CNS) may produce the same endpoint trajectory or torque profile with different muscle activation patterns. What differentiates these patterns is the presence of cocontraction, which does not contribute to effective torque generation but allows to modulate joints' mechanical stiffness. Although it has been suggested that the generation of force and the modulation of stiffness rely on separate pathways, a characterization of the differences between the synaptic inputs to motor neurons (MNs) underlying these tasks is still missing. In this study, participants coactivated the same pair of upper-limb muscles, i.e., the biceps brachii and the triceps brachii, to perform two functionally different tasks: limb stiffness modulation or endpoint force generation. Spike trains of MNs were identified through decomposition of high-density electromyograms (EMGs) collected from the two muscles. Cross-correlogram showed a higher synchronization between MNs recruited to modulate stiffness, whereas cross-muscle coherence analysis revealed peaks in the β-band, which is commonly ascribed to a cortical origin. These peaks did not appear during the coactivation for force generation, thus suggesting separate cortical inputs for stiffness modulation. Moreover, a within-muscle coherence analysis identified two subsets of MNs that were selectively recruited to generate force or regulate stiffness. This study is the first to highlight different characteristics, and probable different neural origins, of the synaptic inputs driving a pair of muscles under different functional conditions. We suggest that stiffness modulation is driven by cortical inputs that project to a separate set of MNs, supporting the existence of a separate pathway underlying the control of stiffness.NEW & NOTEWORTHY The characterization of the pathways underlying force generation or stiffness modulation are still unknown. In this study, we demonstrated that the common input to motor neurons of antagonist muscles shows a high-frequency component when muscles are coactivated to modulate stiffness but not to generate force. Our results provide novel insights on the neural strategies for the recruitment of multiple muscles by identifying specific spectral characteristics of the synaptic inputs underlying functionally different tasks.
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Affiliation(s)
- Daniele Borzelli
- Department of Biomedical, Dental, Morphological and Functional Imaging Sciences, University of Messina, Messina, Italy
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Taian M M Vieira
- Laboratory for Engineering of the Neuromuscular System, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
- PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy
| | - Alberto Botter
- Laboratory for Engineering of the Neuromuscular System, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
- PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy
| | - Marco Gazzoni
- Laboratory for Engineering of the Neuromuscular System, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
- PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy
| | - Francesco Lacquaniti
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Systems Medicine and Center of Space BioMedicine, University of Rome Tor Vergata, Rome, Italy
| | - Andrea d'Avella
- Department of Biomedical, Dental, Morphological and Functional Imaging Sciences, University of Messina, Messina, Italy
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy
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Dominijanni G, Pinheiro DL, Pollina L, Orset B, Gini M, Anselmino E, Pierella C, Olivier J, Shokur S, Micera S. Human motor augmentation with an extra robotic arm without functional interference. Sci Robot 2023; 8:eadh1438. [PMID: 38091424 DOI: 10.1126/scirobotics.adh1438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 11/15/2023] [Indexed: 12/18/2023]
Abstract
Extra robotic arms (XRAs) are gaining interest in neuroscience and robotics, offering potential tools for daily activities. However, this compelling opportunity poses new challenges for sensorimotor control strategies and human-machine interfaces (HMIs). A key unsolved challenge is allowing users to proficiently control XRAs without hindering their existing functions. To address this, we propose a pipeline to identify suitable HMIs given a defined task to accomplish with the XRA. Following such a scheme, we assessed a multimodal motor HMI based on gaze detection and diaphragmatic respiration in a purposely designed modular neurorobotic platform integrating virtual reality and a bilateral upper limb exoskeleton. Our results show that the proposed HMI does not interfere with speaking or visual exploration and that it can be used to control an extra virtual arm independently from the biological ones or in coordination with them. Participants showed significant improvements in performance with daily training and retention of learning, with no further improvements when artificial haptic feedback was provided. As a final proof of concept, naïve and experienced participants used a simplified version of the HMI to control a wearable XRA. Our analysis indicates how the presented HMI can be effectively used to control XRAs. The observation that experienced users achieved a success rate 22.2% higher than that of naïve users, combined with the result that naïve users showed average success rates of 74% when they first engaged with the system, endorses the viability of both the virtual reality-based testing and training and the proposed pipeline.
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Affiliation(s)
- Giulia Dominijanni
- Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Daniel Leal Pinheiro
- Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Neuroengineering and Neurocognition Laboratory, Escola Paulista de Medicina, Department of Neurology and Neurosurgery, Division of Neuroscience, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Leonardo Pollina
- Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Bastien Orset
- Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Martina Gini
- BioRobotics Institute, Health Interdisciplinary Center, and Department of Excellence in AI and Robotics, Scuola Superiore Sant'Anna, Pisa, Italy
- Neuroelectronic Interfaces, Faculty of Electrical Engineering and IT, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, Aachen 52074, Germany
| | - Eugenio Anselmino
- BioRobotics Institute, Health Interdisciplinary Center, and Department of Excellence in AI and Robotics, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Camilla Pierella
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, and Maternal and Children's Sciences (DINOGMI), University of Genoa, Genoa, Italy
| | - Jérémy Olivier
- Institute for Industrial Sciences and Technologies, Haute Ecole du Paysage, d'Ingénierie et d'Architecture (HEPIA), HES-SO University of Applied Sciences and Arts Western Switzerland, Geneva, Switzerland
| | - Solaiman Shokur
- Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- BioRobotics Institute, Health Interdisciplinary Center, and Department of Excellence in AI and Robotics, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Silvestro Micera
- Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- BioRobotics Institute, Health Interdisciplinary Center, and Department of Excellence in AI and Robotics, Scuola Superiore Sant'Anna, Pisa, Italy
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Maselli A, Gordon J, Eluchans M, Lancia GL, Thiery T, Moretti R, Cisek P, Pezzulo G. Beyond simple laboratory studies: Developing sophisticated models to study rich behavior. Phys Life Rev 2023; 46:220-244. [PMID: 37499620 DOI: 10.1016/j.plrev.2023.07.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 07/29/2023]
Abstract
Psychology and neuroscience are concerned with the study of behavior, of internal cognitive processes, and their neural foundations. However, most laboratory studies use constrained experimental settings that greatly limit the range of behaviors that can be expressed. While focusing on restricted settings ensures methodological control, it risks impoverishing the object of study: by restricting behavior, we might miss key aspects of cognitive and neural functions. In this article, we argue that psychology and neuroscience should increasingly adopt innovative experimental designs, measurement methods, analysis techniques and sophisticated computational models to probe rich, ecologically valid forms of behavior, including social behavior. We discuss the challenges of studying rich forms of behavior as well as the novel opportunities offered by state-of-the-art methodologies and new sensing technologies, and we highlight the importance of developing sophisticated formal models. We exemplify our arguments by reviewing some recent streams of research in psychology, neuroscience and other fields (e.g., sports analytics, ethology and robotics) that have addressed rich forms of behavior in a model-based manner. We hope that these "success cases" will encourage psychologists and neuroscientists to extend their toolbox of techniques with sophisticated behavioral models - and to use them to study rich forms of behavior as well as the cognitive and neural processes that they engage.
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Affiliation(s)
- Antonella Maselli
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Jeremy Gordon
- University of California, Berkeley, Berkeley, CA, 94704, United States
| | - Mattia Eluchans
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy; University of Rome "La Sapienza", Rome, Italy
| | - Gian Luca Lancia
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy; University of Rome "La Sapienza", Rome, Italy
| | - Thomas Thiery
- Department of Psychology, University of Montréal, Montréal, Québec, Canada
| | - Riccardo Moretti
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy; University of Rome "La Sapienza", Rome, Italy
| | - Paul Cisek
- Department of Neuroscience, University of Montréal, Montréal, Québec, Canada
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
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Berger DJ, d'Avella A. Exposure to an incompatible virtual surgery impacts the null space components of the muscle patterns after re-adaptation but not the task performance. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082698 DOI: 10.1109/embc40787.2023.10340277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Under the synergy hypothesis, novel muscle synergies may be required for motor skill learning. We have developed a "virtual surgery" experimental paradigm that alters the mapping of muscle activations onto virtual cursor motion during an isometric reaching task using myoelectric control. By creating virtual surgeries that are "incompatible" with the original synergies, we can investigate learning new muscle synergies in controlled experimental conditions. We have previously shown that participants are able to improve their task performance after an incompatible virtual surgery, using novel muscle patterns to overcome the perturbation. In this work, we investigated whether the activation of novel muscle patterns, that are required after an incompatible virtual surgery, affects task performance or the muscle patterns after re-adaptation to the unperturbed baseline mapping. We found that experiencing an incompatible virtual surgery did not affect the task performance during the baseline mapping. However, the adaptation to the incompatible virtual surgery resulted in changes in the null space components of the muscle patterns used in the unperturbed task.
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Zhao K, Zhang Z, Wen H, Liu B, Li J, Andrea d’Avella, Scano A. Muscle synergies for evaluating upper limb in clinical applications: A systematic review. Heliyon 2023; 9:e16202. [PMID: 37215841 PMCID: PMC10199229 DOI: 10.1016/j.heliyon.2023.e16202] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 04/11/2023] [Accepted: 05/09/2023] [Indexed: 09/28/2023] Open
Abstract
INTRODUCTION Muscle synergies have been proposed as a strategy employed by the central nervous system to control movements. Muscle synergy analysis is a well-established framework to examine the pathophysiological basis of neurological diseases and has been applied for analysis and assessment in clinical applications in the last decades, even if it has not yet been widely used in clinical diagnosis, rehabilitative treatment and interventions. Even if inconsistencies in the outputs among studies and lack of a normative pipeline including signal processing and synergy analysis limit the progress, common findings and results are identifiable as a basis for future research. Therefore, a literature review that summarizes methods and main findings of previous works on upper limb muscle synergies in clinical environment is needed to i) summarize the main findings so far, ii) highlight the barriers limiting their use in clinical applications, and iii) suggest future research directions needed for facilitating translation of experimental research to clinical scenarios. METHODS Articles in which muscle synergies were used to analyze and assess upper limb function in neurological impairments were reviewed. The literature research was conducted in Scopus, PubMed, and Web of Science. Experimental protocols (e.g., the aim of the study, number and type of participants, number and type of muscles, and tasks), methods (e.g., muscle synergy models and synergy extraction methods, signal processing methods), and the main findings of eligible studies were reported and discussed. RESULTS 383 articles were screened and 51 were selected, which involved a total of 13 diseases and 748 patients and 1155 participants. Each study investigated on average 15 ± 10 patients. Four to forty-one muscles were included in the muscle synergy analysis. Point-to-point reaching was the most used task. The preprocessing of EMG signals and algorithms for synergy extraction varied among studies, and non-negative matrix factorization was the most used method. Five EMG normalization methods and five methods for identifying the optimal number of synergies were used in the selected papers. Most of the studies report that analyses on synergy number, structure, and activations provide novel insights on the physiopathology of motor control that cannot be gained with standard clinical assessments, and suggest that muscle synergies may be useful to personalize therapies and to develop new therapeutic strategies. However, in the selected studies synergies were used only for assessment; different testing procedures were used and, in general, study-specific modifications of muscle synergies were observed; single session or longitudinal studies mainly aimed at assessing stroke (71% of the studies), even though other pathologies were also investigated. Synergy modifications were either study-specific or were not observed, with few analyses available for temporal coefficients. Thus, several barriers prevent wider adoption of muscle synergy analysis including a lack of standardized experimental protocols, signal processing procedures, and synergy extraction methods. A compromise in the design of the studies must be found to combine the systematicity of motor control studies and the feasibility of clinical studies. There are however several potential developments that might promote the use of muscle synergy analysis in clinical practice, including refined assessments based on synergistic approaches not allowed by other methods and the availability of novel models. Finally, neural substrates of muscle synergies are discussed, and possible future research directions are proposed. CONCLUSIONS This review provides new perspectives about the challenges and open issues that need to be addressed in future work to achieve a better understanding of motor impairments and rehabilitative therapy using muscle synergies. These include the application of the methods on wider scales, standardization of procedures, inclusion of synergies in the clinical decisional process, assessment of temporal coefficients and temporal-based models, extensive work on the algorithms and understanding of the physio-pathological mechanisms of pathology, as well as the application and adaptation of synergy-based approaches to various rehabilitative scenarios for increasing the available evidence.
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Affiliation(s)
- Kunkun Zhao
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Zhisheng Zhang
- School of Mechanical Engineering, Southeast University, Nanjing, China
| | - Haiying Wen
- School of Mechanical Engineering, Southeast University, Nanjing, China
| | - Bin Liu
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Jianqing Li
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Andrea d’Avella
- Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Rome, Italy
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Italy
| | - Alessandro Scano
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), Milan, Italy
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Use of Surface Electromyography to Estimate End-Point Force in Redundant Systems: Comparison between Linear Approaches. Bioengineering (Basel) 2023; 10:bioengineering10020234. [PMID: 36829728 PMCID: PMC9952324 DOI: 10.3390/bioengineering10020234] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
Estimation of the force exerted by muscles from their electromyographic (EMG) activity may be useful to control robotic devices. Approximating end-point forces as a linear combination of the activities of multiple muscles acting on a limb may lead to an inaccurate estimation because of the dependency between the EMG signals, i.e., multi-collinearity. This study compared the EMG-to-force mapping estimation performed with standard multiple linear regression and with three other algorithms designed to reduce different sources of the detrimental effects of multi-collinearity: Ridge Regression, which performs an L2 regularization through a penalty term; linear regression with constraints from foreknown anatomical boundaries, derived from a musculoskeletal model; linear regression of a reduced number of muscular degrees of freedom through the identification of muscle synergies. Two datasets, both collected during the exertion of submaximal isometric forces along multiple directions with the upper limb, were exploited. One included data collected across five sessions and the other during the simultaneous exertion of force and generation of different levels of co-contraction. The accuracy and consistency of the EMG-to-force mappings were assessed to determine the strengths and drawbacks of each algorithm. When applied to multiple sessions, Ridge Regression achieved higher accuracy (R2 = 0.70) but estimations based on muscle synergies were more consistent (differences between the pulling vectors of mappings extracted from different sessions: 67%). In contrast, the implementation of anatomical constraints was the best solution, both in terms of consistency (R2 = 0.64) and accuracy (74%), in the case of different co-contraction conditions. These results may be used for the selection of the mapping between EMG and force to be implemented in myoelectrically controlled robotic devices.
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Borzelli D, Pastorelli S, d’Avella A, Gastaldi L. Virtual Stiffness: A Novel Biomechanical Approach to Estimate Limb Stiffness of a Multi-Muscle and Multi-Joint System. SENSORS (BASEL, SWITZERLAND) 2023; 23:673. [PMID: 36679467 PMCID: PMC9861781 DOI: 10.3390/s23020673] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/30/2022] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
In recent years, different groups have developed algorithms to control the stiffness of a robotic device through the electromyographic activity collected from a human operator. However, the approaches proposed so far require an initial calibration, have a complex subject-specific muscle model, or consider the activity of only a few pairs of antagonist muscles. This study described and tested an approach based on a biomechanical model to estimate the limb stiffness of a multi-joint, multi-muscle system from muscle activations. The "virtual stiffness" method approximates the generated stiffness as the stiffness due to the component of the muscle-activation vector that does not generate any endpoint force. Such a component is calculated by projecting the vector of muscle activations, estimated from the electromyographic signals, onto the null space of the linear mapping of muscle activations onto the endpoint force. The proposed method was tested by using an upper-limb model made of two joints and six Hill-type muscles and data collected during an isometric force-generation task performed with the upper limb. The null-space projection of the muscle-activation vector approximated the major axis of the stiffness ellipse or ellipsoid. The model provides a good approximation of the voluntary stiffening performed by participants that could be directly implemented in wearable myoelectric controlled devices that estimate, in real-time, the endpoint forces, or endpoint movement, from the mapping between muscle activation and force, without any additional calibrations.
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Affiliation(s)
- Daniele Borzelli
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, 98122 Messina, Italy
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
| | - Stefano Pastorelli
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy
| | - Andrea d’Avella
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, 98122 Messina, Italy
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
| | - Laura Gastaldi
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy
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Eden J, Bräcklein M, Ibáñez J, Barsakcioglu DY, Di Pino G, Farina D, Burdet E, Mehring C. Principles of human movement augmentation and the challenges in making it a reality. Nat Commun 2022; 13:1345. [PMID: 35292665 PMCID: PMC8924218 DOI: 10.1038/s41467-022-28725-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 02/04/2022] [Indexed: 12/23/2022] Open
Abstract
Augmenting the body with artificial limbs controlled concurrently to one's natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this possible. By allowing individuals to achieve otherwise impossible actions, movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with the environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning as well as application scenarios, and propose a vision for the field.
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Affiliation(s)
- Jonathan Eden
- Department of Bioengineering, Imperial College of Science, Technology and Medicine, London, UK
| | - Mario Bräcklein
- Department of Bioengineering, Imperial College of Science, Technology and Medicine, London, UK
| | - Jaime Ibáñez
- Department of Bioengineering, Imperial College of Science, Technology and Medicine, London, UK.,BSICoS, IIS Aragón, Universidad de Zaragoza, Zaragoza, Spain.,Department of Clinical and Movement Neurosciences, Institute of Neurology, University College London, London, UK
| | | | - Giovanni Di Pino
- NEXT: Neurophysiology and Neuroengineering of Human-Technology Interaction Research Unit, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Dario Farina
- Department of Bioengineering, Imperial College of Science, Technology and Medicine, London, UK
| | - Etienne Burdet
- Department of Bioengineering, Imperial College of Science, Technology and Medicine, London, UK.
| | - Carsten Mehring
- Bernstein Center Freiburg, University of Freiburg, Freiburg im Breisgau, 79104, Germany.,Faculty of Biology, University of Freiburg, Freiburg im Breisgau, 79104, Germany
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