about
Effect of tDCS stimulation of motor cortex and cerebellum on EEG classification of motor imagery and sensorimotor band power.Evaluating classifiers to detect arm movement intention from EEG signalsUsing eye movement to control a computer: a design for a lightweight electro-oculogram electrode array and computer interfaceAssessing movement factors in upper limb kinematics decoding from EEG signals.Decoding the Attentional Demands of Gait through EEG Gamma Band Features.Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions.Analyzing EEG signals to detect unexpected obstacles during walking.Characterization of Artifacts Produced by Gel Displacement on Non-invasive Brain-Machine Interfaces during Ambulation.Low Intensity Focused tDCS Over the Motor Cortex Shows Inefficacy to Improve Motor Imagery Performance.Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent.EEG-Based Detection of Starting and Stopping During Gait Cycle.Control of a 2 DoF robot using a brain-machine interface.Mental tasks classification for BCI using image correlation.Classification of upper limb center-out reaching tasks by means of EEG-based continuous decoding techniques.Application of the Stockwell Transform to Electroencephalographic Signal Analysis during Gait Cycle.Endogenous brain-machine interface based on the correlation of EEG maps.Detection of intention of pedaling start cycle through EEG signals.Brain-machine interfaces for controlling lower-limb powered robotic systems.Pneumatic robotic device for early delivering of rehabilitation therapy.Effect on the classification of motor imagery in EEG after applying anodal tDCS with a 4×1 ring montage over the motor cortex.Estimation of Neuromuscular Primitives from EEG Slow Cortical Potentials in Incomplete Spinal Cord Injury Individuals for a New Class of Brain-Machine Interfaces.Electromechanical delay in the tibialis anterior muscle during time-varying ankle dorsiflexion.Effects of tDCS on Real-Time BCI Detection of Pedaling Motor Imagery.Shared control architecture based on RFID to control a robot arm using a spontaneous brain–machine interfaceInterface based on electrooculography for velocity control of a robot armImproving Real-Time Lower Limb Motor Imagery Detection Using tDCS and an ExoskeletonCentral nervous system modulates the neuromechanical delay in a broad range for the control of muscle forceStudying Cognitive Attention Mechanisms during Walking from EEG SignalsCombining a Brain–Machine Interface and an Electrooculography Interface to perform pick and place tasks with a robotic armBrain-Machine Interfaces for Assistive RoboticsOnline detection of horizontal hand movements from low frequency EEG componentsStarting and finishing gait detection using a BMI for spinal cord injury rehabilitationUsing EEG Signals to Detect the Intention of Walking Initiation and StopBrain-Machine Interface system to differentiate between five mental tasksDecoding knee angles from EEG signals for different walking speedsFirst steps in the development of an EEG-based system to detect intention of gait initiationSelection of the best mental tasks for a SVM-based BCI systemEmpirical Analysis of the Integration of a BCI and an EOG Interface to Control a Robot ArmOnline classification of two mental tasks using a SVM-based BCI systemPassive robot assistance in arm movement decoding from EEG signals
P50
Q33576960-DECF4AC5-FD72-449F-926C-D1FA8CD7447FQ34554232-A2DE7230-6813-4143-8B85-5F54586468C0Q34812426-0B136906-C971-4A9A-8F30-F48844B10C75Q35644671-EF843FA1-017E-4E6A-8287-31C443CCFF49Q35999490-3EB24446-F675-4575-91F7-3AF2A437D530Q36173240-908E27D7-511C-4B65-A183-D681C08C2283Q36292436-58DE4BC1-3FCD-491F-9536-B1212ABCE6F6Q38621105-2628C16C-EE4B-4517-A3BA-2F1710D41AAFQ38654044-281A65DB-B973-4A9D-A222-C9B92AA54977Q38661333-AF0B724C-C351-480E-93B3-A4FB0CFBFB8DQ38847433-A7132E86-4789-4C4E-B8D9-5DE10BB59677Q39217298-C0B98651-7BF4-48B0-AD63-5A369D5F6DDCQ39662885-9312F597-25A6-4AE9-B348-0C2A6FF03CF7Q42325043-708A01A9-79B4-4ECB-9640-6B0BF7BF5065Q47165268-ABFE00AE-40BA-4E66-AE9E-D67564693CD9Q47251257-7C41042C-2708-4FD9-B224-A2DB4C938EE1Q47359361-0F07A1A2-327F-47CD-BE43-FD9EC0E7AFAFQ47854232-719DD9D4-92D8-481B-9D68-87E553B00660Q48015356-43F1C0B0-FE88-4AB5-ADEB-23A9E74CB2E7Q48152132-F457980F-A804-403A-B87B-B9AB600167A7Q49443635-8FACC554-C41C-42FA-B3E8-161C2A21A7AEQ50502333-1CC629A5-A9E6-4987-8F9F-3EC5F80809E1Q52593650-330478B4-EC64-4432-B47D-1F858BF5FB66Q56424352-24F62EAF-1BBB-4A54-A58F-B808AB106222Q56424967-E24BB269-1E2B-4FF0-A401-139A4DA62D75Q58573243-8555BC9B-59AD-4ADB-B869-59C1A886E0C1Q61947421-F9394B0D-861A-4879-BFF3-43B09C7631A4Q61947461-92E45F1E-BA54-4A69-8BCD-3DF016459853Q61947464-A83BB4AC-6967-4994-9C98-166E3BDCAE64Q61947469-B63EE4D7-2735-4814-9F39-0B5C73D4EF19Q61947475-ED215B13-98DA-4290-9597-FC5538494B14Q61947477-DA66F58B-1DCD-417F-9842-ACECD2CF29DCQ61947480-84850464-CC67-4ABE-8DE4-BAD4BEBC40B8Q61947483-FEFB52F2-ED55-4EE1-B456-85BB174FC85EQ61947485-5F507AD4-2F4E-4F98-B090-767BFEED7E1DQ61947490-DC7D97E6-486F-432E-9770-B4027C0B33F8Q61947496-2D7D85F3-D180-4951-A0AE-661B6F2B899FQ61947505-9B0F3900-E31E-457E-8C8A-5C47E0265626Q61947518-92FED5D3-7EF5-488E-94C5-147B6DD72921Q61947520-EA8E792E-06B9-4C5A-92BF-146BAEE27EE5
P50
description
Spaans onderzoeker
@nl
Spanish researcher
@en
investigador español
@ast
investigador español
@es
taighdeoir Spáinneach
@ga
name
José M Azorín
@an
José M Azorín
@ast
José M Azorín
@ca
José M Azorín
@da
José M Azorín
@de
José M Azorín
@en
José M Azorín
@es
José M Azorín
@eu
José M Azorín
@fr
José M Azorín
@ga
type
label
José M Azorín
@an
José M Azorín
@ast
José M Azorín
@ca
José M Azorín
@da
José M Azorín
@de
José M Azorín
@en
José M Azorín
@es
José M Azorín
@eu
José M Azorín
@fr
José M Azorín
@ga
prefLabel
José M Azorín
@an
José M Azorín
@ast
José M Azorín
@ca
José M Azorín
@da
José M Azorín
@de
José M Azorín
@en
José M Azorín
@es
José M Azorín
@eu
José M Azorín
@fr
José M Azorín
@ga
P1053
G-2157-2011
P106
P21
P27
P31
P3829
P496
0000-0001-5548-9657