Brain-machine interfaces in neurorehabilitation of stroke.
about
Rehabilitation of Motor Function after Stroke: A Multiple Systematic Review Focused on Techniques to Stimulate Upper Extremity RecoveryThe Berlin Brain-Computer Interface: Progress Beyond Communication and ControlEnhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation.Remapping residual coordination for controlling assistive devices and recovering motor functions.Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors.Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial.Physiological properties of brain-machine interface input signals.A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke.Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation.Role of the Contralesional vs. Ipsilesional Hemisphere in Stroke Recovery.Embodied neurofeedback with an anthropomorphic robotic handImproving Motor Corticothalamic Communication After Stroke Using Real-Time fMRI Connectivity-Based Neurofeedback.Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future.Sensory Feedback Interferes with Mu Rhythm Based Detection of Motor Commands from Electroencephalographic Signals.Emergent Coordination Underlying Learning to Reach-to-Grasp with a Brain-Machine Interface.Continuous decoding of human grasp kinematics using epidural and subdural signals.Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity.Motor Imagery-Based Brain-Computer Interface Coupled to a Robotic Hand Orthosis Aimed for Neurorehabilitation of Stroke Patients.Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients.Single-session tDCS over the dominant hemisphere affects contralateral spectral EEG power, but does not enhance neurofeedback-guided event-related desynchronization of the non-dominant hemisphere's sensorimotor rhythm.Across-subject offline decoding of motor imagery from MEG and EEG.Feasibility and safety of shared EEG/EOG and vision-guided autonomous whole-arm exoskeleton control to perform activities of daily livingEarly Findings on Functional Connectivity Correlates of Behavioral Outcomes of Brain-Computer Interface Stroke Rehabilitation Using Machine Learning
P2860
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P2860
Brain-machine interfaces in neurorehabilitation of stroke.
description
2014 nî lūn-bûn
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2014年の論文
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name
Brain-machine interfaces in neurorehabilitation of stroke.
@en
Brain-machine interfaces in neurorehabilitation of stroke.
@nl
type
label
Brain-machine interfaces in neurorehabilitation of stroke.
@en
Brain-machine interfaces in neurorehabilitation of stroke.
@nl
prefLabel
Brain-machine interfaces in neurorehabilitation of stroke.
@en
Brain-machine interfaces in neurorehabilitation of stroke.
@nl
P2093
P1476
Brain-machine interfaces in neurorehabilitation of stroke.
@en
P2093
Leonardo G Cohen
Marc W Slutzky
Surjo R Soekadar
P304
P356
10.1016/J.NBD.2014.11.025
P577
2014-12-07T00:00:00Z