E
Enrique Hortal
Researcher at Maastricht University
Publications - 52
Citations - 619
Enrique Hortal is an academic researcher from Maastricht University. The author has contributed to research in topics: Robotic arm & Gait (human). The author has an hindex of 11, co-authored 50 publications receiving 492 citations. Previous affiliations of Enrique Hortal include Universidad Miguel Hernández de Elche.
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Journal ArticleDOI
SVM-based Brain–Machine Interface for controlling a robot arm through four mental tasks
Enrique Hortal,Daniel Planelles,Álvaro Costa,Eduardo Iáñez,Andrés Úbeda,José M. Azorín,Eduardo Fernández +6 more
TL;DR: A non-invasive spontaneous Brain–Machine Interface has been designed to control a robot arm through the mental activity of the users through the classification of four mental tasks in order to manage the movements of the robot.
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Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions.
TL;DR: The accuracy of the results shows that the combined use of a hybrid upper limb exoskeleton and a BMI could be used for rehabilitation therapies and that the user is an active part of the rehabilitation procedure.
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Evaluating classifiers to detect arm movement intention from EEG signals.
TL;DR: A methodology to detect the intention to make a reaching movement with the arm in healthy subjects before the movement actually starts by measuring brain activity through electroencephalographic signals that are registered by electrodes placed over the scalp is presented.
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Decoding the Attentional Demands of Gait through EEG Gamma Band Features
Álvaro Costa,Eduardo Iáñez,Andrés Úbeda,Enrique Hortal,Antonio J. del-Ama,Ángel Gil-Agudo,José M. Azorín +6 more
TL;DR: Evidence of the existence of classifiable cortical information related to the attention level on the gait is provided to allow the development of a real-time system that obtains the attentionlevel during lower limb rehabilitation.
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EEG-Based Detection of Starting and Stopping During Gait Cycle.
TL;DR: A system to detect the start and the stop of the gait through electroencephalographic signals has been developed and has been designed in order to be applied in the future to control a lower limb exoskeleton to help stroke or spinal cord injured patients duringThe gait.