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Experiences in Neurorobotics for intelligent assistive robots
2019
Zenodo
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Thus, from our perspectives, it is of fundamental importance to equip assistive robots with both an interface towards the human body and an interface towards the environment (i.e., cameras, touch sensors ...
NEUROPHYSIOLOGICAL HUMAN-MACHINE INTERFACES
A. ...
doi:10.5281/zenodo.4782604
fatcat:tqgs664tojhevkoc5w5em7pyf4
Editorial: Biosignal processing and computational methods to enhance sensory motor neuroprosthetics
2015
Frontiers in Neuroscience
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The detection of movement-related components is useful in brain-machine interfaces. A common approach is to classify the brain activity into a number of templates or states. ...
interfaces, such as a brain-computer interface (BCI), as an interaction modality between the patient and the neuroprostheses. ...
doi:10.3389/fnins.2015.00434
pmid:26594147
pmcid:PMC4633489
fatcat:ncx4ukdnsjbqnkjyaohcf2vjru
Proportional Myoelectric Control of Robots: Muscle Synergy Development Drives Performance Enhancement, Retainment, and Generalization
2015
IEEE Transactions on robotics
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Proportional myoelectric control has been proposed for user-friendly interaction with prostheses, orthoses, and new human-machine interfaces. ...
This work supports a shift in myoelectric control schemes towards proportional simultaneous controls learned through development of unique muscle synergies. ...
Myoelectric Finite State Machines As an alternative to pattern recognition-based control, a few applications have implemented finite state machines (FSM) [34] , in which a sequence of input signals maps ...
doi:10.1109/tro.2015.2395731
fatcat:capsa2ijcrfqvouotat4ar3ymi
Neuroengineering challenges of fusing robotics and neuroscience
2020
Science Robotics
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BRAIN-CONTROLLING MACHINES Brain-machine interfaces (BMIs) can achieve direct brain control of robots, enabling the restoration of motor function and the ability to probe the neural circuits of the brain ...
Key challenges toward future brain-robot synergy include the elaboration of neural decoders, soft-and hybrid-structured robotics, advanced feedback to the brain, and more widespread translation of neuroscience ...
doi:10.1126/scirobotics.abd1911
pmid:33298517
fatcat:zk5wwcy5zrbkljeppf3vpgfud4
Utilizing movement synergies to improve decoding performance for a brain machine interface
2013
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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A major challenge facing the development of high degree of freedom (DOF) brain machine interface (BMI) devices is a limited ability to provide prospective users with independent control of many DOFs when ...
It has been previously shown that a large range of complex hand postures can be replicated using a relatively low number of movement synergies. ...
Introduction Brain machine interfaces (BMIs) aim to allow users to control devices such as a cursor on screen or a prosthetic arm and hand via neural signals. ...
doi:10.1109/embc.2013.6609494
pmid:24109681
pmcid:PMC4180097
fatcat:4lexls4chvgyloocjozzkxv36q
Role of Muscle Synergies in Real-Time Classification of Upper Limb Motions using Extreme Learning Machines
2016
Journal of NeuroEngineering and Rehabilitation
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Extreme learning machine (ELM) is a relatively new neural-network based learning algorithm: its performance hasn't been explored in the context of online control, which is a more reliable measure compared ...
To this purpose we aim at focusing our investigation on a myoelectric-based interface which is able to identify and online classify, upper limb motions involving shoulder and elbow. ...
Their main use ranges from interfaces in human-machine interaction based applications like prosthesis [1] [2] [3] , orthosis [4] [5] [6] and telemanipulation [7] [8] [9] [10] , to functional electrical ...
doi:10.1186/s12984-016-0183-0
pmid:27527511
pmcid:PMC4986359
fatcat:ta2zq3n2m5fotasp4lpjid3cke
Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands
2016
Physics of Life Reviews
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behavior and synthesizing artificial robotic systems based on a theory of synergies. ...
Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers ...
, such as more effective methods to acquire, localize, and decode signals for brain-machine interfaces and prosthetic applications. ...
doi:10.1016/j.plrev.2016.02.001
pmid:26923030
pmcid:PMC5839666
fatcat:uu6cf7mc6ngvfpivx3qlk2iyda
Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography
2014
Frontiers in Neurorobotics
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The brain adjusts the neural commands based on the interface output (z) by learning the inverse model of the decoder. ...
interfaces based upon, e.g., electroencephalography. ...
Interfaces: going beyond traditional surface electromyography. ...
doi:10.3389/fnbot.2014.00022
pmid:25177292
pmcid:PMC4133701
fatcat:qnp4coftovgsvco4ksycp7sxvq
A hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements
2017
Journal of Neural Engineering
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of myoelectric interfaces. ...
Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling ...
This method is common in brain-machine interfaces [256] [257] [258] , which utilize brain plasticity to encourage users to associate thoughts with controls [259] [260] [261] . ...
doi:10.1088/1741-2552/aa525f
pmid:28068293
fatcat:p6wtketyefgszlrnfahlkjxoaa
Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges
2021
Sensors
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New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human–machine interfaces. ...
Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. ...
Brain-Computer Interfaces BCIs refer to computer-based systems that interface with the brain to acquire, decode, and translate the brain activity of the user into control commands for various devices ...
doi:10.3390/s21062084
pmid:33809721
pmcid:PMC8002299
fatcat:jv3tjm3w2nh2xhhlymbk7lmm5i
Page 939 of Psychological Abstracts Vol. 81, Issue 2
[page]
1994
Psychological Abstracts
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—Plan recognition (PR) is a step toward making user interfaces more intelligent and cooperative. ...
This paper shows that there is an important synergy between theoretical work in PR and applications of PR to intelligent interfaces. ...
Achieving Neural Compatibility with Human Sensorimotor Control in Prosthetic and Therapeutic Devices
2019
IEEE Transactions on Medical Robotics and Bionics
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will present a range of neural technologies that may allow implementation of biomimetic sensorimotor control, including natural sensory feedback, neuromuscular like compliant control, natural module of synergy-based ...
Based on the evidence in our research and in literature, we propose that achieving neural compatibility with the existing human sensorimotor system should be the ultimate goal of prosthetic and therapeutic ...
Toward this end, muscle synergy provides a useful principle for neuromuscular electrical stimulation as a human-compatible strategy. ...
doi:10.1109/tmrb.2019.2930356
fatcat:srs6eddhqjaydnpro6tsmndrrq
Analyzing biological and artificial neural networks: challenges with opportunities for synergy?
2019
Current Opinion in Neurobiology
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We explore opportunities for synergy between the two fields, such as the use of DNNs as in silico model systems for neuroscience, and how this synergy can lead to new hypotheses about the operating principles ...
Thus, both machine learning and computational neuroscience are faced with a shared challenge: how can we analyze their representations in order to understand how they solve complex tasks? ...
To investigate this question, [57] used multi-electrode recordings to control a brain-machine interface. ...
doi:10.1016/j.conb.2019.01.007
pmid:30785004
fatcat:fa3pbeknffgkxnr5pw2lupolwq
Analyzing biological and artificial neural networks: challenges with opportunities for synergy?
[article]
2018
arXiv
pre-print
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We explore opportunities for synergy between the two fields, such as the use of DNNs as in-silico model systems for neuroscience, and how this synergy can lead to new hypotheses about the operating principles ...
Thus, both machine learning and computational neuroscience are faced with a shared challenge: how can we analyze their representations in order to understand how they solve complex tasks? ...
To investigate this question, Sadtler et al. (2014) used multielectrode recordings to control a brain-machine interface. ...
arXiv:1810.13373v1
fatcat:xkh7z3ef5nc3lpb2cgwas5szry
Guidance-based quantification of arm impairment following brain injury: a pilot study
1999
IEEE transactions on rehabilitation engineering
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During guided movement, the brain-injured subjects generated distinct spatial patterns of constraint force with their impaired arms that were consistent with the standard flexion and extension "synergies ...
We tested the device on four hemiplegic brain-injured individuals and four unimpaired control subjects. ...
By "appropriately designed" machines we mean machines targeted at the distinctive motor impairments that arise after brain injury. ...
doi:10.1109/86.750543
fatcat:sq6k6ngycvcdnj5pnrmhlg73qm
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